1
|
Zhang Z, Du Y, Shi X, Wang K, Qu Q, Liang Q, Ma X, He K, Chi C, Tang J, Liu B, Ji J, Wang J, Dong J, Hu Z, Tian J. NIR-II light in clinical oncology: opportunities and challenges. Nat Rev Clin Oncol 2024; 21:449-467. [PMID: 38693335 DOI: 10.1038/s41571-024-00892-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 05/03/2024]
Abstract
Novel strategies utilizing light in the second near-infrared region (NIR-II; 900-1,880 nm wavelengths) offer the potential to visualize and treat solid tumours with enhanced precision. Over the past few decades, numerous techniques leveraging NIR-II light have been developed with the aim of precisely eliminating tumours while maximally preserving organ function. During cancer surgery, NIR-II optical imaging enables the visualization of clinically occult lesions and surrounding vital structures with increased sensitivity and resolution, thereby enhancing surgical quality and improving patient prognosis. Furthermore, the use of NIR-II light promises to improve cancer phototherapy by enabling the selective delivery of increased therapeutic energy to tissues at greater depths. Initial clinical studies of NIR-II-based imaging and phototherapy have indicated impressive potential to decrease cancer recurrence, reduce complications and prolong survival. Despite the encouraging results achieved, clinical translation of innovative NIR-II techniques remains challenging and inefficient; multidisciplinary cooperation is necessary to bridge the gap between preclinical research and clinical practice, and thus accelerate the translation of technical advances into clinical benefits. In this Review, we summarize the available clinical data on NIR-II-based imaging and phototherapy, demonstrating the feasibility and utility of integrating these technologies into the treatment of cancer. We also introduce emerging NIR-II-based approaches with substantial potential to further enhance patient outcomes, while also highlighting the challenges associated with imminent clinical studies of these modalities.
Collapse
Affiliation(s)
- Zeyu Zhang
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China
| | - Yang Du
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaojing Shi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Qiaojun Qu
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Qian Liang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Ma
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Kunshan He
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Chongwei Chi
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China
| | - Jianqiang Tang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Liu
- Department of General Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiafu Ji
- Department of Gastrointestinal Surgery, Peking University Cancer Hospital and Institute, Beijing, China.
| | - Jun Wang
- Thoracic Oncology Institute/Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
| | - Jiahong Dong
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Zhenhua Hu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
| | - Jie Tian
- Key Laboratory of Big Data-Based Precision Medicine of Ministry of Industry and Information Technology, School of Engineering Medicine, Beihang University, Beijing, China.
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China.
| |
Collapse
|
2
|
Yuan N, Zhang Y, Lv K, Liu Y, Yang A, Hu P, Yu H, Han X, Guo X, Li J, Wang T, Lei B, Ma G. HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images. Cancer Imaging 2024; 24:63. [PMID: 38773670 PMCID: PMC11107051 DOI: 10.1186/s40644-024-00711-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/11/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. METHODS In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. RESULTS Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. CONCLUSIONS Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.
Collapse
Affiliation(s)
- Ning Yuan
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Yongtao Zhang
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Kuan Lv
- Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yiyao Liu
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Aocai Yang
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Pianpian Hu
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xing Guo
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Junfeng Li
- Department of Medical Imaging, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Tianfu Wang
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Baiying Lei
- School of Biomedical Engineering, Health Science Centers, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
- AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Guangdong, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, No. 2 East Yinghua Road, Chaoyang District, Beijing, 100029, China.
| |
Collapse
|
3
|
Shi S, Lin C, Zhou J, Wei L, Chen M, Zhang J, Cao K, Fan Y, Huang B, Luo Y, Feng ST. Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma. Int J Surg 2024; 110:2669-2678. [PMID: 38445459 DOI: 10.1097/js9.0000000000001213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/05/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment. METHODS This retrospective, bicentric study included 302 patients with PDAC (training: n =167, OPM-positive, n =22; internal test: n =72, OPM-positive, n =9: external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts. RESULTS Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI: 0.790-0.903), 0.845 (95% CI: 0.740-0.919), and 0.852 (95% CI: 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model. CONCLUSIONS The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.
Collapse
Affiliation(s)
- Siya Shi
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University
| | - Chuxuan Lin
- Medical AI Lab, School of Biomedical Engineering
- Marshall Laboratory of Biomedical Engineering, Shenzhen University
| | - Jian Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou
- South China Hospital, Medical School, Shenzhen University
| | - Luyong Wei
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University
| | - Mingjie Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University
| | - Jian Zhang
- Shenzhen University Medical School
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, People's Republic of China
| | - Kangyang Cao
- Medical AI Lab, School of Biomedical Engineering
- Marshall Laboratory of Biomedical Engineering, Shenzhen University
| | - Yaheng Fan
- Medical AI Lab, School of Biomedical Engineering
- Marshall Laboratory of Biomedical Engineering, Shenzhen University
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering
- Marshall Laboratory of Biomedical Engineering, Shenzhen University
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, People's Republic of China
| | - Yanji Luo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University
| |
Collapse
|
4
|
Sun S, Li L, Xu M, Wei Y, Shi F, Liu S. Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction. Abdom Radiol (NY) 2024:10.1007/s00261-024-04306-8. [PMID: 38656367 DOI: 10.1007/s00261-024-04306-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 03/17/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
PURPOSE To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC. METHODS This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM). RESULTS T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively. CONCLUSION A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.
Collapse
Affiliation(s)
- Shuangshuang Sun
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210008, China
| | - Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200000, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| |
Collapse
|
5
|
Zhang S, Hu Q, Chen X, Zhou N, Huang Q, Tan S, Su M, Gou H. 68Ga-FAPI-04 positron emission tomography/CT and laparoscopy for the diagnosis of occult peritoneal metastasis in newly diagnosed locally advanced gastric cancer: study protocol of a single-centre prospective cohort study. BMJ Open 2024; 14:e075680. [PMID: 38643004 PMCID: PMC11033661 DOI: 10.1136/bmjopen-2023-075680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 03/22/2024] [Indexed: 04/22/2024] Open
Abstract
INTRODUCTION Accurate baseline clinical staging is critical to inform treatment decision-making for patients with gastric cancers. Peritoneal metastasis (PM) is the most common form of metastasis in gastric cancer and mainly diagnosed by diagnostic laparoscopy and peritoneal lavage evaluation. However, diagnostic laparoscopy is invasive and less cost-effective. It is urgent to develop a safe, fast and non-invasive functional imaging method to verify the peritoneal metastasis of gastric cancer. The aim of our study was to evaluate the proportion of patients in whom 68Ga-FAPI-04 positron emission tomography/CT (PET/CT) led to a change in treatment strategy and to assess the diagnostic accuracy of 68Ga-FAPI-04 PET/CT for the detection of occult peritoneal metastasis compared with laparoscopic exploration. METHODS AND ANALYSIS In this single-centre, prospective diagnostic test accuracy study, a total of 48 patients with locally advanced gastric or gastro-oesophageal junction adenocarcinoma (cT4a-b, N0-3, M0, based on CT images) who are considering radical tumour surgery will be recruited. All participants will undergo 68Ga-FAPI-04 PET/CT before the initiation of laparoscopic exploration. The primary outcome is the proportion of patients with occult peritoneal metastatic lesions detected by 68Ga-FAPI-04 PET/CT, leading to a change in therapy strategy. The secondary outcomes include the diagnostic performance of 68Ga-FAPI-04 PET/CT for occult peritoneal metastasis, including sensitivity, specificity, accuracy, positive predictive value and negative predictive value. ETHICS AND DISSEMINATION The study protocol was approved by the Ethics Committee of West China Hospital, Sichuan University (2022-1484). Study results will be presented at public and scientific conferences and in peer-reviewed journals. TRIAL REGISTRATION NUMBER ChiCTR2300067591.
Collapse
Affiliation(s)
- Shunyu Zhang
- Department of Medical Oncology, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Gastric Cancer Center, Sichuan University West China Hospital, Chengdu, Sichuan, China
| | - Qiancheng Hu
- Department of Medical Oncology, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Gastric Cancer Center, Sichuan University West China Hospital, Chengdu, Sichuan, China
| | - Xinchuan Chen
- Department of Hematology, Sichuan University, Chengdu, Sichuan, China
| | - Nan Zhou
- Department of Medical Oncology, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Gastric Cancer Center, Sichuan University West China Hospital, Chengdu, Sichuan, China
| | - Qiyue Huang
- Department of Medical Oncology, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Gastric Cancer Center, Sichuan University West China Hospital, Chengdu, Sichuan, China
| | - Sirui Tan
- Department of Medical Oncology, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Gastric Cancer Center, Sichuan University West China Hospital, Chengdu, Sichuan, China
| | - Minggang Su
- Department of Nuclear Medicine, Sichuan University, Chengdu, Sichuan, China
| | - Hongfeng Gou
- Department of Medical Oncology, Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
- Gastric Cancer Center, Sichuan University West China Hospital, Chengdu, Sichuan, China
| |
Collapse
|
6
|
Feng QX, Zhu ZN, Li Q, Liu XS. Dual-energy CT quantitative parameters to evaluate occult peritoneal metastasis in advanced gastric cancer preoperatively. Abdom Radiol (NY) 2024:10.1007/s00261-024-04303-x. [PMID: 38634880 DOI: 10.1007/s00261-024-04303-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/14/2024] [Accepted: 03/20/2024] [Indexed: 04/19/2024]
Abstract
PURPOSE To explore whether dual-energy CT (DECT) quantitative parameters could provide analytic value for the diagnosis of patients with occult peritoneal metastasis (OPM) in advanced gastric cancer preoperatively. MATERIALS AND METHODS This retrospective study included 219 patients with advanced gastric cancer and DECT scans. The patient's clinical data and DECT related iodine concentration (IC) parameters and effective atomic number (Zeff) were collated and analyzed among noun-peritoneal metastasis (NPM), OPM and radiologically peritoneal metastasis (RPM) groups. The predictive performance of the DECT parameters was compared with that of the conventional CT features and clinical characteristics through evaluating area under curve of the precision-recall (AUC-PR), F1 score, balanced accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS Borrmann IV type diagnosed on CT and serum tumor indicator CA125 index were statistically different between the NPM and OPM groups. DECT parameters included IC, normalized IC (NIC), and Zeff of PM group were lower than the NPM group. The DECT predictive nomogram combined three independent DECT parameters produced a better diagnostic performance than the conventional CT feature Borrmann IV type and serum CA125 index in AUC-PR with 0.884 vs 0.368 vs 0.189, but similar to the combined indicator which was based on the DECT parameters, the conventional CT feature, and serum CA125 index in AUC-PR with 0.884 vs 0.918. CONCLUSION The lower quantitative NIC, IC ratio, and Zeff on DECT was associated with peritoneal metastasis in advanced gastric cancer and was promising to identify patients with OPM noninvasively.
Collapse
Affiliation(s)
- Qiu-Xia Feng
- Department of Radiology, The First Affiliated Hospital With Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210009, Jiangsu Province, China
| | - Zhen-Ning Zhu
- Department of Radiology, The First Affiliated Hospital With Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210009, Jiangsu Province, China
| | - Qiong Li
- Department of Radiology, The First Affiliated Hospital With Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210009, Jiangsu Province, China
| | - Xi-Sheng Liu
- Department of Radiology, The First Affiliated Hospital With Nanjing Medical University, No. 300, Guangzhou Road, Nanjing, 210009, Jiangsu Province, China.
| |
Collapse
|
7
|
Zhang J, Fu L, Wang H, Yonemura A, Semba T, Yasuda-Yoshihara N, Nishimura A, Tajiri T, Tong Y, Yasuda T, Uchihara T, Yamazaki M, Okamoto Y, Yamasaki J, Nagano O, Baba H, Ishimoto T. RAC1-mediated integrin alpha-6 expression in E-cadherin-deficient gastric cancer cells promotes interactions with the stroma and peritoneal dissemination. Cancer Lett 2024; 591:216901. [PMID: 38641311 DOI: 10.1016/j.canlet.2024.216901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/09/2024] [Accepted: 04/15/2024] [Indexed: 04/21/2024]
Abstract
Diffuse-type gastric cancer (DGC) is a subtype of gastric cancer that is prone to peritoneal dissemination, with poor patient prognosis. Although intercellular adhesion loss between cancer cells is a major characteristic of DGCs, the mechanism underlying the alteration in cell-to-extracellular matrix (ECM) adhesion is unclear. We investigated how DGCs progress and cause peritoneal dissemination through interactions between DGC cells and the tumour microenvironment (TME). P53 knockout and KRASG12V-expressing (GAN-KP) cells and Cdh1-deleted GAN-KP (GAN-KPC) cells were orthotopically transplanted into the gastric wall to mimic peritoneal dissemination. The GAN-KPC tumour morphology was similar to that of human DGCs containing abundant stroma. RNA sequencing revealed that pathways related to Rho GTPases and integrin-ECM interactions were specifically increased in GAN-KPC cells compared with GAN-KP cells. Notably, we found that Rac Family Small GTPase 1 (RAC1) induces Integrin Subunit Alpha 6 (ITGA6) trafficking, leading to its enrichment on the GC cell membrane. Fibroblasts activate the FAK/AKT pathway in GC cells by mediating extracellular matrix (ECM)-Itga6 interactions, exacerbating the malignant phenotype. In turn, GC cells induce abnormal expression of fibroblast collagen and its transformation into cancer-associated fibroblasts (CAFs), resulting in DGC-like subtypes. These findings indicate that Cdh1 gene loss leads to abnormal expression and changes in the subcellular localization of ITGA6 through RAC1 signalling. The latter, through interactions with CAFs, allows for peritoneal dissemination.
Collapse
Affiliation(s)
- Jun Zhang
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Lingfeng Fu
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Huaitao Wang
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Atsuko Yonemura
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Takashi Semba
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Noriko Yasuda-Yoshihara
- Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Akiho Nishimura
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan
| | - Takuya Tajiri
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Yilin Tong
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Tadahito Yasuda
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Tomoyuki Uchihara
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Masaya Yamazaki
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yuya Okamoto
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Juntaro Yamasaki
- Division of Gene Regulation, Cancer Center, Fujita Health University, Toyoake, Japan
| | - Osamu Nagano
- Division of Gene Regulation, Cancer Center, Fujita Health University, Toyoake, Japan
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan; Center for Metabolic Regulation of Healthy Ageing, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Takatsugu Ishimoto
- Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan; Gastrointestinal Cancer Biology, International Research Center of Medical Sciences (IRCMS), Kumamoto University, Kumamoto, Japan; Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
| |
Collapse
|
8
|
Wei GX, Zhou YW, Li ZP, Qiu M. Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis. Heliyon 2024; 10:e29249. [PMID: 38601686 PMCID: PMC11004411 DOI: 10.1016/j.heliyon.2024.e29249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 03/26/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024] Open
Abstract
Peritoneal carcinomatosis (PC) is a type of secondary cancer which is not sensitive to conventional intravenous chemotherapy. Treatment strategies for PC are usually palliative rather than curative. Recently, artificial intelligence (AI) has been widely used in the medical field, making the early diagnosis, individualized treatment, and accurate prognostic evaluation of various cancers, including mediastinal malignancies, colorectal cancer, lung cancer more feasible. As a branch of computer science, AI specializes in image recognition, speech recognition, automatic large-scale data extraction and output. AI technologies have also made breakthrough progress in the field of peritoneal carcinomatosis (PC) based on its powerful learning capacity and efficient computational power. AI has been successfully applied in various approaches in PC diagnosis, including imaging, blood tests, proteomics, and pathological diagnosis. Due to the automatic extraction function of the convolutional neural network and the learning model based on machine learning algorithms, AI-assisted diagnosis types are associated with a higher accuracy rate compared to conventional diagnosis methods. In addition, AI is also used in the treatment of peritoneal cancer, including surgical resection, intraperitoneal chemotherapy, systemic chemotherapy, which significantly improves the survival of patients with PC. In particular, the recurrence prediction and emotion evaluation of PC patients are also combined with AI technology, further improving the quality of life of patients. Here we have comprehensively reviewed and summarized the latest developments in the application of AI in PC, helping oncologists to comprehensively diagnose PC and provide more precise treatment strategies for patients with PC.
Collapse
Affiliation(s)
- Gui-Xia Wei
- Department of Abdominal Cancer, Cancer Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yu-Wen Zhou
- Department of Colorectal Cancer Center, West China Hospital of Sichuan University, Chengdu, China
| | - Zhi-Ping Li
- Department of Abdominal Cancer, Cancer Center, West China Hospital of Sichuan University, Chengdu, China
| | - Meng Qiu
- Department of Colorectal Cancer Center, West China Hospital of Sichuan University, Chengdu, China
| |
Collapse
|
9
|
Zhu ZN, Feng QX, Li Q, Xu WY, Liu XS. Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer. J Comput Assist Tomogr 2024:00004728-990000000-00302. [PMID: 38595104 DOI: 10.1097/rct.0000000000001600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
OBJECTIVE The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features. METHODS This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy. Patients with NPM means patients have neither PM nor other distant metastases, indicating there is no evidence of distant metastases in patients with AGC. Patients' clinical characteristics and CT features such as tumor marker, Borrmann IV, enhancement patterns, and pelvic ascites were observed by 2 experienced radiologists. Computed tomography features and clinical characteristics were combined to construct an indicator for identifying the presence of OPM in patients with AGC based on a logistic regression model. Receiver operating characteristic curves and the area under the receiver operating characteristic curve (AUC) were generated to assess the diagnostic performance of the combined indicator. RESULTS Four independent predictors (Borrmann IV, pelvic ascites, carbohydrate antigen 125, and normalized arterial CT value) differed significantly between OPM and NPM and performed outstandingly in distinguishing patients with OPM from those without PM (AUC = 0.643-0.696). The combined indicator showed a higher AUC value than the independent risk factors (0.820 vs 0.643-0.696). CONCLUSIONS The combined indicator based on abdominopelvic CT features and carbohydrate antigen 125 may assist clinicians in identifying the presence of CT OPMs in patients with AGC.
Collapse
Affiliation(s)
- Zhen-Ning Zhu
- From the Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | | | | | | | | |
Collapse
|
10
|
Zhang P, Gao C, Huang Y, Chen X, Pan Z, Wang L, Dong D, Li S, Qi X. Artificial intelligence in liver imaging: methods and applications. Hepatol Int 2024; 18:422-434. [PMID: 38376649 DOI: 10.1007/s12072-023-10630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/18/2023] [Indexed: 02/21/2024]
Abstract
Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.
Collapse
Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Chaofei Gao
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Yifei Huang
- Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangyi Chen
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Zhuoshi Pan
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Southeast University, Nanjing, China.
| |
Collapse
|
11
|
Marvaso G, Isaksson LJ, Zaffaroni M, Vincini MG, Summers PE, Pepa M, Corrao G, Mazzola GC, Rotondi M, Mastroleo F, Raimondi S, Alessi S, Pricolo P, Luzzago S, Mistretta FA, Ferro M, Cattani F, Ceci F, Musi G, De Cobelli O, Cremonesi M, Gandini S, La Torre D, Orecchia R, Petralia G, Jereczek-Fossa BA. Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models. Eur Radiol 2024:10.1007/s00330-024-10699-3. [PMID: 38507053 DOI: 10.1007/s00330-024-10699-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/29/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024]
Abstract
OBJECTIVE To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. METHODS Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. RESULTS The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. CONCLUSIONS Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. CLINICAL RELEVANCE STATEMENT The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. KEY POINTS • Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. • The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. •Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.
Collapse
Affiliation(s)
- Giulia Marvaso
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Maria Giulia Vincini
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy.
| | - Paul Eugene Summers
- Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Corrao
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Marco Rotondi
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Federico Mastroleo
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- University of Piemonte Orientale, Novara, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Sarah Alessi
- Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Paola Pricolo
- Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stefano Luzzago
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Division of Urology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Division of Urology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Ferro
- Division of Urology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Federica Cattani
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Francesco Ceci
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Division of Nuclear Medicine, IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Gennaro Musi
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Division of Urology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Ottavio De Cobelli
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Division of Urology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Gandini
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Davide La Torre
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- SKEMA Business School, Université Côte d'Azur, Sophia Antipolis, France
| | - Roberto Orecchia
- Scientific Directorate, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Division of Radiology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| |
Collapse
|
12
|
Chen W, Zhang W, Chen X, Dong W, Cai Y, Cheng J, Jin J. Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting. Clin Transl Oncol 2024:10.1007/s12094-024-03417-4. [PMID: 38467894 DOI: 10.1007/s12094-024-03417-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024]
Abstract
BACKGROUND AND OBJECTIVE Neoadjuvant chemotherapy results in various responses when used to treat locally advanced gastric cancer, we aimed to develop and validate a predictive model of the response to neoadjuvant chemotherapy in patients with gastric cancer. METHODS A total of 128 patients with locally advanced gastric cancer who underwent pre-treatment computed tomography (CT) scanning followed by neoadjuvant chemoradiotherapy were included (training cohort: n = 64; validation cohort: n = 64). We built a radiomics score combined with laboratory parameters to create a nomogram for predicting the efficacy of neoadjuvant chemotherapy and calculating scores for risk factors. RESULTS The radiomics score system demonstrated good stability and prediction performance for the response to neoadjuvant chemotherapy, with the area under the curve of the training and validation cohorts being 0.8 and 0.64, respectively. The radiomics score proved to be an independent risk factor affecting the efficacy of neoadjuvant chemotherapy. In addition to the radiomics score, four other risk factors were included in the nomogram, namely the platelet-to-lymphocyte ratio, total bilirubin, ALT/AST, and CA199. The model had a C-index of 0.8. CONCLUSIONS Our results indicated that radiomics features could be potential biomarkers for the early prediction of the response to neoadjuvant treatment.
Collapse
Affiliation(s)
- Wenjing Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Weiteng Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xietao Chen
- School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Weisong Dong
- Department of Pathology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yiqi Cai
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jun Cheng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Jinji Jin
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
| |
Collapse
|
13
|
Ji C, Ma Y, Zheng Z, Liu S, Zhou Z. Borrmann Type IV Gastric Cancer: Computed Tomography Features and Corresponding Pathological Findings. J Comput Assist Tomogr 2024; 48:200-205. [PMID: 37800282 DOI: 10.1097/rct.0000000000001550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
OBJECTIVE We aimed to analyze the association between computed tomography (CT) features and the corresponding pathological findings in Borrmann type IV (BT-4) gastric cancers and explore the pathological basis of the characteristic CT features. METHODS This retrospective study included 84 patients with BT-4 gastric cancers who underwent contrast-enhanced CT and surgical resection. Preoperative CT features were evaluated, including the major location, range, circumferential invasion, perigastric fat infiltration, enlarged lymph nodes, layered enhancement, degree of enhancement, and peak enhanced phase. Postoperative pathological findings were also recorded. Differences in CT features according to different World Health Organization types, surgical margin, adjacent organ invasion, and peritoneal status were assessed using the χ 2 or Fisher exact test (n < 5). RESULTS The most common World Health Organization type of BT-4 gastric cancer was poorly cohesive carcinoma (65.5%), which tended to show circumferential invasion, fewer enlarged lymph nodes, and layered enhancement. Although 82 patients with BT-4 gastric cancer (97.6%) had positive lymph nodes, only 26 (31.0%) had enlarged lymph nodes. Lesions originating from the gastroesophageal junction had a higher rate of positive margins ( P < 0.05). Adjacent organ invasion was more likely to occur in lesions with perigastric fat infiltration ( P < 0.05). Patients with circumferential invasion tended to show peritoneal metastasis ( P < 0.05). CONCLUSIONS The characteristic CT features of BT-4 gastric cancer may be attributed to the corresponding pathological findings. Recognizing the association between CT features and pathological findings may help evaluate the aggressiveness of BT-4 gastric cancers.
Collapse
Affiliation(s)
| | - Yi Ma
- From the Departments of Radiology
| | - Zhong Zheng
- Pathology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Song Liu
- From the Departments of Radiology
| | | |
Collapse
|
14
|
Xu P, Liu K, Huang S, Lv J, Yan Z, Ge H, Cheng Q, Chen Z, Ji P, Qian Y, Li B, Xu H, Yang L, Xu Z, Zhang D. N 6-methyladenosine-modified MIB1 promotes stemness properties and peritoneal metastasis of gastric cancer cells by ubiquitinating DDX3X. Gastric Cancer 2024; 27:275-291. [PMID: 38252226 DOI: 10.1007/s10120-023-01463-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/19/2023] [Indexed: 01/23/2024]
Abstract
BACKGROUND Peritoneal metastasis (PM), one of the most typical forms of metastasis in advanced gastric cancer (GC), indicates a poor prognosis. Exploring the potential molecular mechanism of PM is urgently necessary, as it has not been well studied. E3 ubiquitin ligase has been widely established to exert a biological function in various cancers, but its mechanism of action in GC with PM remains unknown. METHODS The effect of MIB1 on PM of GC was confirmed in vitro and in vivo. Co-immunoprecipitation (Co-IP) and mass spectrometry demonstrated the association between MIB1 and DDX3X. Western blot, flow cytometry and immunofluorescence determined that DDX3X was ubiquitylated by MIB1 and promoted stemness. We further confirmed that METTL3 promoted the up-regulation of MIB1 by RNA immunoprecipitation (RIP), luciferase reporter assay and other experiments. RESULTS We observed that the E3 ubiquitin ligase Mind bomb 1 (MIB1) was highly expressed in PMs, and patients with PM with high MIB1 expression showed a worse prognosis than those with low MIB1 expression. Mechanistically, our study demonstrated that the E3 ubiquitin ligase MIB1 promoted epithelial-mesenchymal transition (EMT) progression and stemness in GC cells by degrading DDX3X. In addition, METTL3 mediated m6A modification to stabilize MIB1, which required the m6A reader IGF2BP2. CONCLUSIONS Our study elucidated the specific molecular mechanism by which MIB1 promotes PM of GC, and suggested that targeting the METTL3-MIB1-DDX3X axis may be a promising therapeutic strategy for GC with PM.
Collapse
Affiliation(s)
- Peng Xu
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Kanghui Liu
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Shansong Huang
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Jialun Lv
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Zhengyuan Yan
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
- Department of Surgery, Nanjing Lishui People's Hospital, Nanjing, 211200, China
| | - Han Ge
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Quan Cheng
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Zetian Chen
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Peicheng Ji
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Yawei Qian
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Bowen Li
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Hao Xu
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Li Yang
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Zekuan Xu
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Diancai Zhang
- Department of General Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China.
| |
Collapse
|
15
|
Mao KZ, Ma C, Song B. Radiomics advances in the evaluation of pancreatic cystic neoplasms. Heliyon 2024; 10:e25535. [PMID: 38333791 PMCID: PMC10850586 DOI: 10.1016/j.heliyon.2024.e25535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/23/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024] Open
Abstract
With the development of medical imaging, the detection rate of pancreatic cystic neoplasms (PCNs) has increased greatly. Serous cystic neoplasm, solid pseudopapillary neoplasm, intraductal papillary mucinous neoplasm and mucinous cystic neoplasm are the main subtypes of PCN, and their treatment options vary greatly due to the different biological behaviours of the tumours. Different from conventional qualitative imaging evaluation, radiomics is a promising noninvasive approach for the diagnosis, classification, and risk stratification of diseases involving high-throughput extraction of medical image features. We present a review of radiomics in the diagnosis of serous cystic neoplasm and mucinous cystic neoplasm, risk classification of intraductal papillary mucinous neoplasm and prediction of solid pseudopapillary neoplasm invasiveness compared to conventional imaging diagnosis. Radiomics is a promising tool in the field of medical imaging, providing a noninvasive, high-performance model for preoperative diagnosis and risk stratification of PCNs and improving prospects regarding management of these diseases. Further studies are warranted to investigate MRI image radiomics in connection with PCNs to improve the diagnosis and treatment strategies in the management of PCN patients.
Collapse
Affiliation(s)
- Kuan-Zheng Mao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
- College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China
| | - Bin Song
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| |
Collapse
|
16
|
Li X, Fan Y, Tong J, Lou M. Risk factors, prognostic factors, and nomograms for distant metastases in patients with gastroenteropancreatic neuroendocrine tumors: a population-based study. Front Endocrinol (Lausanne) 2024; 15:1264952. [PMID: 38449852 PMCID: PMC10916283 DOI: 10.3389/fendo.2024.1264952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Background Patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) have a poor prognosis for distant metastasis. Currently, there are no studies on predictive models for the risk of distant metastasis in GEP-NETs. Methods In this study, risk factors associated with metastasis in patients with GEP-NETs in the Surveillance, Epidemiology, and End Results (SEER) database were analyzed by univariate and multivariate logistic regression, and a nomogram model for metastasis risk prediction was constructed. Prognostic factors associated with distant metastasis in patients with GEP-NETs were analyzed by univariate and multivariate Cox, and a nomogram model for prognostic prediction was constructed. Finally, the performance of the nomogram model predictions is validated by internal validation set and external validation set. Results A total of 9145 patients with GEP-NETs were enrolled in this study. Univariate and multivariate logistic analysis demonstrated that T stage, N stage, tumor size, primary site, and histologic types independent risk factors associated with distant metastasis in GEP-NETs patients (p value < 0.05). Univariate and multivariate Cox analyses demonstrated that age, histologic type, tumor size, N stage, and primary site surgery were independent factors associated with the prognosis of patients with GEP-NETs (p value < 0.05). The nomogram model constructed based on metastasis risk factors and prognostic factors can predict the occurrence of metastasis and patient prognosis of GEP-NETs very effectively in the internal training and validation sets as well as in the external validation set. Conclusion In conclusion, we constructed a new distant metastasis risk nomogram model and a new prognostic nomogram model for GEP-NETs patients, which provides a decision-making reference for individualized treatment of clinical patients.
Collapse
Affiliation(s)
- Xinwei Li
- Department of Gastroenterology, Affiliated Cancer Hospital of Bengbu Medical College, Bengbu, China
| | - Yongfei Fan
- Department of Thoracic Surgery, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Jichun Tong
- Department of Thoracic Surgery, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, China
| | - Ming Lou
- Department of Thoracic Surgery, The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University, Changzhou, China
| |
Collapse
|
17
|
Dai L, Yan W, Ren X, Liu D, Chang L, Lin S, Wu H, Kang H, Ma X. Construction and Validation of a Nomogram Predicting the Overall Survival Benefit of Unilateral Breast Cancer Patients Undergoing Contralateral Prophylactic Mastectomy. Clin Breast Cancer 2024:S1526-8209(24)00039-9. [PMID: 38521702 DOI: 10.1016/j.clbc.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/02/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Currently, research on the prognostic factors of unilateral breast cancer (UBC) patients receiving contralateral prophylactic mastectomy (CPM) is limited. This study aimed to construct a new nomogram to predict these patients' overall survival (OS). METHODS In this retrospective study, 88,477 patients who underwent CPM or unilateral mastectomy (UM) were selected from the Surveillance, Epidemiology, and End Results database. Kaplan-Meier curves and Cox regression analyses were used to determine the difference in the impact of the 2 surgical methods on the prognosis. Multivariate Cox analysis was used to determine the best prognostic variable and construct a nomogram. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the discrimination capability and clinical effectiveness of the nomogram. RESULTS The prognosis of patients receiving CPM and UM was significantly different. The DCA curves indicated that the nomogram could provide more excellent clinical net benefits for these patients. The NRI and IDI of the nomogram demonstrated that its performance was better than that of the classical tumor-node-metastasis (TNM) staging system. CONCLUSION This study developed and validated a practical nomogram to predict the OS of UBC patients undergoing CPM, which provided a beneficial tool for clinical decision-making management.
Collapse
Affiliation(s)
- Luyao Dai
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wenyu Yan
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xueting Ren
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Dandan Liu
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Lidan Chang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shuai Lin
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hao Wu
- School of Basic Medical Sciences, Xi'an Key Laboratory of Immune Related Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Xiaobin Ma
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| |
Collapse
|
18
|
Wang Y, Tang L, Ying X, Li J, Shan F, Li S, Jia Y, Xue K, Miao R, Li Z, Li Z, Ji J. Pre- and Post-treatment Double-Sequential-Point Dynamic Radiomic Model in the Response Prediction of Gastric Cancer to Neoadjuvant Chemotherapy: 3-Year Survival Analysis. Ann Surg Oncol 2024; 31:774-782. [PMID: 37993745 DOI: 10.1245/s10434-023-14478-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 10/09/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND Prognosis prediction of patients with gastric cancer after neoadjuvant chemotherapy is suboptimal. This study aims to develop and validate a dynamic radiomic model for prognosis prediction of patients with gastric cancer on the basis of baseline and posttreatment features. PATIENTS AND METHODS This single-center cohort study included patients with gastric adenocarcinoma treated with neoadjuvant chemotherapy from June 2009 to July 2015 in the Gastrointestinal Cancer Center of Peking University Cancer Hospital. Their clinicopathological data, pre-treatment and post-treatment computed tomography (CT) images, and pathological reports were retrieved and analyzed. Four prediction models were developed and validated using tenfold cross-validation, with death within 3 years as the outcome. Model discrimination was compared by the area under the curve (AUC). The final radiomic model was evaluated for calibration and clinical utility using Hosmer-Lemeshow tests and decision curve analysis. RESULTS The study included 205 patients with gastric adenocarcinoma [166 (81%) male; mean age 59.9 (SD 10.3) years], with 71 (34.6%) deaths occurring within 3 years. The radiomic model alone demonstrated better discrimination than the pathological T stage (ypT) stage model alone (cross-validated AUC 0.598 versus 0.516, P = 0.009). The final radiomic model, which incorporated both radiomic and clinicopathological characteristics, had a significantly higher cross-validated AUC (0.769) than the ypT stage model (0.516), the radiomics alone model (0.598), and the ypT plus other clinicopathological characteristics model (0.738; all P < 0.05). Decision curve analysis confirmed the clinical utility of the final radiomic model. CONCLUSIONS The developed radiomic model had good accuracy and could be used as a decision aid tool in clinical practice to differentiate prognosis of patients with gastric cancer.
Collapse
Affiliation(s)
- Yinkui Wang
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China
| | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, People's Republic of China
| | - Xiangji Ying
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jiazheng Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, People's Republic of China
| | - Fei Shan
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China
| | - Shuangxi Li
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China
| | - Yongning Jia
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China
| | - Kan Xue
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China
| | - Rulin Miao
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China
| | - Zhemin Li
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China
| | - Ziyu Li
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China.
| | - Jiafu Ji
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Hai-Dian District, Beijing, People's Republic of China.
| |
Collapse
|
19
|
Xiao ML, Fu L, Wei Y, Liu AE, Cheng JJ, Ma FH, Li HM, Li YA, Lin ZJ, Zhang GF, Qiang JW. Intratumoral and peritumoral MRI radiomics nomogram for predicting parametrial invasion in patients with early-stage cervical adenocarcinoma and adenosquamous carcinoma. Eur Radiol 2024; 34:852-862. [PMID: 37610442 DOI: 10.1007/s00330-023-10042-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 08/24/2023]
Abstract
OBJECTIVE To develop a comprehensive nomogram based on MRI intra- and peritumoral radiomics signatures and independent risk factors for predicting parametrial invasion (PMI) in patients with early-stage cervical adenocarcinoma (AC) and adenosquamous carcinoma (ASC). METHODS A total of 460 patients with IB to IIB cervical AC and ASC who underwent preoperative MRI examination and radical trachelectomy/hysterectomy were retrospectively enrolled and divided into primary, internal validation, and external validation cohorts. The original (Ori) and wavelet (Wav)-transform features were extracted from the volumetric region of interest of the tumour (ROI-T) and 3mm- and 5mm-peritumoral rings (ROI-3 and ROI-5), respectively. Then the Ori and Ori-Wav feature-based radiomics signatures from the tumour (RST) and 3 mm- and 5 mm-peritumoral regions (RS3 and RS5) were independently built and their diagnostic performances were compared to select the optimal ones. Finally, the nomogram was developed by integrating optimal intra- and peritumoral signatures and clinical independent risk factors based on multivariable logistic regression analysis. RESULTS FIGO stage, disruption of the cervical stromal ring on MRI (DCSRMR), parametrial invasion on MRI (PMIMR), and serum CA-125 were identified as independent risk factors. The nomogram constructed by integrating independent risk factors, Ori-Wav feature-based RST, and RS5 yielded AUCs of 0.874 (0.810-0.922), 0.885 (0.834-0.924), and 0.966 (0.887-0.995) for predicting PMI in the primary, internal and external validation cohorts, respectively. Furthermore, the nomogram was superior to radiomics signatures and clinical model for predicting PMI in three cohorts. CONCLUSION The nomogram can preoperatively, accurately, and noninvasively predict PMI in patients with early-stage cervical AC and ASC. CLINICAL RELEVANCE STATEMENT The nomogram can preoperatively, accurately, and noninvasively predict PMI and facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy in patients with early-stage cervical AC and ASC. KEY POINTS The accurate preoperative prediction of PMI in early-stage cervical AC and ASC can facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy. The nomogram integrating independent risk factors, Ori-Wav feature-based RST, and RS5 can preoperatively, accurately, and noninvasively predict PMI in early-stage cervical AC and ASC. The nomogram was superior to radiomics signatures and clinical model for predicting PMI in early-stage cervical AC and ASC.
Collapse
Affiliation(s)
- Mei Ling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, China
| | - Le Fu
- Department of Radiology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, 536 ChangleRoad, Shanghai, 200092, China
| | - Yan Wei
- Department of Automation, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, 310023, China
| | - Ai E Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, 701 Yunjin Road, Shanghai, 200032, China
| | - Jie Jun Cheng
- Department of Radiology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, 536 ChangleRoad, Shanghai, 200092, China
| | - Feng Hua Ma
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, 128 Shenyang Road, Shanghai, 200090, China
| | - Hai Ming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China
| | - Yong Ai Li
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, China
| | - Zi Jing Lin
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, China
| | - Guo Fu Zhang
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, 128 Shenyang Road, Shanghai, 200090, China
| | - Jin Wei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, China.
| |
Collapse
|
20
|
Che K, Luo Y, Song X, Yang Z, Wang H, Shi T, Wang Y, Wang X, Wu H, Yu L, Liu B, Wei J. Macrophages reprogramming improves immunotherapy of IL-33 in peritoneal metastasis of gastric cancer. EMBO Mol Med 2024; 16:251-266. [PMID: 38238529 PMCID: PMC10897402 DOI: 10.1038/s44321-023-00012-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 09/24/2023] [Accepted: 11/20/2023] [Indexed: 02/17/2024] Open
Abstract
Peritoneal metastasis (PM) has a suppressive tumor immune microenvironment (TIME) that limits the effects of immunotherapy. This study aimed to investigate the immunomodulatory effects of intraperitoneal administration of IL-33, a cytokine that is reported to potentiate antitumor immunity and inhibit metastasis. We found survival was significantly prolonged in patients with high IL-33 mRNA expression. In immunocompetent mice, intraperitoneal administration of IL-33 could induce a celiac inflammatory environment, activate immunologic effector cells, and reverse the immunosuppressive tumor microenvironment, which effectively delayed tumor progression and PM of gastric cancer. Mechanistically, IL-33 could induce M2 polarization by activating p38-GATA-binding protein 3 signaling. IL-33 combined with anti-CSF1R or p38 inhibitor to regulate tumor-associated macrophages (TAMs) had a synergistic antitumor effect. Inducing a local inflammatory milieu by IL-33 administration provided a novel approach for treating peritoneal metastasis, which, when combined with TAM reprogramming to reshape TIME, can achieve better treatment efficacy.
Collapse
Affiliation(s)
- Keying Che
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China
- Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yuting Luo
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xueru Song
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Zhe Yang
- Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Hanbing Wang
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Tao Shi
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Yue Wang
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Xuan Wang
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Department of Pathology, The Third Affiliated Hospital of Soochow University, Changzhou, China
| | - Hongyan Wu
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Lixia Yu
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Baorui Liu
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Jia Wei
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China.
- Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing, China.
- Engineering Research Center of Protein and Peptide Medicine, Ministry of Education, Nanjing, China.
| |
Collapse
|
21
|
Shen S, Li C, Fan Y, Lu S, Yan Z, Liu H, Zhou H, Zhang Z. Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2024; 49:58-67. [PMID: 38615167 PMCID: PMC11017031 DOI: 10.11817/j.issn.1672-7347.2024.230248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 04/15/2024]
Abstract
OBJECTIVES Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.
Collapse
Affiliation(s)
- Shanshan Shen
- Department of Oncology, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000.
- Information Engineering School of Nanchang University, Nanchang 330031.
| | - Chunquan Li
- Information Engineering School of Nanchang University, Nanchang 330031
| | - Yaohua Fan
- Department of Oncology, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000
| | - Shanfu Lu
- Perception Vision Medical Technologies Co., Ltd., Guangzhou 510530
| | - Ziye Yan
- Perception Vision Medical Technologies Co., Ltd., Guangzhou 510530
| | - Hu Liu
- Department of Radiology, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000
| | - Haihang Zhou
- Department of Neurosurgery, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000
| | - Zijian Zhang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China.
| |
Collapse
|
22
|
Sun Z, Zhang T, Ahmad MU, Zhou Z, Qiu L, Zhou K, Xiong W, Xie J, Zhang Z, Chen C, Yuan Q, Chen Y, Feng W, Xu Y, Yu L, Wang W, Yu J, Li G, Jiang Y. Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer. J Clin Invest 2024; 134:e175834. [PMID: 38271117 PMCID: PMC10940098 DOI: 10.1172/jci175834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/22/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.
Collapse
Affiliation(s)
- Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | | | - Zixia Zhou
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Liang Qiu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Kangneng Zhou
- College of Computer Science, Nankai University, Tianjin, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jingjing Xie
- Graduate Group of Epidemiology, UCD, Davis, California, USA
| | - Zhicheng Zhang
- JancsiTech and Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yan Chen
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China
| | - Wanying Feng
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lequan Yu
- The Department of Statistics and Actuarial Science, The University of Hong Kong, HKSAR, Hong Kong, China
| | - Wei Wang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jiang Yu
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| |
Collapse
|
23
|
Luo H, Li J, Chen Y, Wu B, Liu J, Han M, Wu Y, Jia W, Yu P, Cheng R, Wang X, Ke J, Xian H, Tu J, Yi Y. Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia. BMC Neurol 2024; 24:45. [PMID: 38273251 PMCID: PMC10809767 DOI: 10.1186/s12883-024-03532-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/08/2024] [Indexed: 01/27/2024] Open
Abstract
PURPOSE To explore the predictive value of radiomics in predicting stroke-associated pneumonia (SAP) in acute ischemic stroke (AIS) patients and construct a prediction model based on clinical features and DWI-MRI radiomics features. METHODS Univariate and multivariate logistic regression analyses were used to identify the independent clinical predictors for SAP. Pearson correlation analysis and the least absolute shrinkage and selection operator with ten-fold cross-validation were used to calculate the radiomics score for each feature and identify the predictive radiomics features for SAP. Multivariate logistic regression was used to combine the predictive radiomics features with the independent clinical predictors. The prediction performance of the SAP models was evaluated using receiver operating characteristics (ROC), calibration curves, decision curve analysis, and subgroup analyses. RESULTS Triglycerides, the neutrophil-to-lymphocyte ratio, dysphagia, the National Institutes of Health Stroke Scale (NIHSS) score, and internal carotid artery stenosis were identified as clinically independent risk factors for SAP. The radiomics scores in patients with SAP were generally higher than in patients without SAP (P < 0. 05). There was a linear positive correlation between radiomics scores and NIHSS scores, as well as between radiomics scores and infarct volume. Infarct volume showed moderate performance in predicting the occurrence of SAP, with an AUC of 0.635. When compared with the other models, the combined prediction model achieved the best area under the ROC (AUC) in both training (AUC = 0.859, 95% CI 0.759-0.936) and validation (AUC = 0.830, 95% CI 0.758-0.896) cohorts (P < 0.05). The calibration curves and decision curve analysis further confirmed the clinical value of the nomogram. Subgroup analysis showed that this nomogram had potential generalization ability. CONCLUSION The addition of the radiomics features to the clinical model improved the prediction of SAP in AIS patients, which verified its feasibility.
Collapse
Affiliation(s)
- Haowen Luo
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
| | - Jingyi Li
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Yongsen Chen
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Bin Wu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Jianmo Liu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
| | - Mengqi Han
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Yifan Wu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Weijie Jia
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Pengfei Yu
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
| | - Rui Cheng
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Xiaoman Wang
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Jingyao Ke
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Hongfei Xian
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China
- School of Public Health, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Jianglong Tu
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China.
| | - Yingping Yi
- Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1MinDe Road, Nanchang, 330006, P.R. China.
| |
Collapse
|
24
|
Fu C, Zhang B, Guo T, Li J. Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques. Korean J Radiol 2024; 25:86-102. [PMID: 38184772 PMCID: PMC10788608 DOI: 10.3348/kjr.2023.0840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/28/2023] [Accepted: 10/08/2023] [Indexed: 01/08/2024] Open
Abstract
Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.
Collapse
Affiliation(s)
- Chen Fu
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
| | - Bangxing Zhang
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Tiankang Guo
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Junliang Li
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China.
| |
Collapse
|
25
|
Liu S, Deng J, Dong D, Fang M, Ye Z, Hu Y, Li H, Zhong L, Cao R, Zhao X, Shang W, Li G, Liang H, Tian J. Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer. Med Phys 2024; 51:267-277. [PMID: 37573524 DOI: 10.1002/mp.16647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/24/2023] [Accepted: 06/23/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. PURPOSE This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. METHODS A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). RESULTS The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis. CONCLUSIONS The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.
Collapse
Affiliation(s)
- Shengyuan Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jingyu Deng
- Department of Gastrointestinal Surgery, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Di Dong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
| | - Zhaoxiang Ye
- Department of Gastrointestinal Surgery, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yanfeng Hu
- Nanfang Hospital of Southern Medical University, Guangzhou, Guangdong, China
| | - Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
| | - Lianzhen Zhong
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Runnan Cao
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xun Zhao
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wenting Shang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guoxin Li
- Nanfang Hospital of Southern Medical University, Guangzhou, Guangdong, China
| | - Han Liang
- Department of Gastrointestinal Surgery, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jie Tian
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing, China
- Beijing Key Lab of Molecular Imaging, Beijing, China
| |
Collapse
|
26
|
Wang F, Zhang X, Tang L, Wu Q, Cai M, Li Y, Qu X, Qiu H, Zhang Y, Ying J, Zhang J, Sun L, Lin R, Wang C, Liu H, Qiu M, Guan W, Rao S, Ji J, Xin Y, Sheng W, Xu H, Zhou Z, Zhou A, Jin J, Yuan X, Bi F, Liu T, Liang H, Zhang Y, Li G, Liang J, Liu B, Shen L, Li J, Xu R. The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023. Cancer Commun (Lond) 2024; 44:127-172. [PMID: 38160327 PMCID: PMC10794017 DOI: 10.1002/cac2.12516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024] Open
Abstract
The 2023 update of the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Gastric Cancer focuses on standardizing cancer diagnosis and treatment in China, reflecting the latest advancements in evidence-based medicine, healthcare resource availability, and precision medicine. These updates address the differences in epidemiological characteristics, clinicopathological features, tumor biology, treatment patterns, and drug selections between Eastern and Western gastric cancer patients. Key revisions include a structured template for imaging diagnosis reports, updated standards for molecular marker testing in pathological diagnosis, and an elevated recommendation for neoadjuvant chemotherapy in stage III gastric cancer. For advanced metastatic gastric cancer, the guidelines introduce new recommendations for immunotherapy, anti-angiogenic therapy and targeted drugs, along with updated management strategies for human epidermal growth factor receptor 2 (HER2)-positive and deficient DNA mismatch repair (dMMR)/microsatellite instability-high (MSI-H) patients. Additionally, the guidelines offer detailed screening recommendations for hereditary gastric cancer and an appendix listing drug treatment regimens for various stages of gastric cancer. The 2023 CSCO Clinical Guidelines for Gastric Cancer updates are based on both Chinese and international clinical research and expert consensus to enhance their applicability and relevance in clinical practice, particularly in the heterogeneous healthcare landscape of China, while maintaining a commitment to scientific rigor, impartiality, and timely revisions.
Collapse
Affiliation(s)
- Feng‐Hua Wang
- Department of Medical OncologySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouGuangdongP. R. China
| | - Xiao‐Tian Zhang
- Department of Gastrointestinal OncologyKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer HospitalBeijingP. R. China
| | - Lei Tang
- Department of RadiologyPeking University Cancer HospitalBeijingP. R. China
| | - Qi Wu
- Department of Endoscopy CenterPeking University Cancer HospitalBeijingP. R. China
| | - Mu‐Yan Cai
- Department of PathologySun Yat‐sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center of Cancer MedicineGuangzhouGuangdongP. R. China
| | - Yuan‐Fang Li
- Department of Gastric SurgerySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouGuangdongP. R. China
| | - Xiu‐Juan Qu
- Department of Medical OncologyThe First Hospital of China Medical UniversityShenyangLiaoningP. R. China
| | - Hong Qiu
- Department of Medical OncologyTongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and TechnologyWuhanHubeiP. R. China
| | - Yu‐Jing Zhang
- Department of RadiotherapySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouGuangdongP. R. China
| | - Jie‐Er Ying
- Department of Medical OncologyZhejiang Cancer HospitalHangzhouZhejiangP. R. China
| | - Jun Zhang
- Department of Medical OncologyRuijin HospitalShanghai Jiaotong University School of MedicineShanghaiP. R. China
| | - Ling‐Yu Sun
- Department of Surgical OncologyThe Fourth Affiliated Hospital of Harbin Medical UniversityHarbinHeilongjiangP. R. China
| | - Rong‐Bo Lin
- Department of Medical OncologyFujian Cancer HospitalFuzhouFujianP. R. China
| | - Chang Wang
- Tumor CenterThe First Hospital of Jilin UniversityChangchunJilinP. R. China
| | - Hao Liu
- Department of General SurgeryNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Miao‐Zhen Qiu
- Department of Medical OncologySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouGuangdongP. R. China
| | - Wen‐Long Guan
- Department of Medical OncologySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouGuangdongP. R. China
| | - Sheng‐Xiang Rao
- Department of RadiologyZhongshan HospitalFudan UniversityShanghaiP. R. China
| | - Jia‐Fu Ji
- Department of Gastrointestinal SurgeryPeking University Cancer HospitalBeijingP. R. China
| | - Yan Xin
- Pathology Laboratory of Gastrointestinal TumorThe First Hospital of China Medical UniversityShenyangLiaoningP. R. China
| | - Wei‐Qi Sheng
- Department of PathologyZhongshan Hospital Affiliated to Shanghai Fudan UniversityShanghaiP. R. China
| | - Hui‐Mian Xu
- Department of Gastrointestinal Oncology Surgery. The First Hospital of China Medical UniversityShenyangLiaoningP. R. China
| | - Zhi‐Wei Zhou
- Department of Gastric SurgerySun Yat‐sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center of Cancer MedicineGuangzhouGuangdongP. R. China
| | - Ai‐Ping Zhou
- Department of OncologyNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingP. R. China
| | - Jing Jin
- Department of Radiation OncologyShenzhen hospitalCancer Hospital of Chinese Academy of Medical SciencesBeijingP. R. China
| | - Xiang‐Lin Yuan
- Department of OncologyTongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and TechnologyWuhanHubeiP. R. China
| | - Feng Bi
- Department of Abdominal OncologyWest China Hospital of Sichuan UniversityChengduSichuanP. R. China
| | - Tian‐Shu Liu
- Department of Medical OncologyZhongshan Hospital Affiliated to Fudan UniversityShanghaiP. R. China
| | - Han Liang
- Department of Gastric SurgeryTianjin Medical University Cancer Institute & HospitalTianjinP. R. China
| | - Yan‐Qiao Zhang
- Department of Medical OncologyCancer Hospital of Harbin Medical UniversityHarbinHeilongjiangP. R. China
| | - Guo‐Xin Li
- Department of General SurgeryNanfang HospitalSouthern Medical UniversityGuangzhouGuangdongP. R. China
| | - Jun Liang
- Department of Medical OncologyPeking University International HospitalBeijingP. R. China
| | - Bao‐Rui Liu
- Department of Medical OncologyNanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjingP. R. China
| | - Lin Shen
- Department of GI OncologyKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer HospitalBeijingP. R. China
| | - Jin Li
- Department of OncologyEaster Hospital affiliated to Shanghai Tongji UniversityShanghaiP. R. China
| | - Rui‐Hua Xu
- Department of Medical OncologySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouGuangdongP. R. China
| |
Collapse
|
27
|
Yang J, Shu G, Chen T, Dong A, Dong C, Li W, Sun X, Zhou Y, Li D, Zhou J. ESM1 Interacts with c-Met to Promote Gastric Cancer Peritoneal Metastasis by Inducing Angiogenesis. Cancers (Basel) 2023; 16:194. [PMID: 38201620 PMCID: PMC10778290 DOI: 10.3390/cancers16010194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
The peritoneum is the most common metastatic site of advanced gastric cancer and is associated with extremely poor prognosis. Endothelial-specific molecule 1 (ESM1) was found to be significantly associated with gastric cancer peritoneal metastasis (GCPM); however, the biological functions and molecular mechanisms of ESM1 in regulating GCPM remain unclear. Herein, we demonstrated that ESM1 expression was significantly upregulated in gastric cancer tissues and positively correlated with platelet endothelial cell adhesion molecule-1 (CD31) levels. Moreover, clinical validation, in in vitro and in vivo experiments, confirmed that ESM1 promoted gastric cancer angiogenesis, eventually promoting gastric cancer peritoneal metastasis. Mechanistically, ESM1 promoted tumor angiogenesis by binding to c-Met on the vascular endothelial cell membrane. In addition, our results confirmed that ESM1 upregulated VEGFA, HIF1α, and MMP9 expression and induced angiogenesis by activating the MAPK/ERK pathway. In conclusion, our findings identified the role of ESM1 in gastric cancer angiogenesis and GCPM, thus providing insights into the diagnosis and treatment of advanced gastric cancer.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | - Dongbao Li
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China; (J.Y.); (G.S.); (T.C.); (A.D.); (C.D.); (W.L.); (X.S.); (Y.Z.)
| | - Jin Zhou
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou 215006, China; (J.Y.); (G.S.); (T.C.); (A.D.); (C.D.); (W.L.); (X.S.); (Y.Z.)
| |
Collapse
|
28
|
Liu P, Ding P, Wu H, Wu J, Yang P, Tian Y, Guo H, Zhao Q. Prediction of occult peritoneal metastases or positive cytology using CT in gastric cancer. Eur Radiol 2023; 33:9275-9285. [PMID: 37414883 DOI: 10.1007/s00330-023-09854-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 07/08/2023]
Abstract
OBJECTIVE Accurate prediction of preoperative occult peritoneal metastasis (OPM) is critical to selecting appropriate therapeutic regimen for gastric cancer (GC). Considering the clinical practicability, we develop and validate a visible nomogram that integrates the CT images and clinicopathological parameters for the individual preoperative prediction of OPM in GC. METHODS This retrospective study included 520 patients who underwent staged laparoscopic exploration or peritoneal lavage cytology (PLC) examination. Univariate and multivariate logistic regression results were used to screen model predictors and construct nomograms of OPM risk. The performance of the model was detected by using ROC, accuracy, and C-index. The bootstrap resampling method was considered internal validation of the model. The Delong test was used to evaluate the difference in AUC between the two models. RESULTS Grade 2 mural stratification, tumor thickness, and the Lauren classification diffuse were significant predictors of OPM (p < 0.05). The nomogram of these three factors (compared with the original model) showed a higher predictive effect (p < 0.001). The area under the curve (AUC) of the model was 0.830 (95% CI 0.788-0.873), and the internally validated AUC of 1000 bootstrap samples was 0.826 (95% CI 0.756-0.870). The sensitivity, specificity, and accuracy were 76.0%, 78.8%, and 78.3%, respectively. CONCLUSIONS CT phenotype-based nomogram demonstrates favorable discrimination and calibration, and it can be conveniently used for preoperative individual risk rating of OPM in GC. CLINICAL RELEVANCE STATEMENT In this study, the preoperative OPM prediction model based on CT images (mural stratification, tumor thickness) combined with pathological parameters (the Lauren classification) showed excellent predictive ability in GC, and it is also suitable for clinicians to use rather than limited to professional radiologists. KEY POINTS • Nomogram based on CT image analysis can effectively predict occult peritoneal metastasis in gastric cancer (training area under the curve (AUC) = 0.830 and bootstrap AUC = 0.826). • Nomogram model combined with CT features performed better than the original model (established using only clinicopathological parameters) in differentiating occult peritoneal metastasis of gastric cancer.
Collapse
Affiliation(s)
- Pengpeng Liu
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Ping'an Ding
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Haotian Wu
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Jiaxiang Wu
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Peigang Yang
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Yuan Tian
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Honghai Guo
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China
| | - Qun Zhao
- The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, Hebei, China.
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, China.
| |
Collapse
|
29
|
Xie Z, Zhang Q, Wang X, Chen Y, Deng Y, Lin H, Wu J, Huang X, Xu Z, Chi P. Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2023; 49:107118. [PMID: 37844471 DOI: 10.1016/j.ejso.2023.107118] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients. METHODS We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated. RESULTS The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.
Collapse
Affiliation(s)
- Zhongdong Xie
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China
| | - Qingwei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Digestive Diseases, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Xiaojie Wang
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China
| | - Yongchun Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yu Deng
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China
| | - Hanbin Lin
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jiashu Wu
- Department of Science and Technology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xinming Huang
- Department of Radiology, Union Hospital, Fujian Medical University, Fuzhou, China.
| | - Zongbin Xu
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China.
| | - Pan Chi
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China.
| |
Collapse
|
30
|
Xie J, Xue B, Bian S, Ji X, Lin J, Zheng X, Tang K. A radiomics nomogram based on 18 F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer. Nucl Med Commun 2023; 44:977-987. [PMID: 37578301 DOI: 10.1097/mnm.0000000000001742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
PURPOSE Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC. METHOD In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram. RESULT Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively. CONCLUSION The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.
Collapse
Affiliation(s)
- Jiageng Xie
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Beihui Xue
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Shuying Bian
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiaowei Ji
- Departments of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jie Lin
- Departments of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiangwu Zheng
- Departments of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Kun Tang
- Departments of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| |
Collapse
|
31
|
Zheng X, Liu K, Li C, Zhu C, Gao Y, Li J, Wu X. A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study. LA RADIOLOGIA MEDICA 2023; 128:1386-1397. [PMID: 37597124 DOI: 10.1007/s11547-023-01702-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/10/2023] [Indexed: 08/21/2023]
Abstract
PURPOSE To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation. METHODS 558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis. Association of the radiomics signature with PFS was evaluated. A radiomics nomogram was developed based on the radiomics signature, then its calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS In total, 6 CT radiomics features were finally selected. The radiomics signature was significantly associated with PFS (hazard ratio [HR] 4.531, 95% confidence interval [CI] 3.524-5.825, p < 0.001). Incorporating the radiomics signature into the radiomics nomogram resulted in better performance for the estimation of PFS (concordance index [C-index] 0.799) than with the clinical nomogram (C-index 0.629), as well as high 6 months and 12 months area under the curves of 0.885 and 0.846, respectively. Furthermore, the radiomics nomogram also significantly improved the classification accuracy for PFS outcomes, based on the net reclassification improvement (33.7%, 95% CI 0.216-0.609, p < 0.05) and integrated discrimination improvement (22.7%, 95% CI 0.168-0.278, p < 0.05). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram. CONCLUSION A CT-based radiomics nomogram exhibited a promising performance for predicting PFS in patients with SCLC, which could provide valuable information for individualized treatment.
Collapse
Affiliation(s)
- Xiaomin Zheng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Kaicai Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China
| | - Jianying Li
- CT Advanced Application, GE HealthCare China, Beijing, 100186, People's Republic of China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Shushan District, Hefei, 230031, Anhui, People's Republic of China.
| |
Collapse
|
32
|
Ruan D, Wang J, Ding T, Chen L, Du Y, Ruan Y, Cui W, Feng W. Targeting Adhesive Tumor Adventitia via Injectable Electrospun Short Fibers in Perfusion of Intraperitoneal Sporadic Tumors. SMALL METHODS 2023; 7:e2300681. [PMID: 37670530 DOI: 10.1002/smtd.202300681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/21/2023] [Indexed: 09/07/2023]
Abstract
Intraperitoneal sporadic tumor is a common and complicated syndrome in cancers, causing a high rate of death, and people find that intraperitoneal chemotherapy (IPC) can treat intraperitoneal sporadic tumors better than intravenous chemotherapy and surgery. However, the effectiveness and side effects of IPC are controversial, and the operation process of IPC is complicated. Herein, the injectable paclitaxel-loaded (PTX-loaded) electrospun short fibers are constructed through a series process of electrospinning, homogenizing, crosslinking, and subsequent polydopamine coating and folate acid (FA) modification. The evenly dispersed short fibers exhibited effective tumor cell killing and good injectable ability, which is convenient to use and greatly improved the complex operation procedure. Mussel-like protein poly-dopamine coating and FA modification endowed short fibers with the ability of targeted adhesion to tumors, and therefore the short fibers further acted as a kind of micro membrane that could release drugs to tumors at close range, maintaining local high drug concentration and prevent paclitaxel killing normal tissues. Thus, the target-adhesive injectable electrospun short fibers are expected to be the potential candidate for cancer treatment, especially the intraperitoneal sporadic tumors, which are hard to treat by surgery or intravenous chemotherapy.
Collapse
Affiliation(s)
- Dan Ruan
- Department of Gynecology and Obstetrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| | - Juan Wang
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| | - Tao Ding
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| | - Liang Chen
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| | - Yawei Du
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| | - Yiyin Ruan
- Department of Gynecology and Obstetrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| | - Wenguo Cui
- Department of Orthopaedics, Shanghai Key Laboratory for Prevention and Treatment of Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| | - Weiwei Feng
- Department of Gynecology and Obstetrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin 2nd Road, Shanghai, 200025, P. R. China
| |
Collapse
|
33
|
Yang C, Chen Y, Zhu L, Wang L, Lin Q. A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma. Eur Arch Otorhinolaryngol 2023; 280:5039-5047. [PMID: 37358652 DOI: 10.1007/s00405-023-08084-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVE As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various factors, making it difficult for clinical physicians to predict the outcome, the objective of this study was to develop a deep learning-based signature for risk stratification in NPC patients. METHODS A total of 293 patients were enrolled in the study and divided into training, validation, and testing groups with a ratio of 7:1:2. MRI scans and corresponding clinical information were collected, and the 3-year disease-free survival (DFS) was chosen as the endpoint. The Res-Net18 algorithm was used to develop two deep learning (DL) models and another solely based on clinical characteristics developed by multivariate cox analysis. The performance of both models was evaluated using the area under the curve (AUC) and the concordance index (C-index). Discriminative performance was assessed using Kaplan-Meier survival analysis. RESULTS The deep learning approach identified DL prognostic models. The MRI-based DL model showed significantly better performance compared to the traditional model solely based on clinical characteristics (AUC: 0.8861 vs 0.745, p = 0.04 and C-index: 0.865 vs 0.727, p = 0.03). The survival analysis showed significant survival differences between the risk groups identified by the MRI-based model. CONCLUSION Our study highlights the potential of MRI in predicting the prognosis of NPC through DL algorithm. This approach has the potential to become a novel tool for prognosis prediction and can help physicians to develop more valid treatment strategies in the future.
Collapse
Affiliation(s)
- Chen Yang
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China
| | - Yuan Chen
- Department of Computer Science, Xiamen University, Xiamen, Fujian, China
| | - Luchao Zhu
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China
| | - Liansheng Wang
- Department of Computer Science, Xiamen University, Xiamen, Fujian, China.
| | - Qin Lin
- Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
| |
Collapse
|
34
|
Gao H, Ji K, Bao L, Chen H, Lin C, Feng M, Tao L, Wang M. Establishment and verification of prediction model of occult peritoneal metastasis in advanced gastric cancer. World J Surg Oncol 2023; 21:320. [PMID: 37833730 PMCID: PMC10571475 DOI: 10.1186/s12957-023-03188-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 09/17/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND To investigate the risk factors associated with the development of occult peritoneal metastasis in advanced gastric cancer, and establish and externally validate a nomogram for predicting the occurrence of occult peritoneal metastasis in patients with advanced gastric cancer. METHODS A total of 111 patients with advanced gastric cancer who underwent laparoscopic exploration or peritoneal lavage cytology examination at the Affiliated Drum Tower Hospital of Nanjing University Medical School from August 2014 to December 2021 were retrospectively analyzed. The patients diagnosed between 2019 and 2021 were assigned to the training set (n = 64), while those diagnosed between 2014 and 2016 constituted the external validation set (n = 47). In the training set, patients were classified into two groups based on preoperative imaging and postoperative pathological data: the occult peritoneal metastasis group (OPMG) and the peritoneal metastasis negative group (PMNG). In the validation set, patients were classified into the occult peritoneal metastasis group (CY1P0, OPMG) and the peritoneal metastasis negative group (CY0P0, PMNG) based on peritoneal lavage cytology results. A nomogram was constructed using univariate and multivariate analyses. The performance of the nomogram was evaluated using Harrell's C-index, the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plots. RESULTS This study analyzed 22 potential variables of OPM in 111 gastric cancer patients who underwent laparoscopic exploration or peritoneal lavage cytology examination. Logistic regression analysis results showed that Lauren classification, CLDN18.2 score and CA125 were independent risk factors for OPM in patients with gastric cancer. We developed a simple and easy-to-use prediction nomogram of occult peritoneal metastasis in advanced gastric cancer. This nomogram had an excellent diagnostic performance. The AUC of the bootstrap model in the training set was 0.771 and in the validation set was 0.711. This model showed a good fitting and calibration and positive net benefits in decision curve analysis. CONCLUSION We have developed a prediction nomogram of OPM for gastric cancer. This novel nomogram has the potential to enhance diagnostic accuracy for occult peritoneal metastasis in gastric cancer patients.
Collapse
Affiliation(s)
- Hengfei Gao
- Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- Medical School of Nanjing University, Nanjing, China
| | - Kangkang Ji
- Department of Gastrointestinal, Fuyang People's Hospital, Fuyang, China
| | - Linsen Bao
- Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- Medical School of Nanjing University, Nanjing, China
| | - Hao Chen
- Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China
- Medical School of Nanjing University, Nanjing, China
| | - Chen Lin
- Department of General Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Min Feng
- Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
- Medical School of Nanjing University, Nanjing, China.
| | - Liang Tao
- Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
- Medical School of Nanjing University, Nanjing, China.
| | - Meng Wang
- Department of General Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
- Medical School of Nanjing University, Nanjing, China.
| |
Collapse
|
35
|
Cheng X, Zhang Y, Zhu M, Sun R, Liu L, Li X. Predicting response to CCRT for esophageal squamous carcinoma by a radiomics-clinical SHAP model. BMC Med Imaging 2023; 23:145. [PMID: 37779188 PMCID: PMC10544369 DOI: 10.1186/s12880-023-01089-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND Radical concurrent chemoradiotherapy (CCRT) is frequently used as the first-line treatment for patients with locally advanced esophageal cancer. Unfortunately, some patients respond poorly. To predict response to radical concurrent chemoradiotherapy in pre-treatment patients with esophageal squamous carcinoma (ESCC), and compare the predicting efficacies of radiomics features of primary tumor with or without regional lymph nodes, we developed a radiomics-clinical model based on the positioning CT images. Finally, SHapley Additive exPlanation (SHAP) was used to explain the models. METHODS This retrospective study enrolled 105 patients with medically inoperable and/or unresectable ESCC who underwent radical concurrent chemoradiotherapy (CCRT) between October 2018 and May 2023. Patients were classified into responder and non-responder groups with RECIST standards. The 11 recently admitted patients were chosen as the validation set, previously admitted patients were randomly split into the training set (n = 70) and the testing set (n = 24). Primary tumor site (GTV), the primary tumor and the uninvolved lymph nodes at risk of microscopic disease (CTV) were identified as Regions of Interests (ROIs). 1762 radiomics features from GTV and CTV were respectively extracted and then filtered by statistical differential analysis and Least Absolute Shrinkage and Selection Operator (LASSO). The filtered radiomics features combined with 13 clinical features were further filtered with Mutual Information (MI) algorithm. Based on the filtered features, we developed five models (Clinical Model, GTV Model, GTV-Clinical Model, CTV Model, and CTV-Clinical Model) using the random forest algorithm and evaluated for their accuracy, precision, recall, F1-Score and AUC. Finally, SHAP algorithm was adopted for model interpretation to achieve transparency and utilizability. RESULTS The GTV-Clinical model achieves an AUC of 0.82 with a 95% confidence interval (CI) of 0.76-0.99 on testing set and an AUC of 0.97 with a 95% confidence interval (CI) of 0.84-1.0 on validation set, which are significantly higher than those of other models in predicting ESCC response to CCRT. The SHAP force map provides an integrated view of the impact of each feature on individual patients, while the SHAP summary plots indicate that radiomics features have a greater influence on model prediction than clinical factors in our model. CONCLUSION GTV-Clinical model based on texture features and the maximum diameter of lesion (MDL) may assist clinicians in pre-treatment predicting ESCC response to CCRT.
Collapse
Affiliation(s)
- Xu Cheng
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China
| | - Yuxin Zhang
- School of Biomedical Engineering, Anhui Medical University, Hefei, 230032, China
| | - Min Zhu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China.
- School of Mathematics and Computer Science, Tongling University, Tongling, China.
| | - Ruixia Sun
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China
| | - Lingling Liu
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China
| | - Xueling Li
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China.
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, P.R. China.
- School of Biomedical Engineering, Anhui Medical University, Hefei, 230032, China.
| |
Collapse
|
36
|
Xue Y, Zhang H, Zheng Z, Liu X, Yin J, Zhang J. Predictive performance of radiomics for peritoneal metastasis in patients with gastric cancer: a meta-analysis and radiomics quality assessment. J Cancer Res Clin Oncol 2023; 149:12103-12113. [PMID: 37422882 DOI: 10.1007/s00432-023-05096-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE The purpose of this meta-analysis is to systematically review the diagnostic performance of radiomic techniques in predicting peritoneal metastasis in patients with gastric cancer, and to evaluate the quality of current research. METHODS We searched PubMed, Web of Science, EBSCO, Embase, and Cochrane databases for relevant studies up to April 3, 2023. Data extraction and quality evaluation were performed by two independent reviewers. Then we performed statistical analysis, including plotting the forest plot and summary receiver operating characteristic (SROC) curve, and source of heterogeneity analysis, through the MIDAS module in Stata 15. We performed meta-regression and subgroup analyses to analyze the sources of heterogeneity. Using the QUADAS-2 scale and the RQS scale to assess the quality of retrieved studies. RESULTS Ten studies with 6199 patients were finally included in our meta-analysis. Pooled sensitivity and specificity were 0.77 (95% confidence interval [CI]: 0.66, 0.86), and 0.88 (95% CI 0.80, 0.93), respectively. The overall AUC was 0.89 (95% CI 0.86, 0.92). The heterogeneity of this meta-analysis was high, with I2 = 88% (95% CI 75,100). The result of meta-regression showed that QUADAS-2 results, RQS results and machine learning method led to heterogeneity in sensitivity and specificity (P < 0.05). Furthermore, the image segmentation area and the presence or absence of combined clinical factors were associated with sensitivity heterogeneity and specificity heterogeneity, respectively. CONCLUSION Undoubtedly, radiomics has potential value in diagnosing peritoneal metastasis of gastric cancer, but the quality of current research is inconsistent, and more standardized and high-quality research is still needed in the future to achieve the transformation of radiomics results into clinical applications.
Collapse
Affiliation(s)
- Yasheng Xue
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Haiqiao Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Zhi Zheng
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Xiaoye Liu
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jie Yin
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jun Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China.
| |
Collapse
|
37
|
Solass W, Nadiradze G, Reymond MA, Bösmüller H. The Role of Additional Staining in the Assessment of the Peritoneal Regression Grading Score (PRGS) in Peritoneal Metastasis of Gastric Origin. Appl Immunohistochem Mol Morphol 2023; 31:583-589. [PMID: 37698957 DOI: 10.1097/pai.0000000000001152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/26/2023] [Indexed: 09/14/2023]
Abstract
INTRODUCTION The Peritoneal Regression Grading Score (PRGS) is a 4-tied histologic regression grading score for determining the response of peritoneal metastasis to chemotherapy. Peritoneal biopsies in every abdominal quadrant are recommended. A positive therapy response is defined as a decreasing or stable mean PRGS between 2 therapy cycles. The added value of periodic acid satin (PAS) and Ber-EP4 staining over HE staining for diagnosing PRGS1 (the absence of vital tumor cells) is unclear. MATERIALS AND METHODS A total of 339 biopsies obtained during 76 laparoscopies in 33 patients with peritoneal metastasis of gastric cancer were analyzed. Biopsies classified as PRGS 1 (no residual tumor, n=95) or indefinite (n=50) were stained with PAS, and remaining indefinite or PRGS1 cases additionally stained with BerEP4. RESULTS After PAS-staining tumor cells were detected in 28 out of 145 biopsies (19%), the remaining 117 biopsies were immunostained with Ber-EP4. Tumor cells were detected in 22 biopsies (19%). In total, additional staining allowed the detection of residual tumor cells in 50 out of 339 biopsies (15%) and changed the therapy response assessment in 7 out of 33 (21%) patients. CONCLUSIONS In summary, 25% (24 out of 95) of initially tumor-free samples (PRGS1) showed residual tumor cells after additional staining with PAS and/or BerEp4. Immunohistochemistry provided important additional information (the presence of tumor cells) in 22 of all 339 biopsies (11.2%). Further staining reduced the instances of unclear diagnosis from 50 to 0 and changed the therapy response assessment in 7 out of 33 patients (21%). We recommend additional staining in PRGS1 or unclear cases.
Collapse
Affiliation(s)
- Wiebke Solass
- Institute of Tissue Medicine and Pathology Bern, University Bern, Switzerland
- National Center for Pleura and Peritoneum
- Institute of Pathology
| | - Giorgi Nadiradze
- National Center for Pleura and Peritoneum
- Department of General and Transplant Surgery, University Hospital Tuebingen, Eberhard-Karls-University, Tuebingen, Germany
| | - Marc A Reymond
- National Center for Pleura and Peritoneum
- Department of General and Transplant Surgery, University Hospital Tuebingen, Eberhard-Karls-University, Tuebingen, Germany
| | | |
Collapse
|
38
|
Ma T, Wang H, Ye Z. Artificial intelligence applications in computed tomography in gastric cancer: a narrative review. Transl Cancer Res 2023; 12:2379-2392. [PMID: 37859746 PMCID: PMC10583011 DOI: 10.21037/tcr-23-201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/01/2023] [Indexed: 10/21/2023]
Abstract
Background and Objective Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed. Methods We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized. Key Content and Findings AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving. Conclusions AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists' workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.
Collapse
Affiliation(s)
- Tingting Ma
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hua Wang
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| |
Collapse
|
39
|
Huang F, Fang M. Prediction model of liver metastasis risk in patients with gastric cancer: A population-based study. Medicine (Baltimore) 2023; 102:e34702. [PMID: 37773864 PMCID: PMC10545098 DOI: 10.1097/md.0000000000034702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 07/20/2023] [Indexed: 10/01/2023] Open
Abstract
Liver was the most common site of distant metastasis in patients with gastric cancer (GC). The prediction model of the risk of liver metastasis was rarely proposed. Therefore, we aimed to establish a prediction model for liver metastasis in patients with GC. In this retrospective cohort study, we extracted demographic and clinical data of all the GC patients from the Surveillance, Epidemiology, and End Results registration database from 2010 to 2015. Patients were divided into training set (n = 1691) for model development and testing set (n = 3943) for validation. Univariable and multivariable logistic regression analyses were carried out on the training set to screen potential predictors of liver metastasis and constructed a prediction model. The receiver operator characteristics curves with the area under curve values were used to assess the predictive performance of the liver metastasis prediction model. And a nomogram of the prediction model was also constructed. Of the total 5634 GC patients, 444 (7.88%) had liver metastasis. Variables including age, gender, N stage, T stage, Lauren classification, tumor size, histological type, and surgery were included in the liver metastasis prediction model. The study results indicated that the model had excellent discriminative ability with an area under curve of 0.851 (95% confidence interval: 0.829-0.873) in the training set, and that of 0.849 (95% confidence interval: 0.813-0.885) in the testing set. We have developed an effective prediction model with 8 easily acquired predictors of liver metastasis. The prediction model could predict the risk of liver metastasis in GC patients and performed well, which would assist clinicians to make individualized prediction of liver metastasis in GC patients and adjust treatment strategies in time to improve the prognosis.
Collapse
Affiliation(s)
- Fang Huang
- Department of Oncology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, P. R. China
| | - Meihua Fang
- Department of Oncology, Shanghai Jiading District Hospital of Traditional Chinese Medicine, Shanghai, P. R. China
| |
Collapse
|
40
|
Li X, Zhang C, Li T, Lin X, Wu D, Yang G, Cao D. Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies. Sci Rep 2023; 13:15586. [PMID: 37730961 PMCID: PMC10511693 DOI: 10.1038/s41598-023-42916-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/16/2023] [Indexed: 09/22/2023] Open
Abstract
Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.
Collapse
Affiliation(s)
- Xiumei Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Tingting Li
- Department of Radiology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, 361021, Fujian, China
| | - Xiuqiang Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, 3663 North Zhangshan Road, Shanghai, 200062, China.
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, China.
- Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, China.
- Key Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Shanghai, 200062, China.
| |
Collapse
|
41
|
Xia B, Liu Y, Wang J, Lu Q, Lv X, Deng K, Yang J. Emerging role of exosome-shuttled noncoding RNAs in gastrointestinal cancers: From intercellular crosstalk to clinical utility. Pharmacol Res 2023; 195:106880. [PMID: 37543095 DOI: 10.1016/j.phrs.2023.106880] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 08/07/2023]
Abstract
Gastrointestinal cancer remains a significant global health burden. The pursuit of advancing the comprehension of tumorigenesis, along with the identification of reliable biomarkers and the development of precise therapeutic strategies, represents imperative objectives in this field. Exosomes, small membranous vesicles released by most cells, commonly carry functional biomolecules, including noncoding RNAs (ncRNAs), which are specifically sorted and encapsulated by exosomes. Exosome-mediated communication involves the release of exosomes from tumor or stromal cells and the uptake by nearby or remote recipient cells. The bioactive cargoes contained within these exosomes exert profound effects on the recipient cells, resulting in significant modifications in the tumor microenvironment (TME) and distinct alterations in gastrointestinal tumor behaviors. Due to the feasibility of isolating exosomes from various bodily fluids, exosomal ncRNAs have shown great potential as liquid biopsy-based indicators for different gastrointestinal cancers, using blood, ascites, saliva, or bile samples. Moreover, exosomes are increasingly recognized as natural delivery vehicles for ncRNA-based therapeutic interventions. In this review, we elucidate the processes of ncRNA-enriched exosome biogenesis and uptake, examine the regulatory and functional roles of exosomal ncRNA-mediated intercellular crosstalk in gastrointestinal TME and tumor behaviors, and explore their potential clinical utility in diagnostics, prognostics, and therapeutics.
Collapse
Affiliation(s)
- Bihan Xia
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Yuzhi Liu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Jin Wang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Qing Lu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Xiuhe Lv
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China
| | - Kai Deng
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China.
| | - Jinlin Yang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China; Sichuan University-University of Oxford Huaxi Joint Centre for Gastrointestinal Cancer, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan Province 610041, China.
| |
Collapse
|
42
|
Zhu Y, Li H, Huang Y, Fu W, Wang S, Sun N, Dong D, Tian J, Peng Y. CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center. Pediatr Res 2023; 94:1104-1110. [PMID: 36959318 DOI: 10.1038/s41390-023-02553-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/21/2022] [Accepted: 01/05/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Deep learning (DL) is more and more widely used in children's medical treatment. In this study, we have developed a computed tomography (CT)-based DL model for identifying undiagnosed non-Wilms tumors (nWTs) from pediatric renal tumors. METHODS This study collected and analyzed the preoperative clinical data and CT images of pediatric renal tumor patients diagnosed by our center from 2008 to 2020, and established a DL model to identify nWTs noninvasively. RESULTS A total of 364 children who had been confirmed by histopathology with renal tumors from our center were enrolled, including 269 Wilms tumors (WTs) and 95 nWTs. For DL model development, all cases were randomly allocated to training set (218 cases), validation set (73 cases), and test set (73 cases). In the test set, the DL model achieved area under the curve of 0.831 (95% CI: 0.712-0.951) in discriminating WTs from nWTs, with the accuracy, sensitivity, and specificity of 0.781, 0.563, and 0.842, respectively. The sensitivity of our model was higher than a radiologist with 15 years of experience. CONCLUSIONS We presented a DL model for identifying undiagnosed nWTs from pediatric renal tumors, with the potential to improve the image-based diagnosis. IMPACT Deep learning model was used for the first time to identify pediatric renal tumors in this study. Deep learning model can identify non-Wilms tumors from pediatric renal tumors. Deep learning model based on computed tomography images can improve tumor diagnosis rate.
Collapse
Affiliation(s)
- Yupeng Zhu
- Department of Radiology, MOE Key Laboratory of Major Diseases in Children, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
- Department of Radiology, Peking University Third Hospital, Beijing, 100191, China
| | - Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yangyue Huang
- Department of Pediatric Urology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Wangxing Fu
- Department of Radiology, MOE Key Laboratory of Major Diseases in Children, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Siwen Wang
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ning Sun
- Department of Pediatric Urology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.
- CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China.
- Zhuhai Precision Medical Center, Zhuhai People's Hospital (affiliated with Jinan University), Zhuhai, 519000, China.
| | - Yun Peng
- Department of Radiology, MOE Key Laboratory of Major Diseases in Children, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
| |
Collapse
|
43
|
Ruiqing L, Jing Y, Shunli L, Jia K, Zhibo W, Hongping Z, Keyu R, Xiaoming Z, Zhiming W, Weiming Z, Tianye N, Yun L. A Novel Radiomics Model Integrating Luminal and Mesenteric Features to Predict Mucosal Activity and Surgery Risk in Crohn's Disease Patients: A Multicenter Study. Acad Radiol 2023; 30 Suppl 1:S207-S219. [PMID: 37149448 DOI: 10.1016/j.acra.2023.03.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/16/2023] [Accepted: 03/18/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND To investigate the feasibility of integrating radiomics and morphological features based on computed tomography enterography (CTE) for developing a noninvasive grading model for mucosal activity and surgery risk of Crohn's disease (CD) patients. METHODS A total of 167 patients from three centers were enrolled. Radiomics and image morphological features were extracted to quantify segmental and global simple endoscopic score for Crohn's disease (SES-CD). An image-fusion-based support vector machine (SVM) classifier was used for grading SES-CD and identifying moderate-to-severe SES-CD. The performance of the predictive model was assessed using the area under the receiver operating characteristic curve (AUC). A multiparametric model was developed to predict surgical progression in CD patients by combining sum-image scores and clinical data. RESULTS The AUC values of the multicategorical segmental SES-CD fusion radiomic model based on a combination of luminal and mesenteric radiomics were 0.828 and 0.709 in training and validation cohorts. The image fusion model integrating the fusion radiomics and morphological features could accurately distinguish bowel segments with moderate-to-severe SES-CD in both the training cohort (AUC = 0.847, 95% confidence interval (CI): 0.784-0.902) and the validation cohort (AUC = 0.896, 95% CI: 0.812-0.960). A predictive nomogram for interval surgery was developed based on multivariable cox analysis. CONCLUSIONS This study demonstrated the feasibility of integrating lumen and mesentery radiomic features to develop a promising noninvasive grading model for mucosal activity of CD. In combination with clinical data, the fusion-image score may yield an accurate prognostic model for time to surgery.
Collapse
Affiliation(s)
- Liu Ruiqing
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China
| | - Yang Jing
- Institute of Translational Medicine, Zhejiang University, Hangzhou, ZJ, China
| | - Liu Shunli
- Department of Radiology, The Affiliated Hospital of Qingdao University Qingdao, Qingdao, SD, China
| | - Ke Jia
- Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, GD, China
| | - Wang Zhibo
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China
| | - Zhu Hongping
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China
| | - Ren Keyu
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, SD, China
| | - Zhou Xiaoming
- Department of Radiology, The Affiliated Hospital of Qingdao University Qingdao, Qingdao, SD, China
| | - Wang Zhiming
- Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, JS, China
| | - Zhu Weiming
- Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, JS, China
| | - Niu Tianye
- Shenzhen Bay Laboratory, Shenzhen, GD, China
| | - Lu Yun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China.
| |
Collapse
|
44
|
Wu P, Jiang Y, Xing H, Song W, Cui X, Wu XL, Xu G. Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study. Phys Med Biol 2023; 68:175023. [PMID: 37524093 DOI: 10.1088/1361-6560/acec2d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/31/2023] [Indexed: 08/02/2023]
Abstract
Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.
Collapse
Affiliation(s)
- Peiyan Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Yan Jiang
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Hanshuo Xing
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Wenbo Song
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Xinwu Cui
- Department of Medical Ultrasound, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Xing Long Wu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| | - Guoping Xu
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China
| |
Collapse
|
45
|
Wang C, Zhao J, Sun J, Tian C, Dai Z, Liu J, Ji C, Liu D, Wang X, Li H, Sun Y. Intraoperative Pathological Evaluation of Suspicious Peritoneal Nodules for Surgical Decision-making in Gastric Cancer. J Gastrointest Surg 2023; 27:1545-1559. [PMID: 37059962 DOI: 10.1007/s11605-023-05671-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/21/2023] [Indexed: 04/16/2023]
Abstract
BACKGROUND When frozen pathological results of suspicious peritoneal nodules found in gastric cancer (GC) patients are negative or indeterminant, whether to perform gastrectomy will always be a dilemma for surgeons. This study aimed to facilitate intraoperative surgical decision-making based on frozen section (FS) results and clinicopathological characteristics. METHODS From January 2015 to July 2021, 318 GC patients were enrolled retrospectively. The correlations between frozen and paraffin pathology of peritoneal nodules were examined. Then, predictive factors of positive paraffin section (PS) results were identified, and a nomogram was constructed. The survival significance of gastrectomy was also explored. RESULTS Of 70 FS-negative patients, 59 (84.3%) had concordant negative PS results, while the PS results of 11 (15.7%) were positive. Forty-six (93.9%) and 3 (6.1%) of 49 patients with indeterminant FS results had positive and negative PS results, respectively. The PS results of 95 FS-positive patients were all positive. A nomogram for predicting positive PS results was developed based on Lauren type, nodule distribution, and CA125. Gastrectomy for FS-negative patients improved survival compared to no gastrectomy (HR 0.26, 95% CI 0.11-0.62; P = 0.0012). Survival benefits for gastrectomy vs. no gastrectomy were not demonstrated in patients with indeterminant (HR 0.74, 95% CI 0.27-2.01; P = 0.53) and positive (HR 0.87, 95% CI 0.43-1.74; P = 0.69) FS results. CONCLUSIONS Gastrectomy can be justified for the treatment of operable GC patients with negative frozen pathological results of peritoneal nodules. For patients with positive and indeterminant frozen pathological results, gastrectomy is not recommended unless it is performed as palliative surgery.
Collapse
Affiliation(s)
- Chen Wang
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Junjie Zhao
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Jie Sun
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chenyu Tian
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Zhiqiang Dai
- Department of General Surgery, Zhongshan Hospital, Fudan University (Xiamen Branch), 668 Jinhu Road, Huli District, Xiamen, 361015, Fujian Province, China
| | - Jingdong Liu
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chengbo Ji
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Dan Liu
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Xuefei Wang
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Haojie Li
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Yihong Sun
- Department of General Surgery, Gastric Cancer Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| |
Collapse
|
46
|
Guo Q, Sun Q, Bian X, Wang M, Dong H, Yin H, Dai X, Fan G, Chen G. Development and validation of a multiphase CT radiomics nomogram for the preoperative prediction of lymphovascular invasion in patients with gastric cancer. Clin Radiol 2023; 78:e552-e559. [PMID: 37117048 DOI: 10.1016/j.crad.2023.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/13/2023] [Accepted: 03/22/2023] [Indexed: 04/30/2023]
Abstract
AIM To develop a nomogram to predict lymphovascular invasion (LVI) in gastric cancer by integrating multiphase computed tomography (CT) radiomics and clinical risk factors. MATERIALS AND METHODS One hundred and seventy-two gastric cancer patients (121 training and 51 validation) with preoperative contrast-enhanced CT images and clinicopathological data were collected retrospectively. The clinical risk factors were selected by univariate and multivariate regression analysis. Radiomic features were extracted and selected from the arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images of each patient. Clinical risk factors, radiomic features, and integration of both were used to develop the clinical model, radiomic models, and nomogram, respectively. RESULTS Radiomic features from AP (n=6), VP (n=6), DP (n=7) CT images and three selected clinical risk factors were used for model development. The nomogram showed better performance than the AP, VP, DP, and clinical models in the training and validation datasets, providing areas under the curves (AUCs) of 0.890 (95% CI: 0.820-0.940) and 0.885 (95% CI:0.765-0.957), respectively. All models indicated good calibration, and decision curve analysis proved that the net benefit of the nomogram was superior to that of the clinical and radiomic models throughout the vast majority of the threshold probabilities. CONCLUSIONS The nomogram integrating multiphase CT radiomics and clinical risk factors showed favourable performance in predicting LVI of gastric cancer, which may benefit clinical practice.
Collapse
Affiliation(s)
- Q Guo
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - Q Sun
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - X Bian
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - M Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - H Dong
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - H Yin
- Institute of Advanced Research, Beijing Infervision Technology Co., Ltd, Beijing, China
| | - X Dai
- Department of Pathology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - G Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China
| | - G Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, Jiangsu, 215004, China.
| |
Collapse
|
47
|
Zhang QW, Yang PP, Gao YJY, Li ZH, Yuan Y, Li SJ, Duan SF, Shao CW, Hao Q, Lu Y, Chen Q, Shen F. Assessing synchronous ovarian metastasis in gastric cancer patients using a clinical-radiomics nomogram based on baseline abdominal contrast-enhanced CT: a two-center study. Cancer Imaging 2023; 23:71. [PMID: 37488597 PMCID: PMC10367237 DOI: 10.1186/s40644-023-00584-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/09/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND To build and validate a radiomics nomogram based on preoperative CT scans and clinical data for detecting synchronous ovarian metastasis (SOM) in female gastric cancer (GC) cases. METHODS Pathologically confirmed GC cases in 2 cohorts were retrospectively enrolled. All cases had presurgical abdominal contrast-enhanced CT and pelvis contrast-enhanced MRI and pathological examinations for any suspicious ovarian lesions detected by MRI. Cohort 1 cases (n = 101) were included as the training set. Radiomics features were obtained to develop a radscore. A nomogram combining the radscore and clinical factors was built to detect SOM. The bootstrap method was carried out in cohort 1 as internal validation. External validation was carried out in cohort 2 (n = 46). Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and the confusion matrix were utilized to assess the performances of the radscore, nomogram and subjective evaluation model. RESULTS The nomogram, which combined age and the radscore, displayed a higher AUC than the radscore and subjective evaluation (0.910 vs 0.827 vs 0.773) in the training cohort. In the external validation cohort, the nomogram also had a higher AUC than the radscore and subjective evaluation (0.850 vs 0.790 vs 0.675). DCA and the confusion matrix confirmed the nomogram was superior to the radscore in both cohorts. CONCLUSIONS This pilot study showed that a nomogram model combining the radscore and clinical characteristics is useful in detecting SOM in female GC cases. It may be applied to improve clinical treatment and is superior to subjective evaluation or the radscore alone.
Collapse
Affiliation(s)
- Qian-Wen Zhang
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Pan-Pan Yang
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Yong-Jun-Yi Gao
- Department of Emergency, the Eighth Medical Center of Chinese, PLA General Hospital, 17 Heishanhu Rd, Haidian District, Beijing, 100091, China
| | - Zhi-Hui Li
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuan Yuan
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Si-Jie Li
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Shao-Feng Duan
- GE Healthcare China, Pudong New Town, No.1 Huatuo Road, Shanghai, 210000, China
| | - Cheng-Wei Shao
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Qiang Hao
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China
| | - Yong Lu
- Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qi Chen
- Department of Health Statistics, The Navy Medical University, Shanghai, 200433, China.
| | - Fu Shen
- Department of Radiology, Changhai Hospital, The Navy Medical University, 168 Changhai Road, Shanghai, 200433, China.
| |
Collapse
|
48
|
Liu Z, Tian H, Zhu Z. Application of Circulating Tumor Cells and Interleukin-6 in Preoperative Prediction of Peritoneal Metastasis of Advanced Gastric Cancer. J Inflamm Res 2023; 16:3033-3047. [PMID: 37497064 PMCID: PMC10366674 DOI: 10.2147/jir.s414786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/16/2023] [Indexed: 07/28/2023] Open
Abstract
Background The purpose of this study was to explore the clinical significance of circulating tumor cells (CTCs) and cytokines in peripheral blood in preoperative prediction of peritoneal metastasis (PM) in advanced gastric cancer (AGC). Methods The clinicopathological characteristics of 282 patients with AGC were retrospectively analyzed. The patients were divided into training and validation groups according to the time of receiving treatment. We used univariate analysis and multivariate logistic regression analysis to screen out the independent risk factors of PM in AGC. Then, we incorporated independent risk factors into the nomogram, and evaluated the discriminative ability. Results The levels of CTCs and interleukin-6 (IL-6) of AGC patients with PM were higher than those without PM (P<0.05). Moreover, the levels of CTCs and IL-6 in the occult peritoneal metastasis (OPM) group and the CT-positive PM group were higher than those in the negative PM (P<0.05). Multivariate logistic regression analysis showed that IL-6 > 12.22 pg/mL, CTCs > 4/5mL, CA724 > 6 IU/mL, CA125 > 35 U/mL and tumor size > 5 cm were independent risk factors for PM of AGC. The area under the ROC curve of the nomogram were 0.898 and 0.926 in the training and validation sets, respectively. The clinical decision curve showed that the nomogram had good clinical utility. Conclusion CTCs and IL-6 in peripheral blood are promising biomarkers for predicting the risk of PM in AGC. The nomogram constructed from five risk factors can effectively assess the risk of PM in AGC patients individually.
Collapse
Affiliation(s)
- Zitao Liu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Huakai Tian
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Zhengming Zhu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| |
Collapse
|
49
|
Liu Y. Comparison of Magnetic Resonance Imaging-Based Radiomics Features with Nomogram for Prediction of Prostate Cancer Invasion. Int J Gen Med 2023; 16:3043-3051. [PMID: 37485455 PMCID: PMC10361087 DOI: 10.2147/ijgm.s419039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
Objective To explore the value of the magnetic resonance imaging (MRI) radiomics model in predicting prostate cancer (PCa) invasion. Methods Clinical data of 86 pathologically confirmed PCa patients in our hospital were collected, including 44 cases in the invasive group and 42 cases in the non-invasive group. All patients underwent MRI examinations, and the same parameters were used. The lesion area was manually delineated and the radiomics features were extracted from T2WI. The radiomics signature based on LASSO regression was established. Besides, logistic regression was used to identify independent clinical predictors, and a combined model incorporating the radiomics signature and independent clinical risk factor was constructed. Finally, the receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) was performed to compare the prediction efficiency and clinical benefit of each model. Results A total of 867 radiomics features were obtained, and six of them were incorporated into the radiomics model. Multivariate logistic regression analysis exhibited the Gleason score as an independent clinical risk factor for PCa invasion. ROC results showed that the performance of the radiomics model was comparable to that of the clinical-radiomics model in predicting PCa invasion, and it was better than that of the single Gleason score. DCA also confirmed the considerable clinical application value of the radiomics and the clinical-radiomics models. Conclusion As a simple, non-invasive, and efficient method, the radiomics model has important predictive value for PCa invasion.
Collapse
Affiliation(s)
- Yang Liu
- X-Ray Department, The No.1 People’s Hospital of Huzhou, Huzhou, Zhejiang, 313000, People’s Republic of China
| |
Collapse
|
50
|
Wu H, Ding P, Wu J, Sun C, Guo H, Chen S, Lowe S, Yang P, Tian Y, Liu Y, Zhao Q. A New Online Dynamic Nomogram: Construction and Validation of a Predictive Model for Distant Metastasis Risk and Prognosis in Patients with Gastrointestinal Stromal Tumors. J Gastrointest Surg 2023; 27:1429-1444. [PMID: 37231240 DOI: 10.1007/s11605-023-05706-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/28/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Gastrointestinal stromal tumor (GIST) is the most common sarcoma of the digestive tract, among which patients with distant metastases tend to have a poor prognosis. This study aimed to develop a model for predicting distant metastasis in GIST patients and to develop two models for monitoring overall survival (OS) and cancer-specific survival (CSS) in GIST patients with metastasis. This would allow us to develop an optimal, individualized treatment strategy. METHODS We reviewed demographic and clinicopathological characteristics data from 2010 to 2017 of patients diagnosed with GIST in the Surveillance, Epidemiology, and End Results (SEER) database. The data of the external validation group was reviewed from the Forth Hospital of Hebei Medical University. Univariate and multivariate logistic regression analyses were used to confirm the independent risk factors for distant metastasis in the GIST patients, and univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors for OS and CSS in the GIST patients with distant metastasis. Subsequently, three web-based novel nomograms were developed, which were evaluated by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS Of the 3639 patients who met the inclusion criteria, 418 (11.4%) had distant metastases. The risk factors for distant metastasis in GIST patients included sex, primary site, grade, N stage, tumor size, and mitotic count. For OS, the independent prognosis factors for GIST patients with metastasis included age, race, marital, primary site, chemotherapy, mitotic count, and metastasis at the lung, and for CSS, age, race, marital, primary site, and metastasis at the lung were the independent prognosis factors. Three web-based nomograms were constructed based on these independent factors, respectively. The ROC curves, calibration curves, and DCA were performed in the training, testing, and validation sets which confirmed the high accuracy and strong clinical practice power for the nomograms. CONCLUSION Population-based nomograms can help clinicians predict the occurrence and prognosis of distant metastases in patients with GIST, which may be helpful for clinicians to formulate clinical management and appropriate treatment strategies.
Collapse
Affiliation(s)
- Haotian Wu
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Ping'an Ding
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Jiaxiang Wu
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Chenyu Sun
- AMITA Health Saint Joseph Hospital Chicago, 2900 N. Lake Shore Drive, Chicago, IL, 60657, USA
| | - Honghai Guo
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Shuya Chen
- Newham University Hospital, Glen Road, Plaistow, E13 8SL, London, UK
| | - Scott Lowe
- College of Osteopathic Medicine, Kansas City University, 1750 Independence Ave, Kansas City, MO, 64106, USA
| | - Peigang Yang
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Yuan Tian
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Yang Liu
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China
| | - Qun Zhao
- The Third Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
- Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China.
| |
Collapse
|