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Zhang HM, Wang Y, Huang ZX, Liu YX, Liu L, Bao YG, Cai X, Wu T, Xu Q, Zhu XL, Yin HK, Zhang HL, Yuan F, Song B. Preoperative CT-based radiomics model for predicting muscle invasion in patients with upper tract urothelial carcinoma below T3 stage. Abdom Radiol (NY) 2025:10.1007/s00261-025-04979-9. [PMID: 40382482 DOI: 10.1007/s00261-025-04979-9] [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: 03/23/2025] [Revised: 04/26/2025] [Accepted: 04/29/2025] [Indexed: 05/20/2025]
Abstract
PURPOSE To development of a preoperative CT-based radiomics model for predicting muscle invasion in patients with upper tract urothelial carcinoma below T3 stage. METHODS 163 consecutive patients who underwent radical nephroureterectomy for stage pT1-2 UTUC were retrospectively enrolled two medical centers (116 patients in training data and 47 patients in external validation data). Lesion segmentation, extraction and selection of radiomic features on pre-surgical CT urography, development and validation of predictive models were performed. Risk stratification of UTUC was evaluated. The diagnostic performance of the radiomics model and risk stratification was analyzed. Reference standard was histopathological analysis. RESULTS Among 163 patients (mean age, 52 years ± 9 [standard deviation], 97 men), 61.5% had pT2 grade tumors. 1165 features with intraclass coefficients > 0.75 were retained for least absolute shrinkage and selection operator (LASSO) regression. Nine radiomic features with non-zero coefficients on LASSO regression were selected from the training dataset and used for constructing the radiomics model. Good discrimination capability of the predictive model was observed, as AUCs were 0.859 (95% CI, 0.782-0.917) in the training dataset and 0.821 (95% CI, 0.682-0.918) in the validation dataset, respectively. Based on judgement by the model, When the tumor length diameter > 3 cm, combining ureteroscopy biopsy would improve sensitivity and NPV to 0.86 (95% CI, 0.776-0.922), 0.81 (95% CI, 0.714-0.903). CONCLUSION The preoperative radiomics model showed promising diagnostic performance in predicting UTUC muscle invasion. This could help patients receive more accurate risk classification, especially help patients avoiding radical nephroureterectomy.
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Affiliation(s)
- Han-Mei Zhang
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Yi Wang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zi-Xing Huang
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Yu-Xi Liu
- Department of Radiology, Sichuan University West China Tianfu Hospital, Chengdu, China
| | - Li Liu
- Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China
| | - Yi-Ge Bao
- Department of Urology, Sichuan University West China Hospital, Chengdu, China
| | - Xiang Cai
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Tao Wu
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Qian Xu
- Department of Urology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiang-Lan Zhu
- Department of Pathology, Sichuan University West China Hospital, Chengdu, China
| | - Hong-Kun Yin
- Infervision Medical Technology Co., Ltd, Beijing, China
| | | | - Fang Yuan
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China.
| | - Bin Song
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China.
- Sanya People's Hospital, Sanya, China.
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Shiota M, Nemoto S, Ikegami R, Tanegashima T, Blas L, Miyake H, Takahashi M, Oya M, Tsuchiya N, Masumori N, Kobayashi K, Obara W, Shinohara N, Fujimoto K, Nozawa M, Ohba K, Ohyama C, Hashine K, Akamatsu S, Motoshima T, Mita K, Gotoh M, Tatarano S, Fujisawa M, Tomita Y, Mukai S, Ito K, Eto M. Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study. JCO Clin Cancer Inform 2025; 9:e2400167. [PMID: 40279530 DOI: 10.1200/cci-24-00167] [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: 07/07/2024] [Revised: 12/10/2024] [Accepted: 02/24/2025] [Indexed: 04/27/2025] Open
Abstract
PURPOSE Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody treatment for advanced RCC by integrating genetic and clinical data using machine learning (ML). METHODS Clinical and single-nucleotide polymorphism (SNP) data obtained in the SNPs in nivolumab PD-1 inhibitor for RCC study, which enrolled Japanese patients treated with nivolumab monotherapy for advanced clear cell RCC, were used. A point-wise linear (PWL) algorithm, logistic regression with elastic-net regularization, and eXtreme Gradient Boosting were used in this study. AUC values for objective response and C-indices for progression-free survival (PFS) were calculated to evaluate the utility of the models. RESULTS Among the three ML algorithms, the AUC values to predict objective response were highest for the PWL algorithm among all the data sets. Three predictive models (clinical model, small SNP model, and large SNP model) were created by the PWL algorithm using the clinical data alone and using eight and 49 SNPs in addition to the clinical data. C-indices for PFS by the clinical model, small SNP model, and large SNP model were 0.522, 0.600, and 0.635, respectively. CONCLUSION The results demonstrated that the SNP models created by ML produced excellent predictions of tumor response to nivolumab monotherapy for advanced clear cell RCC and will be helpful in treatment decisions.
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Affiliation(s)
- Masaki Shiota
- Department of Urology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shota Nemoto
- Industrial & Digital Business Unit, Hitachi, Ltd, Tokyo, Japan
| | - Ryo Ikegami
- Industrial & Digital Business Unit, Hitachi, Ltd, Tokyo, Japan
| | - Tokiyoshi Tanegashima
- Department of Urology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Leandro Blas
- Department of Urology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hideaki Miyake
- Department of Urology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Masayuki Takahashi
- Department of Urology, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan
| | - Mototsugu Oya
- Department of Urology, Keio University School of Medicine, Tokyo, Japan
| | - Norihiko Tsuchiya
- Department of Urology, Faculty of Medicine, Yamagata University, Yamagata, Japan
| | - Naoya Masumori
- Department of Urology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Keita Kobayashi
- Department of Urology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Wataru Obara
- Department of Urology, Iwate Medical University School of Medicine, Iwate, Japan
| | - Nobuo Shinohara
- Department of Renal and Genitourinary Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | | | - Masahiro Nozawa
- Department of Urology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Kojiro Ohba
- Department of Urology, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan
| | - Chikara Ohyama
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Katsuyoshi Hashine
- Department of Urology, National Hospital Organization Shikoku Cancer Center, Ehime, Japan
| | - Shusuke Akamatsu
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Takanobu Motoshima
- Department of Urology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Koji Mita
- Department of Urology, Hiroshima City Asa Citizens Hospital, Hiroshima, Japan
| | - Momokazu Gotoh
- Department of Urology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shuichi Tatarano
- Department of Urology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan
| | - Masato Fujisawa
- Department of Urology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yoshihiko Tomita
- Department of Urology and Molecular Oncology, Graduate School of Medicine and Dental Sciences, Niigata University, Niigata, Japan
| | - Shoichiro Mukai
- Department of Urology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Keiichi Ito
- Department of Urology, National Defense Medical College, Saitama, Japan
| | - Masatoshi Eto
- Department of Urology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Han X, Guan J, Guo L, Jiao Q, Wang K, Hou F, Liu S, Yang S, Huang C, Cong W, Wang H. A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study. Cancer Imaging 2025; 25:27. [PMID: 40065444 PMCID: PMC11892212 DOI: 10.1186/s40644-025-00849-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2025] [Accepted: 02/25/2025] [Indexed: 03/14/2025] Open
Abstract
BACKGROUND To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa). METHODS This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models. We then compared the performance of the DL signature with the radiomics machine learning signature and selected the optimal signature to build a nomogram with the clinical model. Finally, the internal forecasting process of the DL signature was explained using Shapley additive explanation technology. RESULTS On the external validation set, the DL signature had an area under the curve of 0.857 (95% confidence interval: 0.745-0.932), and demonstrated superior prediction performance in comparison with the other models. SHAP expression images revealed that the prediction of PD-L1 expression status is mainly influenced by the tumor edge region, particularly the area close to the bladder wall. CONCLUSIONS The DL signature performed well in comparison with other models and proved to be a valuable, dependable, and interpretable tool for predicting programmed cell death ligand 1 expression status in patients with BCa.
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Affiliation(s)
- Xiaomeng Han
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China
| | - Jing Guan
- Department of Radiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, Hebei, 050000, China
| | - Li Guo
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong, 266071, China
| | - Qiyan Jiao
- Qingdao Medical College, Qingdao University, Qingdao, Shandong, 266071, China
| | - Kexin Wang
- Qingdao Medical College, Qingdao University, Qingdao, Shandong, 266071, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250022, China
| | - Chencui Huang
- Department of Research Collaboration, R&d Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, China
| | - Wenbin Cong
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
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Mai H, Li L, Xin X, Jiang Z, Tang Y, Huang J, Lei Y, Chen L, Dong T, Zhong X. Prediction of immunotherapy response in nasopharyngeal carcinoma: a comparative study using MRI-based radiomics signature and programmed cell death ligand 1 expression score. Eur Radiol 2025:10.1007/s00330-025-11350-5. [PMID: 39853331 DOI: 10.1007/s00330-025-11350-5] [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: 07/24/2024] [Revised: 10/29/2024] [Accepted: 12/10/2024] [Indexed: 01/26/2025]
Abstract
OBJECTIVES To compare an MRI-based radiomics signature with the programmed cell death ligand 1 (PD-L1) expression score for predicting immunotherapy response in nasopharyngeal carcinoma (NPC). METHODS Consecutive patients with NPC who received immunotherapy between January 2019 and June 2022 were divided into training (n = 111) and validation (n = 66) sets. Tumor radiomics features were extracted from pretreatment MR images. PD-L1 combined positive score (CPS) was calculated using immunohistochemistry. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and radiomics signature construction. Receiver operating characteristic (ROC) curve analysis was performed to assess prediction performance. RESULTS A total of eleven radiomics features with the greatest discrimination capability were identified by the LASSO algorithm to construct the radiomics signature. In predicting patients with objective response to immunotherapy, radiomics score (Rd-score) yielded a significantly higher area under the ROC curve than that of CPS in both the training (0.790 vs. 0.645, p = 0.025) and the validation (0.735 vs. 0.608, p = 0.038) sets. Multivariate analysis identified the Rd-score as an independent influencing factor in predicting immunotherapy response (odds ratio = 19.963, p < 0.001). Kaplan-Meier analysis indicated that patients with Rd-score ≥ 0.5 showed longer progression-free survival than patients with Rd-score < 0.5 (log-rank p < 0.01). CONCLUSION An MRI-based radiomics signature demonstrated greater efficacy than the PD-L1 expression score in predicting immunotherapy response in patients with NPC. KEY POINTS Question How does an MRI-based radiomics signature compare with the programmed cell death ligand 1 expression score for predicting immunotherapy response in nasopharyngeal carcinoma? Findings The MRI-based radiomics signature demonstrated superior predictive value compared with programmed cell death ligand 1 expression score in identifying immunotherapy responders. Clinical relevance MRI-based radiomics are a promising novel noninvasive tool for predicting immunotherapy outcomes in nasopharyngeal carcinoma.
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Affiliation(s)
- Hui Mai
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Li Li
- Department of Otolaryngology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Xin Xin
- Department of Medical Imaging, Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China
| | - Zhike Jiang
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Yongfang Tang
- Department of Medical Imaging, Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China
| | - Jie Huang
- Department of Medical Imaging, Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China
| | - Yanxing Lei
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Lianzhi Chen
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
| | - Tianfa Dong
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
| | - Xi Zhong
- Department of Medical Imaging, Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China.
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Zhang J, Wang Q, Guo TH, Gao W, Yu YM, Wang RF, Yu HL, Chen JJ, Sun LL, Zhang BY, Wang HJ. Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer. World J Gastrointest Oncol 2024; 16:4115-4128. [DOI: 10.4251/wjgo.v16.i10.4115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 08/18/2024] [Accepted: 08/28/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND Neoadjuvant immunochemotherapy (nICT) has emerged as a popular treatment approach for advanced gastric cancer (AGC) in clinical practice worldwide. However, the response of AGC patients to nICT displays significant heterogeneity, and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes.
AIM To establish a radiomic model to predict the response of AGC patients to nICT.
METHODS Patients with AGC who received nICT (n = 60) were randomly assigned to a training cohort (n = 42) or a test cohort (n = 18). Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT. An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature. The performance of all the models was assessed through receiver operating characteristic curve analysis, decision curve analysis (DCA) and the Hosmer-Lemeshow goodness-of-fit test.
RESULTS The radiomic nomogram could accurately predict the response of AGC patients to nICT. In the test cohort, the area under curve was 0.893, with a 95% confidence interval of 0.803-0.991. DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models.
CONCLUSION A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC. This tool can assist clinicians in treatment-related decision-making.
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Affiliation(s)
- Jun Zhang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Qi Wang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Tian-Hui Guo
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Wen Gao
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Yi-Miao Yu
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Rui-Feng Wang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Hua-Long Yu
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Jing-Jing Chen
- Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Ling-Ling Sun
- Department of Pathology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Bi-Yuan Zhang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
| | - Hai-Ji Wang
- Department of Radiation Oncology, Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong Province, China
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Ma Q, Ren J, Wang R, Yuan Y, Tao X. Predicting response to immunotherapy in oral squamous cell carcinoma via a CT-based radiomics model. BMC Med Imaging 2024; 24:266. [PMID: 39375583 PMCID: PMC11460018 DOI: 10.1186/s12880-024-01444-9] [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: 06/17/2024] [Accepted: 09/27/2024] [Indexed: 10/09/2024] Open
Abstract
BACKGROUND To investigate whether radiomics models derived from pretreatment CT could help to predict response to immunotherapy in oral squamous cell carcinoma (OSCC). METHODS Retrospectively, a total of 40 patients with measurable OSCC were included. The patients were divided into responder group and non-responder group according to the comparison of pre-treatment and post-treatment CT findings. Radiomics features were extracted from pre-treatment CT images, and optimal features were selected by univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis. Neural network, support vector machine, random forest and logistic regression models were used to predict response to immunotherapy in OSCC, and leave-one-out cross validation was employed to assess the performance of the classifiers. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated to quantify the predictive efficacy. RESULTS A total of 7 features were selected to build models upon machine learning methods. By comparing different machine learning based models, the neural network model achieved the best predictive ability, with an AUC of 0.864, an accuracy of 82.5%, a sensitivity of 82.5%, and a specificity of 82.5%. CONCLUSIONS The pretreatment CT-based radiomics model showed good performance in predicting response to immunotherapy in OSCC. Pretreatment CT-based radiomics model might provide an alternative approach for the selection of patients who benefit from immunotherapy.
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Affiliation(s)
- Qifan Ma
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, 200010, China
| | - Jiliang Ren
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, 200010, China
| | - Rui Wang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, 200010, China
| | - Ying Yuan
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, 200010, China.
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639 Zhizaoju Road, Shanghai, 200010, China.
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Peng J, Tang Z, Li T, Pan X, Feng L, Long L. Contrast-enhanced computed tomography-based radiomics nomogram for predicting HER2 status in urothelial bladder carcinoma. Front Oncol 2024; 14:1427122. [PMID: 39206159 PMCID: PMC11349509 DOI: 10.3389/fonc.2024.1427122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Objective To evaluate the performance of a clinical-radiomics model based on contrast-enhanced computed tomography (CE-CT) in assessing human epidermal growth factor receptor 2 (HER2) status in urothelial bladder carcinoma (UBC). Methods From January 2022 to December 2023, 124 patients with UBC were classified into the training (n=100) and test (n=24) sets. CE-CT scans were performed on the patients. Univariate and multivariate analyses were conducted to identify independent predictors of HER2 status in patients with UBC. We employed eight machine learning algorithms to establish radiomic models. A clinical-radiomics model was developed by integrating radiomic signatures and clinical features. Receiver operating characteristic curves and decision curve analysis (DCA) were generated to evaluate and validate the predictive capabilities of the models. Results Among the eight classifiers, the random forest radiomics model based on CE-CT demonstrated the highest efficacy in predicting HER2 status, with area under the curve (AUC) values of 0.880 (95% CI: 0.813-0.946) and 0.814 (95% CI: 0.642-0.986) in the training and test sets, respectively. In the training set, the clinical-radiomics model achieved an AUC of 0.935, an accuracy of 0.870, a sensitivity of 0.881, and a specificity of 0.854. In the test set, the clinical-radiomics model achieved an AUC of 0.857, an accuracy of 0.760, a sensitivity of 0.643, and a specificity of 0.900. DCA analysis indicated that the clinical-radiomics model provided good clinical benefit. Conclusion The radiomics nomogram demonstrates good diagnostic performance in predicting HER2 expression in patients with UBC.
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Affiliation(s)
- Jiao Peng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Department of Radiology, Liuzhou Workers Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China
| | - Zhen Tang
- Department of Urology, Liuzhou Workers Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China
| | - Tao Li
- Department of Radiology, Liuzhou Workers Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China
| | - Xiaoyu Pan
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Lijuan Feng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Zhang S, Li K, Sun Y, Wan Y, Ao Y, Zhong Y, Liang M, Wang L, Chen X, Pei X, Hu Y, Chen D, Li M, Shan H. Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study. Int J Radiat Oncol Biol Phys 2024; 119:1590-1600. [PMID: 38432286 DOI: 10.1016/j.ijrobp.2024.02.035] [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: 11/02/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.
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Affiliation(s)
- Shuaitong Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yuchen Sun
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Yun Wan
- Department of Radiology, Xinyi City People's Hospital, Xinyi, Guangdong, China
| | - Yong Ao
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yinghua Zhong
- Department of Radiology, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong, China
| | - Mingzhu Liang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Lizhu Wang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Xiaofeng Pei
- Department of Radiation Oncology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yi Hu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Man Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
| | - Hong Shan
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Department of Interventional Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
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9
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Holder AM, Dedeilia A, Sierra-Davidson K, Cohen S, Liu D, Parikh A, Boland GM. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 2024; 24:498-512. [PMID: 38867074 DOI: 10.1038/s41568-024-00705-7] [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] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
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Affiliation(s)
- Ashley M Holder
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sonia Cohen
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Aparna Parikh
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
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10
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Qin Y, Huo M, Liu X, Li SC. Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy. Front Immunol 2024; 15:1368749. [PMID: 38524135 PMCID: PMC10957591 DOI: 10.3389/fimmu.2024.1368749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.
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Affiliation(s)
- Yurong Qin
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Miaozhe Huo
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
| | - Xingwu Liu
- School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong, Kowloon, China
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, Guangdong, China
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11
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Ligero M, Gielen B, Navarro V, Cresta Morgado P, Prior O, Dienstmann R, Nuciforo P, Trebeschi S, Beets-Tan R, Sala E, Garralda E, Perez-Lopez R. A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking? NPJ Precis Oncol 2024; 8:42. [PMID: 38383736 PMCID: PMC10881558 DOI: 10.1038/s41698-024-00534-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.
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Affiliation(s)
- Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Bente Gielen
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Victor Navarro
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Pablo Cresta Morgado
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
- Prostate Cancer Translational Research Group, Institute of Oncology (VHIO), Vall d'Hebron University Hospital, Barcelona, Spain
| | - Olivia Prior
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rodrigo Dienstmann
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Regina Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Evis Sala
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy
| | - Elena Garralda
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.
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12
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Wu Q, Wang J, Sun Z, Xiao L, Ying W, Shi J. Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer Using Multi-View Adaptive Weighted Graph Convolutional Networks. IEEE J Biomed Health Inform 2023; 27:5564-5575. [PMID: 37643107 DOI: 10.1109/jbhi.2023.3309840] [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: 08/31/2023]
Abstract
Immunotherapy is an effective way to treat non-small cell lung cancer (NSCLC). The efficacy of immunotherapy differs from person to person and may cause side effects, making it important to predict the efficacy of immunotherapy before surgery. Radiomics based on machine learning has been successfully used to predict the efficacy of NSCLC immunotherapy. However, most studies only considered the radiomic features of the individual patient, ignoring the inter-patient correlations. Besides, they usually concatenated different features as the input of a single-view model, failing to consider the complex correlation among features of multiple types. To this end, we propose a multi-view adaptive weighted graph convolutional network (MVAW-GCN) for the prediction of NSCLC immunotherapy efficacy. Specifically, we group the radiomic features into several views according to the type of the fitered images they extracted from. We construct a graph in each view based on the radiomic features and phenotypic information. An attention mechanism is introduced to automatically assign weights to each view. Considering the view-shared and view-specific knowledge of radiomic features, we propose separable graph convolution that decomposes the output of the last convolution layer into two components, i.e., the view-shared and view-specific outputs. We maximize the consistency and enhance the diversity among different views in the learning procedure. The proposed MVAW-GCN is evaluated on 107 NSCLC patients, including 52 patients with valid efficacy and 55 patients with invalid efficacy. Our method achieved an accuracy of 77.27% and an area under the curve (AUC) of 0.7780, indicating its effectiveness in NSCLC immunotherapy efficacy prediction.
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13
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Dai Y, Cheng Y, Zhou Z, Li Z, Luo Y, Qiu H. A contrast-enhanced CT-based whole-spleen radiomics signature for early prediction of oxaliplatin-related thrombocytopenia in patients with gastrointestinal malignancies: a retrospective study. PeerJ 2023; 11:e16230. [PMID: 37849829 PMCID: PMC10578303 DOI: 10.7717/peerj.16230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 09/12/2023] [Indexed: 10/19/2023] Open
Abstract
Background Thrombocytopenia is a common adverse event of oxaliplatin-based chemotherapy. Grade 2 or higher oxaliplatin-related thrombocytopenia may result in dose reduction, discontinuation or delay initiation of chemotherapy and may adversely affect the therapeutic efficacy and even overall survival of patients. Early recognition of patients at risk of developing grade 2 or higher thrombocytopenia is critical. However, to date there is no well-established method to early identify patients at high risk. The aims of this study were to develop and validate a contrast-enhanced CT-based whole-spleen radiomics signature for early prediction of grade 2 or higher thrombocytopenia in patients with gastrointestinal malignancies treated with oxaliplatin-based chemotherapy and to explore the incremental value of combining the radiomics signature and conventional clinical factors for risk prediction. Methods A total of 119 patients with gastrointestinal malignancies receiving oxaliplatin-based chemotherapy from March 2017 to December 2020 were retrospectively included and randomly divided into a training cohort (n = 85) and a validation cohort (n = 34). Grade 2 or higher thrombocytopenia occurred in 26.1% of patients (22 and nine patients in the training and validation cohort, respectively) with a median time interval of 101 days from the start of chemotherapy. The whole-spleen radiomics features were extracted on the portal venous phase of the first follow-up CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select radiomics features and to build the radiomics signature for the prediction of grade 2 or higher thrombocytopenia. A clinical model that included clinical factors only and a clinical-radiomics model that incorporated clinical factors and radiomics signature were constructed. The performances of both models were evaluated and compared in the training, validation and the whole cohorts. Results The radiomics signature yielded favorable performance in predicting grade 2 or higher thrombocytopenia, with the area under the curve (AUC), sensitivity and specificity being 0.865, 81.8% and 84.1% in the training cohort and 0.747, 77.8% and 80.0% in the validation cohort. The AUCs of the clinical-radiomics model in the training and validation cohorts reached 0.913 (95% CI [0.720-0.935]) and 0.867 (95% CI [0.727-1.000]), greater than the AUCs of the clinical model. Integrated discrimination improvement (IDI) index showed that incorporating radiomic signature into conventional clinical factors significantly improved the predictive accuracy by 17.0% (95% CI [4.9%-29.1%], p = 0.006) in the whole cohort. Conclusions Contrast-enhanced CT-based whole-spleen radiomics signature might serve as an early predictor for grade 2 or higher thrombocytopenia during oxaliplatin-based chemotherapy in patients with gastrointestinal malignancies and provide incremental value over conventional clinical factors.
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Affiliation(s)
- Yuhong Dai
- Department of Oncology, Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiqi Cheng
- Department of Radiology, Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziling Zhou
- Department of Radiology, Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Luo
- Department of Radiology, Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Qiu
- Department of Oncology, Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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14
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Song H, Yang S, Yu B, Li N, Huang Y, Sun R, Wang B, Nie P, Hou F, Huang C, Zhang M, Wang H. CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study. Cancer Imaging 2023; 23:89. [PMID: 37723572 PMCID: PMC10507832 DOI: 10.1186/s40644-023-00609-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 09/10/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively. METHODS We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature. CONCLUSION The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.
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Affiliation(s)
- Hongzheng Song
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Boyang Yu
- Qingdao No.58 High School of Shandong Province, Qingdao, Shandong, China
| | - Na Li
- Department of Radiology, The People's Hospital of Zhangqiu Area, Jinan, Shandong, China
| | - Yonghua Huang
- Department of Radiology, The Puyang Oilfield General Hospital, Puyang, Henan, China
| | - Rui Sun
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Bo Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Pei Nie
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China.
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15
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Duwe G, Müller L, Ruckes C, Fischer ND, Frey LJ, Börner JH, Rölz N, Haack M, Sparwasser P, Jorg T, Neumann CCM, Tsaur I, Höfner T, Haferkamp A, Hahn F, Mager R, Brandt MP. Change in Splenic Volume as a Surrogate Marker for Immunotherapy Response in Patients with Advanced Urothelial and Renal Cell Carcinoma-Evaluation of a Novel Approach of Fully Automated Artificial Intelligence Based Splenic Segmentation. Biomedicines 2023; 11:2482. [PMID: 37760923 PMCID: PMC10526098 DOI: 10.3390/biomedicines11092482] [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: 07/08/2023] [Revised: 09/05/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND In the treatment of advanced urothelial (aUC) and renal cell carcinoma (aRCC), biomarkers such as PD-1 and PD-L1 are not robust prognostic markers for immunotherapy (IO) response. Previously, a significant association between IO and a change in splenic volume (SV) was described for several tumour entities. To the best of our knowledge, this study presents the first correlation of SV to IO in aUC and aRCC. METHODS All patients with aUC (05/2017-10/2021) and aRCC (01/2012-05/2022) treated with IO at our academic centre were included. SV was measured at baseline, 3 and 9 months after initiation of IO using an in-house developed convolutional neural network-based spleen segmentation method. Uni- and multivariate Cox regression models for overall survival (OS) and progression-free survival (PFS) were used. RESULTS In total, 35 patients with aUC and 30 patients with aRCC were included in the analysis. Lower SV at the three-month follow-up was significantly associated with improved OS in the aRCC group. CONCLUSIONS We describe a new, innovative artificial intelligence-based approach of a radiological surrogate marker for IO response in aUC and aRCC which presents a promising new predictive imaging marker. The data presented implicate improved OS with lower follow-up SV in patients with aRCC.
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Affiliation(s)
- Gregor Duwe
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Lukas Müller
- Department of Diagnostic and Interventional Radiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Christian Ruckes
- Interdisciplinary Center for Clinical Trials Mainz, University Medical Center, Johannes Gutenberg-University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Nikita Dhruva Fischer
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Lisa Johanna Frey
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Jan Hendrik Börner
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Niklas Rölz
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Maximilian Haack
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Peter Sparwasser
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Tobias Jorg
- Department of Diagnostic and Interventional Radiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Christopher C. M. Neumann
- Department of Hematology, Oncology and Tumor Immunology, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, 10117 Berlin, Germany
| | - Igor Tsaur
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Thomas Höfner
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
- Department of Urology, Ordensklinikum Linz Elisabethinen, Fadingerstraße 1, 4020 Linz, Austria
| | - Axel Haferkamp
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Rene Mager
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
| | - Maximilian Peter Brandt
- Department of Urology and Pediatric Urology, University Medical Center, Johannes-Gutenberg University Mainz, Langenbeckstrasse 1, 55131 Mainz, Germany
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16
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Tang L, Zhang Z, Fan J, Xu J, Xiong J, Tang L, Jiang Y, Zhang S, Zhang G, Luo W, Xu Y. Comprehensively analysis of immunophenotyping signature in triple-negative breast cancer patients based on machine learning. Front Pharmacol 2023; 14:1195864. [PMID: 37426809 PMCID: PMC10328722 DOI: 10.3389/fphar.2023.1195864] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023] Open
Abstract
Immunotherapy is a promising strategy for triple-negative breast cancer (TNBC) patients, however, the overall survival (OS) of 5-years is still not satisfactory. Hence, developing more valuable prognostic signature is urgently needed for clinical practice. This study established and verified an effective risk model based on machine learning methods through a series of publicly available datasets. Furthermore, the correlation between risk signature and chemotherapy drug sensitivity were also performed. The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that IL18R1, BTN3A1, CD160, CD226, IL12B, GNLY and PDCD1LG2 are key genes that may affect immune typing of TNBC patients. The risk signature plays a robust ability in prognosis prediction compared with other clinicopathological features in TNBC patients. In addition, the effect of our constructed risk model on immunotherapy response was superior to TIDE results. Finally, high-risk groups were more sensitive to MR-1220, GSK2110183 and temsirolimus, indicating that risk characteristics could predict drug sensitivity in TNBC patients to a certain extent. This study proposes an immunophenotype-based risk assessment model that provides a more accurate prognostic assessment tool for patients with TNBC and also predicts new potential compounds by performing machine learning algorithms.
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17
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Cellina M, Cè M, Rossini N, Cacioppa LM, Ascenti V, Carrafiello G, Floridi C. Computed Tomography Urography: State of the Art and Beyond. Tomography 2023; 9:909-930. [PMID: 37218935 PMCID: PMC10204399 DOI: 10.3390/tomography9030075] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/26/2023] [Accepted: 04/27/2023] [Indexed: 05/24/2023] Open
Abstract
Computed Tomography Urography (CTU) is a multiphase CT examination optimized for imaging kidneys, ureters, and bladder, complemented by post-contrast excretory phase imaging. Different protocols are available for contrast administration and image acquisition and timing, with different strengths and limits, mainly related to kidney enhancement, ureters distension and opacification, and radiation exposure. The availability of new reconstruction algorithms, such as iterative and deep-learning-based reconstruction has dramatically improved the image quality and reducing radiation exposure at the same time. Dual-Energy Computed Tomography also has an important role in this type of examination, with the possibility of renal stone characterization, the availability of synthetic unenhanced phases to reduce radiation dose, and the availability of iodine maps for a better interpretation of renal masses. We also describe the new artificial intelligence applications for CTU, focusing on radiomics to predict tumor grading and patients' outcome for a personalized therapeutic approach. In this narrative review, we provide a comprehensive overview of CTU from the traditional to the newest acquisition techniques and reconstruction algorithms, and the possibility of advanced imaging interpretation to provide an up-to-date guide for radiologists who want to better comprehend this technique.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Piazza Principessa Clotilde 3, 20121 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Nicolo’ Rossini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
| | - Laura Maria Cacioppa
- Division of Interventional Radiology, Department of Radiological Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Radiology Department, Policlinico di Milano Ospedale Maggiore|Fondazione IRCCS Ca’ Granda, Via Francesco Sforza 35, 20122 Milan, Italy
| | - Chiara Floridi
- Division of Interventional Radiology, Department of Radiological Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Division of Special and Pediatric Radiology, Department of Radiology, University Hospital “Umberto I-Lancisi-Salesi”, 60126 Ancona, Italy
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18
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Dai J, Wang H, Xu Y, Chen X, Tian R. Clinical application of AI-based PET images in oncological patients. Semin Cancer Biol 2023; 91:124-142. [PMID: 36906112 DOI: 10.1016/j.semcancer.2023.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.
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Affiliation(s)
- Jiaona Dai
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hui Wang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City 421001, China
| | - Xiyang Chen
- Division of Vascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
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19
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Xue C, Zhou Q, Xi H, Zhou J. Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment. Diagn Interv Imaging 2023; 104:113-122. [PMID: 36283933 DOI: 10.1016/j.diii.2022.10.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 12/24/2022]
Abstract
With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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20
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Li J, Chen Z, Chen Y, Zhao J, He M, Li X, Zhang L, Dong B, Zhang X, Tang L, Shen L. CT-based delta radiomics in predicting the prognosis of stage IV gastric cancer to immune checkpoint inhibitors. Front Oncol 2023; 12:1059874. [PMID: 36686828 PMCID: PMC9847891 DOI: 10.3389/fonc.2022.1059874] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 11/29/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction To explore the prognostic value of CT-based delta radiomics in predicting the prognosis of patients with stage IV gastric cancer treated with immune checkpoint inhibitors (ICI). Materials and methods Forty-two patients with stage IV gastric cancer, who had received ICI monotherapy, were enrolled in this retrospective study. Baseline and first follow-up CT scans were analyzed. Intratumoral and peritumoral regions of interest (ROI) were contoured, enabling the extraction of 192 features from each ROI. The intraclass correlation coefficients were used to select features with high stability. The least absolute shrinkage and selection operator was used to select features with high weights for predicting patient prognosis. Kaplan-Meier analysis and log-rank test were performed to explore the association between features and progression free survival (PFS). Cox regression analyses were used to identify predictors for PFS. The C-index was used to assess the prediction performance of features. Results Two radiomics features of ΔVintra_ZV and postVperi_Sphericity were identified from intratumoral and peritumoral regions, respectively. The Kaplan-Meier analysis revealed significant differences in PFS between patients with low and high feature value (ΔVintra_ZV: P=0.000; postVperi_Sphericity: P=0.012), and the multivariable cox analysis demonstrated that ΔVintra_ZV was independent predictor for PFS (HR, 1.911; 95% CI: 1.163-3.142; P=0.011), with C-index of 0.705. Conclusions Based on CT scans at baseline and first follow-up, the delta radiomics features could efficiently predict the PFS of gastric cancer patients treated with ICI therapy.
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Affiliation(s)
- Jiazheng Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Zifan Chen
- Center for Data Science, Peking University, Beijing, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Jie Zhao
- National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China
| | - Meng He
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiaoting Li
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Li Zhang
- Center for Data Science, Peking University, Beijing, China
| | - Bin Dong
- Beijing International Center for Mathematical Research (BICMR), Peking University, Beijing, China,*Correspondence: Bin Dong, ; Xiaotian Zhang, ; Lei Tang, ; Lin Shen,
| | - Xiaotian Zhang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China,*Correspondence: Bin Dong, ; Xiaotian Zhang, ; Lei Tang, ; Lin Shen,
| | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China,*Correspondence: Bin Dong, ; Xiaotian Zhang, ; Lei Tang, ; Lin Shen,
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China,*Correspondence: Bin Dong, ; Xiaotian Zhang, ; Lei Tang, ; Lin Shen,
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21
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Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
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Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
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22
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Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer. Eur Urol Focus 2023; 9:145-153. [PMID: 36115774 DOI: 10.1016/j.euf.2022.08.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/26/2022] [Accepted: 08/30/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited. OBJECTIVE To evaluate the performance of quantitative radiomics signatures for detection of LN metastases in BC. DESIGN, SETTING, AND PARTICIPANTS Of 1354 patients with BC who underwent radical cystectomy (RC) with lymphadenectomy who were screened, 391 with pathological nodal staging (pN0: n = 297; pN+: n = 94) were included and randomized into training (n = 274) and test (n = 117) cohorts. Pelvic LNs were segmented manually and automatically. A total of 1004 radiomics features were extracted from each LN and a machine learning model was trained to assess pN status using histopathology labels as the ground truth. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Radiologist assessment was compared to radiomics-based analysis using manual and automated LN segmentations for detection of LN metastases in BC. Statistical analysis was performed using the receiver operating characteristics curve method and evaluated in terms of sensitivity, specificity, and area under the curve (AUC). RESULTS AND LIMITATIONS In total, 1845 LNs were manually segmented. Automated segmentation correctly located 361/557 LNs in the test cohort. Manual and automatic masks achieved an AUC of 0.80 (95% confidence interval [CI] 0.69-0.91; p = 0.64) and 0.70 (95% CI: 0.58-0.82; p = 0.17), respectively, in the test cohort compared to radiologist assessment, with an AUC of 0.78 (95% CI 0.67-0.89). A combined model of a manually segmented radiomics signature and radiologist assessment reached an AUC of 0.81 (95% CI 0.71-0.92; p = 0.63). CONCLUSIONS A radiomics signature allowed discrimination of nodal status with high diagnostic accuracy. The model based on manual LN segmentation outperformed the fully automated approach. PATIENT SUMMARY For patients with bladder cancer, evaluation of computed tomography (CT) scans before surgery using a computer-based method for image analysis, called radiomics, may help in standardizing and improving the accuracy of assessment of lymph nodes. This could be a valuable tool for optimizing treatment options.
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23
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Can PD-L1 expression be predicted by contrast-enhanced CT in patients with gastric adenocarcinoma? a preliminary retrospective study. Abdom Radiol (NY) 2023; 48:220-228. [PMID: 36271155 PMCID: PMC9849168 DOI: 10.1007/s00261-022-03709-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/08/2022] [Accepted: 10/10/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND This study aimed to construct a computed tomography (CT) radiomics model to predict programmed cell death-ligand 1 (PD-L1) expression in gastric adenocarcinoma patients using radiomics features. METHODS A total of 169 patients with gastric adenocarcinoma were studied retrospectively and randomly divided into training and testing datasets. The clinical data of the patients were recorded. Radiomics features were extracted to construct a radiomics model. The random forest-based Boruta algorithm was used to screen the features of the training dataset. A receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model. RESULTS Four radiomics features were selected to construct a radiomics model. The radiomics signature showed good efficacy in predicting PD-L1 expression, with an area under the receiver operating characteristic curve (AUC) of 0.786 (p < 0.001), a sensitivity of 0.681, and a specificity of 0.826. The radiomics model achieved the greatest areas under the curve (AUCs) in the training dataset (AUC = 0.786) and testing dataset (AUC = 0.774). The calibration curves of the radiomics model showed great calibration performances outcomes in the training dataset and testing dataset. The net clinical benefit for the radiomics model was high. CONCLUSION CT radiomics has important value in predicting the expression of PD-L1 in patients with gastric adenocarcinoma.
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24
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Sun R, Lerousseau M, Briend-Diop J, Routier E, Roy S, Henry T, Ka K, Jiang R, Temar N, Carré A, Laville A, Hamaoui A, Laurent PA, Rouyar A, Robert C, Robert C, Deutsch E. Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-004867. [PMID: 36307149 PMCID: PMC9621183 DOI: 10.1136/jitc-2022-004867] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE While there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy treatments, radiomic approaches have shown promising results. The objectives of this study were to evaluate whether a previously validated radiomics signature of CD8 T-cells could predict progressions at a lesion level and whether the spatial heterogeneity of this radiomics score could be used at a patient level to assess the clinical response and survival of melanoma patients. METHODS Clinical data from patients with advanced melanoma treated in our center with immunotherapy were retrieved. Radiomic features were extracted and the CD8 radiomics signature was applied. A progressive lesion was defined by an increase in lesion size of 20% or more. Dispersion metrics of the radiomics signature were estimated to evaluate the impact of interlesion heterogeneity on patient's response. Fine-tuned cut-offs for predicting overall survival were evaluated after splitting data into training and test sets. RESULTS A total of 136 patients were included in this study, with 1120 segmented lesions at baseline, and 1052 lesions at first evaluation. A low CD8 radiomics score at baseline was associated with a significantly higher risk of lesion progression (AUC=0.55, p=0.0091), especially for lesions larger than >1 mL (AUC=0.59 overall, p=0.0035, with AUC=0.75, p=0.002 for subcutaneous lesions, AUC=0.68, p=0.01, for liver lesions and AUC=0.62, p=0.03 for nodes). The least infiltrated lesion according to the radiomics score of CD8 T-cells was positively associated with overall survival (training set HR=0.31, p=0.00062, test set HR=0.28, p=0.016), which remained significant in a multivariate analysis including clinical and biological variables. CONCLUSIONS These results confirm the predictive value at a lesion level of the biologically inspired CD8 radiomics score in melanoma patients treated with anti-PD1-based immunotherapy and may be interesting to assess the disease spatial heterogeneity to evaluate the patient prognosis with potential clinical implication such as tumor selection for focal ablative therapies.
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Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France,Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Marvin Lerousseau
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Jade Briend-Diop
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Emilie Routier
- Dermatology Unit, Department of Medicine, Gustave Roussy, Villejuif, France,Université Paris Saclay, Inserm U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Gustave Roussy, Villejuif, France
| | - Severine Roy
- Dermatology Unit, Department of Medicine, Gustave Roussy, Villejuif, France,Université Paris Saclay, Inserm U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Gustave Roussy, Villejuif, France
| | - Théophraste Henry
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France,Imaging Department, Gustave Roussy, Villejuif, France
| | - Kanta Ka
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Rui Jiang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Nawal Temar
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Alexandre Carré
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Anthony Hamaoui
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Pierre-Antoine Laurent
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Angela Rouyar
- Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France,Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
| | - Caroline Robert
- Dermatology Unit, Department of Medicine, Gustave Roussy, Villejuif, France,Université Paris Saclay, Inserm U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Gustave Roussy, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France,Université Paris Saclay, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique, Gustave Roussy, Villejuif, France
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25
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Woźnicki P, Laqua FC, Messmer K, Kunz WG, Stief C, Nörenberg D, Schreier A, Wójcik J, Ruebenthaler J, Ingrisch M, Ricke J, Buchner A, Schulz GB, Gresser E. Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy. Cancers (Basel) 2022; 14:4449. [PMID: 36139609 PMCID: PMC9497387 DOI: 10.3390/cancers14184449] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.
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Affiliation(s)
- Piotr Woźnicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg-Oberdürrbacher Str. 6, 97080 Würzburg, Germany
| | - Fabian Christopher Laqua
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg-Oberdürrbacher Str. 6, 97080 Würzburg, Germany
| | - Katharina Messmer
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Wolfgang Gerhard Kunz
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Christian Stief
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim-Theodor-Kutzer-Ufer 1–3, 68167 Mannheim, Germany
| | - Andrea Schreier
- Department of Otolaryngology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Jan Wójcik
- Faculty of Medicine, Medical University of Warsaw, Żwirki i Wigury 61, 02091 Warsaw, Poland
| | - Johannes Ruebenthaler
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Alexander Buchner
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Gerald Bastian Schulz
- Department of Urology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
| | - Eva Gresser
- Department of Radiology, University Hospital, LMU Munich, Munich-Marchioninistr. 15, 81377 Munich, Germany
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26
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Ter Maat LS, van Duin IAJ, Elias SG, van Diest PJ, Pluim JPW, Verhoeff JJC, de Jong PA, Leiner T, Veta M, Suijkerbuijk KPM. Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review. Eur J Cancer 2022; 175:60-76. [PMID: 36096039 DOI: 10.1016/j.ejca.2022.07.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. METHODS PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted. RESULTS In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist. CONCLUSIONS There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.
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Affiliation(s)
- Laurens S Ter Maat
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Isabella A J van Duin
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Josien P W Pluim
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Tim Leiner
- Utrecht University, Utrecht, the Netherlands; Department of Radiology, Mayo Clinical, Rochester, MN, USA
| | - Mitko Veta
- Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Karijn P M Suijkerbuijk
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands.
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Radiomics Models Based on Magnetic Resonance Imaging for Prediction of the Response to Bortezomib-Based Therapy in Patients with Multiple Myeloma. BIOMED RESEARCH INTERNATIONAL 2022; 2022:6911246. [PMID: 36105939 PMCID: PMC9467708 DOI: 10.1155/2022/6911246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 08/04/2022] [Accepted: 08/20/2022] [Indexed: 11/17/2022]
Abstract
Purpose. To identify significant radiomics features based on MRI and establish effective models for predicting the response to bortezomib-based regimens. Materials and Methods. In total, 95 MM patients treated with bortezomib-based therapy were enrolled, including 77 with bortezomib, cyclophosphamide, and dexamethasone (BCD) and 18 with bortezomib, lenalidomide, and dexamethasone (VRD). Based on T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-fs), radiomics features were extracted and then selected. The random forest (RF),
-nearest neighbor, support vector machine, logistic regression, decision tree, and Bayes models were built using the selected features. The predictive power of six models for response to BCD and VRD regimens were evaluated. The correlation between the selected features and progression-free survival (PFS) was also analyzed. Results. Four wavelet features were correlated with BCD treatment response. The six models all showed predictive power for BCD regimen (AUC: 0.84-0.896 in the training set, 0.801-0.885 in the validation set), and RF performed relatively better than others. Nevertheless, all the BCD-based models were incapable of predicting the VRD treatment response. The wavelet-HLH_firstorder_kurtosis was also associated with PFS (log-rank
). Conclusion. The four wavelet features were valuable biomarkers for predicting the response to BCD regimen. The six models based on these features showed predictive power, and RF was the best. One wavelet feature was also a survival-related biomarker. MRI-based radiomics had the potential to guide clinicians in MM management.
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Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, Mokrane FZ, Farwell M, Ammari S, Schoder H, Zhao B, Schwartz LH. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-005292. [PMID: 36180071 PMCID: PMC9528623 DOI: 10.1136/jitc-2022-005292] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2022] [Indexed: 11/04/2022] Open
Abstract
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.
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Affiliation(s)
- Laurent Dercle
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Jeremy McGale
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Shawn Sun
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Aurelien Marabelle
- Therapeutic Innovation and Early Trials, Gustave Roussy, Villejuif, Île-de-France, France
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Deutsch
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France
| | | | - Michael Farwell
- Division of Nuclear Medicine and Molecular Imaging, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samy Ammari
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France.,Radiology, Institut de Cancérologie Paris Nord, Sarcelles, France
| | - Heiko Schoder
- Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Binsheng Zhao
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
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Zheng YM, Yuan MG, Zhou RQ, Hou F, Zhan JF, Liu ND, Hao DP, Dong C. A computed tomography-based radiomics signature for predicting expression of programmed death ligand 1 in head and neck squamous cell carcinoma. Eur Radiol 2022; 32:5362-5370. [PMID: 35298679 DOI: 10.1007/s00330-022-08651-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/02/2022] [Accepted: 02/13/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Accurate prediction of the expression of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) before immunotherapy is crucial. This study was performed to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to predict the expression of PD-L1 in HNSCC. METHODS In total, 157 patients with confirmed HNSCC who underwent CECT scans and immunohistochemical examination of tumor PD-L1 expression were enrolled in this study. The patients were divided into a training set (n = 104; 62 PD-L1-positive and 42 PD-L1-negative) and an external validation set (n = 53; 34 PD-L1-positive and 19 PD-L1-negative). A radiomics signature was constructed from radiomics features extracted from the CECT images, and a radiomics score was calculated. Performance of the radiomics signature was assessed using receiver operating characteristics analysis. RESULTS Nine features were finally selected to construct the radiomics signature. The performance of the radiomics signature to distinguish between a PD-L1-positive and PD-L1-negative status in both the training and validation sets was good, with an area under the receiver operating characteristics curve of 0.852 and 0.802 for the training and validation sets, respectively. CONCLUSIONS A CECT-based radiomics signature was constructed to predict the expression of PD-L1 in HNSCC. This model showed favorable predictive efficacy and might be useful for identifying patients with HNSCC who can benefit from anti-PD-L1 immunotherapy. KEY POINTS • Accurate prediction of the expression of PD-L1 in HNSCC before immunotherapy is crucial. • A CECT-based radiomics signature showed favorable predictive efficacy in estimation of the PD-L1 expression status in patients with HNSCC.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Rui-Qing Zhou
- Department of Radiology, Jiaozhou Hospital of Traditional Chinese Medicine, Jiaozhou, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jin-Feng Zhan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Nai-Dong Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Da-Peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022; 10:e004848. [PMID: 35793875 PMCID: PMC9260846 DOI: 10.1136/jitc-2022-004848] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient's outcomes and to assess response to these promising combinations.
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Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Théophraste Henry
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Nuclear Medicine, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Anthony Hamaoui
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Ines Chaffai
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Cyrus Chargari
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Brachytherapy Unit, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- INSERM U1030, Gustave Roussy, Villejuif, France
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Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022; 66:575-591. [PMID: 35581928 PMCID: PMC9323544 DOI: 10.1111/1754-9485.13426] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022]
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation OncologyPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of Oncology, University of MelbournePeter MacCallum Cancer CentreMelbourneVictoriaAustralia
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Wang Y, Zheng XD, Zhu GQ, Li N, Zhou CW, Yang C, Zeng MS. Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma. Front Immunol 2022; 13:861328. [PMID: 35479084 PMCID: PMC9035905 DOI: 10.3389/fimmu.2022.861328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/22/2022] [Indexed: 01/01/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.
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Affiliation(s)
- Yi Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xin-De Zheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Gui-Qi Zhu
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Na Li
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chang-Wu Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Meng-Su Zeng, ; Chun Yang,
| | - Meng-Su Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
- *Correspondence: Meng-Su Zeng, ; Chun Yang,
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A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment. Aging (Albany NY) 2022; 14:907-922. [PMID: 35073519 PMCID: PMC8833127 DOI: 10.18632/aging.203850] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 01/11/2022] [Indexed: 12/24/2022]
Abstract
Programmed cell death 1 (PD1) inhibitors have shown promising treatment effects in advanced gastric cancer, the beneficiary population not definite. This study aimed to construct an individualized radiomics model to predict the treatment benefits of PD-1 inhibitors in gastric cancer. Patients with advanced gastric cancer treated with PD-1 inhibitors were randomly divided into a training set (n = 58) and a validation set (n = 29). CT imaging data were extracted from medical records, and an individual radiomics nomogram was generated based on the imaging features and clinicopathological risk factors. Discrimination performance was evaluated by Harrell’s c-index and receiver operator characteristic (ROC) curve analyses. The areas under the ROC curves (AUCs) were analyzed to predict anti-PD-1 efficacy and survival. We found that the radiomics nomogram could predict the response of gastric cancer to anti-PD-1 treatment. The AUC was 0.865 with a 95% CI of 0.812-0.828 in the training set, while the AUC was 0.778 with a 95% CI of 0.732–0.776 in the validation set. The diagnostic performance of the radiomics was significantly higher than that of the clinical factors (p < 0.01). Patients with a low risk of disease progression discriminated by the radiomics nomogram had longer progression-free survival than those with a high risk (6.5 vs. 3.2 months, HR 1.99, 95% CI: 1.19-3.31, p = 0.009). The radiomics nomogram based on CT imaging features and clinical risk factors could predict the treatment benefits of PD-1 inhibitors in advanced gastric cancer, enabling it to guide decision-making regarding clinical treatment.
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Zheng YM, Zhan JF, Yuan MG, Hou F, Jiang G, Wu ZJ, Dong C. A CT-based radiomics signature for preoperative discrimination between high and low expression of programmed death ligand 1 in head and neck squamous cell carcinoma. Eur J Radiol 2022; 146:110093. [PMID: 34890937 DOI: 10.1016/j.ejrad.2021.110093] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Accurate prediction of the expression level of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) is crucial before immunotherapy. The purpose of this study was to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to discriminate between high and low expression status of PD-L1. METHODS A total of 179 HNSCC patients who underwent immunohistochemical examination of tumor PD-L1 expression at one of two centers were enrolled in this study and divided into a training set (n = 122; 55 high PD-L1 expression and 67 low PD-L1 expression) and an external validation set (n = 57; 26 high PD-L1 expression and 31 low PD-L1 expression). The least absolute shrinkage and selection operator method was used to select the key features for a CECT-image-based radiomics signature. The performance of the radiomics signature was assessed using receiver operating characteristics analysis. RESULTS Six features were finally selected to construct the radiomics signature. The performance of the radiomics signature in the discrimination between high and low PD-L1 expression status was good in both the training and validation sets, with areas under the receiver operating characteristics curve of 0.889 and 0.834 for the training and validation sets, respectively. CONCLUSIONS The constructed CECT-based radiomics signature model showed favorable performance for discriminating between high and low PD-L1 expression status in HNSCC patients. It may be useful for screening out those patients with HNSCC who can best benefit from anti-PD-L1 immunotherapy.
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Affiliation(s)
- Ying-Mei Zheng
- Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jin-Feng Zhan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ming-Gang Yuan
- Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Gang Jiang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zeng-Jie Wu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Cheng Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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35
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Zheng Z, Gu Z, Xu F, Maskey N, He Y, Yan Y, Xu T, Liu S, Yao X. Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer. Cancer Imaging 2021; 21:65. [PMID: 34863282 PMCID: PMC8642943 DOI: 10.1186/s40644-021-00433-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/12/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa. METHODS AND MATERIALS We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status. RESULTS 1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05). CONCLUSIONS The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.
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Affiliation(s)
- Zongtai Zheng
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Zhuoran Gu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Feijia Xu
- Department of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Niraj Maskey
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Yanyan He
- Department of Pathology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yang Yan
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
| | - Tianyuan Xu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China
- Department of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shenghua Liu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
- Institute of Urinary Oncology, School of Medicine, Tongji University, Yan Chang Zhong Road 301, Shanghai, 200072, China.
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Gao RZ, Wen R, Wen DY, Huang J, Qin H, Li X, Wang XR, He Y, Yang H. Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:2685-2697. [PMID: 33615528 DOI: 10.1002/jum.15659] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 05/28/2023]
Abstract
OBJECTIVES To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. METHODS We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. RESULTS A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. CONCLUSIONS The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA.
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Affiliation(s)
- Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dong-Yue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jing Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Rebuzzi SE, Banna GL, Murianni V, Damassi A, Giunta EF, Fraggetta F, De Giorgi U, Cathomas R, Rescigno P, Brunelli M, Fornarini G. Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence. Cancers (Basel) 2021; 13:5517. [PMID: 34771680 PMCID: PMC8583566 DOI: 10.3390/cancers13215517] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 12/13/2022] Open
Abstract
In recent years, the treatment landscape of urothelial carcinoma has significantly changed due to the introduction of immune checkpoint inhibitors (ICIs), which are the standard of care for second-line treatment and first-line platinum-ineligible patients with advanced disease. Despite the overall survival improvement, only a minority of patients benefit from this immunotherapy. Therefore, there is an unmet need to identify prognostic and predictive biomarkers or models to select patients who will benefit from ICIs, especially in view of novel therapeutic agents. This review describes the prognostic and predictive role, and clinical readiness, of clinical and tumour factors, including new molecular classes, tumour mutational burden, mutational signatures, circulating tumour DNA, programmed death-ligand 1, inflammatory indices and clinical characteristics for patients with urothelial cancer treated with ICIs. A classification of these factors according to the levels of evidence and grades of recommendation currently indicates both a prognostic and predictive value for ctDNA and a prognostic relevance only for concomitant medications and patients' characteristics.
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Affiliation(s)
- Sara Elena Rebuzzi
- Medical Oncology, Ospedale San Paolo, 17100 Savona, Italy
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genova, 16132 Genova, Italy
| | | | - Veronica Murianni
- Medical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (V.M.); (G.F.)
| | - Alessandra Damassi
- Academic Unit of Medical Oncology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy;
| | - Emilio Francesco Giunta
- Department of Precision Medicine, Università Degli Studi della Campania Luigi Vanvitelli, 80131 Naples, Italy;
| | | | - Ugo De Giorgi
- Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy;
| | - Richard Cathomas
- Division of Oncology/Hematology, Kantonsspital Graubünden, 7000 Chur, Switzerland;
| | - Pasquale Rescigno
- Interdisciplinary Group for Translational Research and Clinical Trials, Urogenital Cancers GIRT-Uro, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, 10060 Turin, Italy;
| | - Matteo Brunelli
- Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, 37134 Verona, Italy;
| | - Giuseppe Fornarini
- Medical Oncology Unit 1, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (V.M.); (G.F.)
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Zhu Y, Yao W, Xu BC, Lei YY, Guo QK, Liu LZ, Li HJ, Xu M, Yan J, Chang DD, Feng ST, Zhu ZH. Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers. BMC Cancer 2021; 21:1167. [PMID: 34717582 PMCID: PMC8557514 DOI: 10.1186/s12885-021-08899-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 10/11/2021] [Indexed: 12/22/2022] Open
Abstract
Objectives To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC). Methods A total of 64 patients with advance ESCC receiving first-line ICI + CT at two centers between January 2019 and June 2020 were enrolled in this study. Both 2D ROIs and 3D ROIs were segmented. ComBat correction was applied to minimize the potential bias on the results due to different scan protocols. A total of 788 features were extracted and radiomics models were built on corrected/uncorrected 2D and 3D features by using 5-fold cross-validation. The performance of the radiomics models was assessed by its discrimination, calibration and clinical usefulness with independent validation. Results Five features and support vector machine algorithm were selected to build the 2D uncorrected, 2D corrected, 3D uncorrected and 3D corrected radiomics models. The 2D radiomics models significantly outperformed the 3D radiomics models in both primary and validation cohorts. When ComBat correction was used, the performance of 2D models was better (p = 0.0059) in the training cohort, and significantly better (p < 0.0001) in the validation cohort. The 2D corrected radiomics model yielded the optimal performance and was used to build the nomogram. The calibration curve of the radiomics model demonstrated good agreement between prediction and observation and the decision curve analysis confirmed the clinical utility. Conclusions The easy-to-use 2D corrected radiomics model could facilitate noninvasive preselection of ESCC patients who would benefit from ICI + CT. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08899-x.
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Affiliation(s)
- Ying Zhu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510080, Province Guangdong, People's Republic of China.,Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Wang Yao
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Bing-Chen Xu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Yi-Yan Lei
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Qi-Kun Guo
- Department of Radiological Interventional, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Li-Zhi Liu
- Department of Medical Imaging Center, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Hao-Jiang Li
- Department of Medical Imaging Center, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Min Xu
- Scientific Collaboration, CT-MR Division, Canon Medical System (China), Jiuxianqiao North Road, Chaoyang District, 100015, Beijing, People's Republic of China
| | - Jing Yan
- Scientific Collaboration, CT-MR Division, Canon Medical System (China), Jiuxianqiao North Road, Chaoyang District, 100015, Beijing, People's Republic of China
| | - Dan-Dan Chang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China.
| | - Zhi-Hua Zhu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510080, Province Guangdong, People's Republic of China.
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Chen Z, Wang M, De Wilde RL, Feng R, Su M, Torres-de la Roche LA, Shi W. A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype. Front Immunol 2021; 12:749459. [PMID: 34603338 PMCID: PMC8484710 DOI: 10.3389/fimmu.2021.749459] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 08/30/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB. METHODS Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. RESULTS A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/. CONCLUSION TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.
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Affiliation(s)
- Zihao Chen
- Department of Urology, University of Freiburg, Freiburg, Germany
| | - Maoli Wang
- Department of Breast Surgery, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Rudy Leon De Wilde
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
| | - Ruifa Feng
- Breast Center of The Second Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Mingqiang Su
- Department of Urology, Zigong Hospital, Affiliated to Southwest Medical University, Zigong, China
| | | | - Wenjie Shi
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
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Integrating multiparametric MRI radiomics features and the Vesical Imaging-Reporting and Data System (VI-RADS) for bladder cancer grading. Abdom Radiol (NY) 2021; 46:4311-4323. [PMID: 33978825 DOI: 10.1007/s00261-021-03108-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/19/2021] [Accepted: 04/24/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Pathological grade is important for the treatment selection and outcome prediction in bladder cancer (BCa). We aimed to construct a radiomics-clinical nomogram to preoperatively differentiate high-grade BCa from low-grade BCa. METHODS A total of 185 BCa patients who received multiparametric MRI (mpMRI) before surgery between August 2014 and April 2020 were enrolled in our study. Radiomics features were extracted from the largest tumor located on dynamic contrast-enhancement and T2WI images. After feature selection, the synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (low-grade group). Radiomics signatures were constructed in the training set and assessed in the validation set. Univariable and multivariable logistic regression were applied to build a nomogram. RESULTS The radiomics signature generated by the least absolute shrinkage and selection operator model achieved the optimal performance for BCa grading in both the SMOTE-balanced training [accuracy: 93.2%, area under the curve (AUC): 0.961] and validation sets (accuracy: 89.9%, AUC: 0.952). A radiomics-clinical nomogram incorporating the radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score had novel calibration and discrimination both in the training (AUC: 0.956) and validation sets (AUC: 0.958). Decision curve analysis presented the clinical utility of the nomogram for decision-making. CONCLUSIONS The mpMRI-based radiomics signature had the potential to preoperatively predict the pathological grade of BCa. The proposed nomogram combining the radiomics signature with the VI-RADS score improved the diagnostic power, which may aid in clinical decision-making.
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Zheng Z, Xu F, Gu Z, Yan Y, Xu T, Liu S, Yao X. Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer. Front Oncol 2021; 11:619893. [PMID: 34055600 PMCID: PMC8155615 DOI: 10.3389/fonc.2021.619893] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 04/26/2021] [Indexed: 12/31/2022] Open
Abstract
Background The treatment and prognosis for muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are different. We aimed to construct a nomogram based on the multiparametric MRI (mpMRI) radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score for the preoperative differentiation of MIBC from NMIBC. Method The retrospective study involved 185 pathologically confirmed bladder cancer (BCa) patients (training set: 129 patients, validation set: 56 patients) who received mpMRI before surgery between August 2014 to April 2020. A total of 2,436 radiomics features were quantitatively extracted from the largest lesion located on the axial T2WI and from dynamic contrast-enhancement images. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature screening. The selected features were introduced to construct radiomics signatures using three classifiers, including least absolute shrinkage and selection operator (LASSO), support vector machines (SVM) and random forest (RF) in the training set. The differentiation performances of the three classifiers were evaluated using the area under the curve (AUC) and accuracy. Univariable and multivariable logistic regression were used to develop a nomogram based on the optimal radiomics signature and clinical characteristics. The performance of the radiomics signatures and the nomogram was assessed and validated in the validation set. Results Compared to the RF and SVM classifiers, the LASSO classifier had the best capacity for muscle invasive status differentiation in both the training (accuracy: 90.7%, AUC: 0.934) and validation sets (accuracy: 87.5%, AUC: 0.906). Incorporating the radiomics signature and VI-RADS score, the nomogram demonstrated better discrimination and calibration both in the training set (accuracy: 93.0%, AUC: 0.970) and validation set (accuracy: 89.3%, AUC: 0.943). Decision curve analysis showed the clinical usefulness of the nomogram. Conclusions The mpMRI radiomics signature may be useful for the preoperative differentiation of muscle-invasive status in BCa. The proposed nomogram integrating the radiomics signature with the VI-RADS score may further increase the differentiation power and improve clinical decision making.
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Affiliation(s)
- Zongtai Zheng
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Feijia Xu
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhuoran Gu
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yang Yan
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tianyuan Xu
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.,Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shenghua Liu
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
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Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD. Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 2021; 28:97-115. [PMID: 33937530 PMCID: PMC8076712 DOI: 10.1016/j.ctro.2021.03.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/20/2021] [Accepted: 03/24/2021] [Indexed: 02/08/2023] Open
Abstract
Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.
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Affiliation(s)
- Jarey H. Wang
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States
| | - Reid F. Thompson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Trebeschi S, Bodalal Z, van Dijk N, Boellaard TN, Apfaltrer P, Tareco Bucho TM, Nguyen-Kim TDL, van der Heijden MS, Aerts HJWL, Beets-Tan RGH. Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol 2021; 11:637804. [PMID: 33889546 PMCID: PMC8056079 DOI: 10.3389/fonc.2021.637804] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/22/2021] [Indexed: 01/06/2023] Open
Abstract
Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods. Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival. Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p < 0.001) for abdominal imaging, and 0.67 AUC (p < 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p < 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p < 0.001), suggesting its complementary value in current clinical settings. Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings.
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Affiliation(s)
- Stefano Trebeschi
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.,Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States
| | - Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Nick van Dijk
- Department of Medical Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Thierry N Boellaard
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Paul Apfaltrer
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Teresa M Tareco Bucho
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Thi Dan Linh Nguyen-Kim
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.,Institute for Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
| | - Michiel S van der Heijden
- Department of Medical Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,Department of Molecular Carcinogenesis, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Hugo J W L Aerts
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.,Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, United States.,Radiology and Nuclear Medicine, Maastricht University, Maastricht, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands.,GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands.,Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
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Choi SJ, Park KJ, Heo C, Park BW, Kim M, Kim JK. Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer. Clin Radiol 2021; 76:627.e13-627.e21. [PMID: 33762138 DOI: 10.1016/j.crad.2021.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/21/2020] [Accepted: 02/11/2021] [Indexed: 12/29/2022]
Abstract
AIM To develop and validate a radiomics-based model for predicting response to neoadjuvant chemotherapy (NAC) using baseline computed tomography (CT) images in patients with muscle-invasive bladder cancer (MIBC). MATERIALS AND METHODS A radiomics signature for predicting pathological complete response (pCR) was developed using radiomics features selected by a random forest classifier on baseline CT images, and imaging predictors were identified in the training set (87 patients). By incorporating imaging predictors and radiomics signature, an imaging-based model was constructed using multivariate logistic regression analysis and validated in an independent validation set consisting of 48 patients with CT from outside institutions. The performance and clinical usefulness of the imaging-based model for predicting pCR were evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Using a cut-off determined in the training set, the positive likelihood ratios of the imaging-based model were calculated and compared with imaging and histological predictors. RESULTS The radiomics signature was developed based on six stable radiomics features. An imaging-based model incorporating radiomics signature, tumour shape, tumour size, and clinical stage showed good performance for predicting pCR in both the training (AUC, 0.85; 95% confidence interval [CI], 0.78-0.93) and validation (AUC, 0.75; 95% CI, 0.60-0.86) sets, providing a larger net benefit in decision curve analysis. The imaging-based model showed a higher positive likelihood ratio (1.91) for pCR than imaging and histological predictors (1.33-1.63). CONCLUSIONS The radiomics-based model using baseline CT images may predict the response of patients with MIBC to NAC.
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Affiliation(s)
- S J Choi
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - K J Park
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - C Heo
- Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - B W Park
- Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - M Kim
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - J K Kim
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Yi X, Liu Y, Zhou B, Xiang W, Deng A, Fu Y, Zhao Y, Ouyang Q, Liu Y, Sun Z, Zhang K, Li X, Zeng F, Zhou H, Chen BT. Incorporating SULF1 polymorphisms in a pretreatment CT-based radiomic model for predicting platinum resistance in ovarian cancer treatment. Biomed Pharmacother 2021; 133:111013. [DOI: 10.1016/j.biopha.2020.111013] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/03/2020] [Accepted: 11/11/2020] [Indexed: 01/08/2023] Open
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Byun JH, Yoon IS, Jeong YD, Kim S, Jung IH. A Tumor-Immune Interaction Model for Synergistic Combinations of Anti PD-L1 and Ionizing Irradiation Treatment. Pharmaceutics 2020; 12:pharmaceutics12090830. [PMID: 32878065 PMCID: PMC7558639 DOI: 10.3390/pharmaceutics12090830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 08/25/2020] [Accepted: 08/29/2020] [Indexed: 11/30/2022] Open
Abstract
Combination therapy with immune checkpoint blockade and ionizing irradiation therapy (IR) generates a synergistic effect to inhibit tumor growth better than either therapy does alone. We modeled the tumor-immune interactions occurring during combined IT and IR based on the published data from Deng et al. The mathematical model considered programmed cell death protein 1 and programmed death ligand 1, to quantify data fitting and global sensitivity of critical parameters. Fitting of data from control, IR and IT samples was conducted to verify the synergistic effect of a combination therapy consisting of IR and IT. Our approach using the model showed that an increase in the expression level of PD-1 and PD-L1 was proportional to tumor growth before therapy, but not after initiating therapy. The high expression level of PD-L1 in T cells may inhibit IT efficacy. After combination therapy begins, the tumor size was also influenced by the ratio of PD-1 to PD-L1. These results highlight that the ratio of PD-1 to PD-L1 in T cells could be considered in combination therapy.
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Affiliation(s)
- Jong Hyuk Byun
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
- Institute of Mathematical Sciences, Pusan National University, Busan 46241, Korea
| | - In-Soo Yoon
- College of Pharmacy, Pusan National University, Busan 46241, Korea;
| | - Yong Dam Jeong
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
| | - Sungchan Kim
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
- Finance Fishery Manufacture Industrial Mathematics Center on Big Data, Pusan National University, Busan 46241, Korea
| | - Il Hyo Jung
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
- Finance Fishery Manufacture Industrial Mathematics Center on Big Data, Pusan National University, Busan 46241, Korea
- Correspondence:
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