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Chen L, Zhu W, Zhang W, Chen E, Zhou W. Magnetic resonance imaging radiomics-based prediction of severe inflammatory response in locally advanced rectal cancer patients after neoadjuvant radiochemotherapy. Langenbecks Arch Surg 2024; 409:218. [PMID: 39017754 PMCID: PMC11255083 DOI: 10.1007/s00423-024-03416-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 07/12/2024] [Indexed: 07/18/2024]
Abstract
PURPOSE To predict severe inflammatory response after neoadjuvant radiochemotherapy in locally advanced rectal cancer (RC) patients using magnetic resonance imaging (MRI) radiomics models. METHODS This retrospective study included patients who underwent radical surgery for RC cancer after neoadjuvant radiochemotherapy between July 2017 and December 2019 at XXX Hospital. MRI radiomics features were extracted from T2WI images before (pre-nRCT-RF) and after (post-nRCT-RF) neoadjuvant radiochemotherapy, and the variation of radiomics features before and after neoadjuvant radiochemotherapy (delta-RF) were calculated. Eight, eight, and five most relevant features were identified for pre-nRCT-RF, post-nRCT-RF, and delta-RF, respectively. RESULTS Eighty-six patients were included and randomized 3:1 to the training and test set (n = 65 and n = 21, respectively). The prediction model based on delta-RF had areas under the curve (AUCs) of 0.80 and 0.85 in the training and test set, respectively. A higher rate of difficult operations was observed in patients with severe inflammation (65.5% vs. 42.9%, P = 0.045). CONCLUSION The prediction model based on MRI delta-RF may be a useful tool for predicting severe inflammatory response after neoadjuvant radiochemotherapy in locally advanced RC patients.
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Affiliation(s)
- Li Chen
- Department of Colorectal Surgery, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China.
| | - Wenchao Zhu
- Department of Radiology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Wei Zhang
- Department of Colorectal Surgery, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Engeng Chen
- Department of Colorectal Surgery, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China
| | - Wei Zhou
- Department of Colorectal Surgery, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China
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Han X, Wang Y, Jia X, Zheng Y, Ding C, Zhang X, Zhang K, Cao Y, Li Y, Xia L, Zheng C, Huang J, Shi H. Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy. Transl Lung Cancer Res 2024; 13:1247-1263. [PMID: 38973966 PMCID: PMC11225045 DOI: 10.21037/tlcr-24-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 04/29/2024] [Indexed: 07/09/2024]
Abstract
Background No robust predictive biomarkers exist to identify non-small cell lung cancer (NSCLC) patients likely to benefit from immune checkpoint inhibitor (ICI) therapies. The aim of this study was to explore the role of delta-radiomics features in predicting the clinical outcomes of patients with advanced NSCLC who received ICI therapy. Methods Data of 179 patients with advanced NSCLC (stages IIIB-IV) from two institutions (Database 1 =133; Database 2 =46) were retrospectively analyzed. Patients in the Database 1 were randomly assigned into training and validation dataset, with a ratio of 8:2. Patients in Database 2 were allocated into testing dataset. Features were selected from computed tomography (CT) images before and 6-8 weeks after ICI therapy. For each lesion, a total of 1,037 radiomic features were extracted. Lowly reliable [intraclass correlation coefficient (ICC) <0.8] and redundant (r>0.8) features were excluded. The delta-radiomics features were defined as the relative net change of radiomics features between two time points. Prognostic models for progression-free survival (PFS) and overall survival (OS) were established using the multivariate Cox regression based on selected delta-radiomics features. A clinical model and a pre-treatment radiomics model were established as well. Results The median PFS (after therapy) was 7.0 [interquartile range (IQR): 3.4, 9.1] (range, 1.4-13.2) months. To predict PFS, the model established based on the five most contributing delta-radiomics features yielded Harrell's concordance index (C-index) values of 0.708, 0.688, and 0.603 in the training, validation, and testing databases, respectively. The median survival time was 12 (IQR: 8.7, 15.8) (range, 2.9-23.3) months. To predict OS, a promising prognostic performance was confirmed with the corresponding C-index values of 0.810, 0.762, and 0.697 in the three datasets based on the seven most contributing delta-radiomics features, respectively. Furthermore, compared with clinical and pre-treatment radiomics models, the delta-radiomics model had the highest area under the curve (AUC) value and the best patients' stratification ability. Conclusions The delta-radiomics model showed a good performance in predicting therapeutic outcomes in advanced NSCLC patients undergoing ICI therapy. It provides a higher predictive value than clinical and the pre-treatment radiomics models.
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Affiliation(s)
- Xiaoyu Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yujin Wang
- Departments of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuting Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | | | - Xiaohui Zhang
- Clinical Solution, Philips Healthcare, Shanghai, China
| | - Kailu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yunkun Cao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yumin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Liming Xia
- Departments of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jing Huang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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van Timmeren JE, Bussink J, Koopmans P, Smeenk RJ, Monshouwer R. Longitudinal Image Data for Outcome Modeling. Clin Oncol (R Coll Radiol) 2024:S0936-6555(24)00277-2. [PMID: 39003124 DOI: 10.1016/j.clon.2024.06.053] [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: 10/23/2023] [Revised: 04/15/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024]
Abstract
In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.
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Affiliation(s)
- J E van Timmeren
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - J Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - P Koopmans
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R J Smeenk
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
| | - R Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
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Han X, Wang M, Zheng Y, Wang N, Wu Y, Ding C, Jia X, Yang R, Geng M, Chen Z, Zhang S, Zhang K, Li Y, Liu J, Gu J, Liao Y, Fan J, Shi H. Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer. Eur Radiol 2024; 34:2716-2726. [PMID: 37736804 DOI: 10.1007/s00330-023-10241-x] [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: 11/16/2022] [Revised: 07/10/2023] [Accepted: 07/24/2023] [Indexed: 09/23/2023]
Abstract
OBJECTIVES To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients. METHODS Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated. RESULTS Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets. CONCLUSION The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC. CLINICAL RELEVANCE STATEMENT Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions. KEY POINTS • The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. • The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. • Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.
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Affiliation(s)
- Xiaoyu Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China
| | - Mingliang Wang
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Thoracic Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuting Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China
| | - Na Wang
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
| | - Ying Wu
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
| | - Chengyu Ding
- ShuKun (BeiJing) Technology Co., Ltd., Beijing, The People's Republic of China
| | - Xi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China
| | - Ran Yang
- Department of Thoracic Surgery, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang, China
| | - Mingfei Geng
- Department of Thoracic Surgery, Anyang Tumor Hospital, The Affiliated Anyang Tumor Hospital of Henan University of Science and Technology, Anyang, China
| | - Zhen Chen
- Department of Cardiothoracic Surgery, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Cardiothoracic Surgery, Yichang Central People's Hospital, Yichang, China
| | - Songlin Zhang
- Department of Cardiothoracic Surgery, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Cardiothoracic Surgery, Yichang Central People's Hospital, Yichang, China
| | - Kailu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China
| | - Yumin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China
| | - Jia Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China
| | - Jin Gu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China
| | - Yongde Liao
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Jun Fan
- Department of Cardiothoracic Surgery, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China.
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, The People's Republic of China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, The People's Republic of China.
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Wang K, Karalis JD, Elamir A, Bifolco A, Wachsmann M, Capretti G, Spaggiari P, Enrico S, Balasubramanian K, Fatimah N, Pontecorvi G, Nebbia M, Yopp A, Kaza R, Pedrosa I, Zeh H, Polanco P, Zerbi A, Wang J, Aguilera T, Ligorio M. Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients. Ann Surg Oncol 2024; 31:2608-2620. [PMID: 38151623 PMCID: PMC10908610 DOI: 10.1245/s10434-023-14805-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/06/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions. METHODS We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS. RESULTS The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69). CONCLUSIONS Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.
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Affiliation(s)
- Kai Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - John D Karalis
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ahmed Elamir
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Bifolco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Megan Wachsmann
- Department of Pathology, Veterans Affairs North Texas Health Care System, Dallas, TX, USA
| | - Giovanni Capretti
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Paola Spaggiari
- Department of Pathology, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Sebastian Enrico
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Nafeesah Fatimah
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Giada Pontecorvi
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Martina Nebbia
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Adam Yopp
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ravi Kaza
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Herbert Zeh
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Patricio Polanco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Zerbi
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Todd Aguilera
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Matteo Ligorio
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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Cheung BMF. Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study. Radiat Oncol J 2024; 42:4-16. [PMID: 38549380 PMCID: PMC10982060 DOI: 10.3857/roj.2023.00612] [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: 07/11/2023] [Revised: 09/22/2023] [Accepted: 10/10/2023] [Indexed: 04/04/2024] Open
Abstract
PURPOSE Stereotactic body radiotherapy (SBRT) has been widely utilized for curative treatment of early-stage non-small cell lung cancer (NSCLC). It has achieved good local control rate comparable to surgery. Currently, no standard risk model exists for SBRT outcome or complication prediction. Radiomics has the potential to improve clinical outcome prognostication. Here, we reviewed the current literature on the radiomic analyses of thoracic SBRT through the use of radiomic quality score (RQS). MATERIALS AND METHODS Literature search was conducted on PubMed and Embase to retrieve radiomics studies on SBRT for early NSCLC. The literature search included studies up to June 2021. Only full papers published in peer reviewed journals were included. Studies that included metastatic lung cancers or non-lung cancers were excluded. Two independent investigators evaluated each study using the RQS and resolved discrepancies through discussion. RESULTS A total number of 25 studies were analysed. The mean RQS was 7.76 of a maximum score of 36. This corresponds to 21.56% of the maximum score. Lack of feature reduction strategies, external validation and open data sharing were identified as key limitations of the reviewed studies. Meanwhile, various common radiomic signatures across different studies such as gray level co-occurrence matrix Homogeneity and energy have been identified. Multiple robust radiomic models have also been reviewed that may improve outcome or complication prediction. CONCLUSION Radiomics in thoracic SBRT has a very promising future as a prognostication tool. However, larger multicenter prospective studies are required to confirm radiomic signatures. Improvement in future study methodologies can also facilitate its wider application.
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Yang P, Shan J, Ge X, Zhou Q, Ding M, Niu T, Du J. Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study. Phys Eng Sci Med 2024; 47:295-307. [PMID: 38165634 DOI: 10.1007/s13246-023-01366-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/06/2023] [Indexed: 01/04/2024]
Abstract
This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.
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Affiliation(s)
- Pengfei Yang
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jingjing Shan
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xin Ge
- School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Qinxuan Zhou
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mingchao Ding
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
| | - Tianye Niu
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
| | - Jichen Du
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
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Miranda J, Horvat N, Araujo-Filho JAB, Albuquerque KS, Charbel C, Trindade BMC, Cardoso DL, de Padua Gomes de Farias L, Chakraborty J, Nomura CH. The Role of Radiomics in Rectal Cancer. J Gastrointest Cancer 2023; 54:1158-1180. [PMID: 37155130 PMCID: PMC11301614 DOI: 10.1007/s12029-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/26/2022] [Indexed: 05/10/2023]
Abstract
PURPOSE Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT. METHODS We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically. RESULTS The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice. CONCLUSION Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
| | - Jose A B Araujo-Filho
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | - Kamila S Albuquerque
- Department of Radiology, Hospital Beneficência Portuguesa, 637 Maestro Cardim, Sao Paulo, SP, 01323-001, Brazil
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA
| | - Bruno M C Trindade
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
| | - Daniel L Cardoso
- Department of Radiology, Hospital Sirio-Libanes, 91 Adma Jafet, Sao Paulo, SP, 01308-050, Brazil
| | | | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, 75 Dr. Ovídio Pires de Campos, Sao Paulo, SP, 05403-010, Brazil
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Wu RR, Zhou YM, Xie XY, Chen JY, Quan KR, Wei YT, Xia XY, Chen WJ. Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy. Sci Rep 2023; 13:19409. [PMID: 37938596 PMCID: PMC10632513 DOI: 10.1038/s41598-023-46621-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/02/2023] [Indexed: 11/09/2023] Open
Abstract
This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. A total of 157 patients were divided into two groups: those without any intermediary- or high-risk factors and those with one intermediary-risk factor (negative group; n = 75). Those with any high-risk factor or more than one intermediary-risk factor (positive group; n = 82). Radiomics characteristics were extracted using Ax-LAVA + C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann-Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models-the clinical model, the radiomics model, and the combined clinic and radiomics model-were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. The use of machine learning algorithms to analyze Delta Ax LAVA + C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy.
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Affiliation(s)
- Rong-Rong Wu
- Department of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Yi-Min Zhou
- School of Nuclear Science and Technology, University of South China, Hengyang, China
| | - Xing-Yun Xie
- Department of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Jin-Yang Chen
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China
| | - Ke-Run Quan
- Department of Radiation Oncology, Xiangtan City Central Hospital Xiangtan, Hengyang, China
| | - Yu-Ting Wei
- Department of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Xiao-Yi Xia
- Department of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
| | - Wen-Juan Chen
- Department of Radiation Oncology, Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
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10
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Ahmed B, Sheikhzadeh P, Changizi V, Abbasi M, Soleymani Y, Sarhan W, Rahmim A. CT radiomics analysis of primary colon cancer patients with or without liver metastases: a correlative study with [ 18F]FDG PET uptake values. Abdom Radiol (NY) 2023; 48:3297-3309. [PMID: 37453942 DOI: 10.1007/s00261-023-03999-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE Utilizing [18F]Fluoro-2-deoxy-D-glucose Positron Emission Tomography/Computed Tomography ([18F]FDG PET/CT) scans on primary colon cancer (CC) patients including with liver metastases (LM), we aimed to determine the relationship between structural CT radiomic features and metabolic PET standard uptake value (SUV) in these patients. MATERIAL AND METHOD A retrospective analysis was performed on 60 patients with primary CC, of which 40 had liver metastases that were more than 2 cm in diameter. [18F]FDG PET/CT was used to calculate SUVmax, and 42 CT radiomic characteristics were extracted from non-enhanced CT images. Tumors were manually segmented on fused PET/CT scans by two experienced nuclear medicine physicians. Sixty primary CC and forty LM lesions were segmented accordingly. In the cases of multiple LM lesions, the lesion with the largest diameter was chosen for segmentation. In a univariate analysis approach, we used Spearman correlation with multiple testing correction (Benjamini-Hutchberg false discovery rate (FDR), α = 0.05) to ascertain the relationship between SUVmax and CT radiomic features. RESULT Twenty-two (52.3%) and twenty-six (61.9%) CT radiomic features were found to be significantly correlated with SUVmax values of primary CC (n = 60) and LM (n = 40) lesions, respectively (FDR-corrected p value < 0.05 and 0.6 < |ρ| < 1). GLCM_homogeneity (ρ = 0.839), GLCM_dissimilarity (ρ = - 0.832), GLZLM_ZLNU (ρ = 0.827), and GLCM_contrast (ρ = - 0.815) were the 4 features most correlated with SUVmax in CC. On the other hand, in LM, the 4 features most correlated with SUVmax were GLRLM_LRHGE (ρ = 0.859), GLRLM_LRE (ρ = 0.859), GLRLM_LRLGE (ρ = 0.857), and GLRLM_RP (ρ = - 0.820). CONCLUSION We investigated the relationship between SUVmax of preoperative primary CC lesions and their LM with CT radiomic features. We found some CT radiomic features having relationships with the metabolic characteristics of lesions. This work suggests that non-invasive predictive imaging biomarkers for precision medicine can be derived from CT radiomic.
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Affiliation(s)
- Badr Ahmed
- Department of Radiology Technology and Radiotherapy, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Peyman Sheikhzadeh
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
| | - Vahid Changizi
- Department of Radiology Technology and Radiotherapy, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehrshad Abbasi
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Yunus Soleymani
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Wisam Sarhan
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
- Department of Nuclear Medicine International Hospital for Cancer and Nuclear Medicine, University of Kufa, Najaf, Iraq
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
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Amintas S, Giraud N, Fernandez B, Dupin C, Denost Q, Garant A, Frulio N, Smith D, Rullier A, Rullier E, Vuong T, Dabernat S, Vendrely V. The Crying Need for a Better Response Assessment in Rectal Cancer. Curr Treat Options Oncol 2023; 24:1507-1523. [PMID: 37702885 PMCID: PMC10643426 DOI: 10.1007/s11864-023-01125-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/09/2023] [Indexed: 09/14/2023]
Abstract
OPINION STATEMENT Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients' clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers.
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Affiliation(s)
- Samuel Amintas
- Tumor Biology and Tumor Bank Laboratory, CHU Bordeaux, F-33600, Pessac, France.
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France.
| | - Nicolas Giraud
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | | | - Charles Dupin
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
| | - Quentin Denost
- Bordeaux Colorectal Institute, F-33000, Bordeaux, France
| | - Aurelie Garant
- UT Southwestern Department of Radiation Oncology, Dallas, USA
| | - Nora Frulio
- Radiology Department, CHU Bordeaux, F-33600, Pessac, France
| | - Denis Smith
- Department of Digestive Oncology, CHU Bordeaux, F-33600, Pessac, France
| | - Anne Rullier
- Histology Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Eric Rullier
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Surgery Department, CHU Bordeaux, F-33600, Pessac, France
| | - Te Vuong
- Department of Radiation Oncology, McGill University, Jewish General Hospital, Montreal, Canada
| | - Sandrine Dabernat
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Biochemistry Department, CHU Bordeaux, F-33000, Bordeaux, France
| | - Véronique Vendrely
- BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France
- Department of Radiation Oncology, CHU Bordeaux, F-33000, Bordeaux, France
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12
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Zhang Y, Zheng J, Huang Z, Teng Y, Chen C, Xu J. Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI. Eur Radiol 2023; 33:7482-7493. [PMID: 37488296 PMCID: PMC10598191 DOI: 10.1007/s00330-023-09963-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/18/2023] [Accepted: 05/23/2023] [Indexed: 07/26/2023]
Abstract
OBJECTIVES To investigate whether morphological changes after surgery and delta-radiomics of the optic chiasm obtained from routine MRI could help predict postoperative visual recovery of pituitary adenoma patients. METHODS A total of 130 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (n = 87) and non-recovery group (n = 43) according to visual outcome 1 year after endoscopic endonasal transsphenoidal surgery. Morphological parameters of the optic chiasm were measured preoperatively and postoperatively, including chiasmal thickness, deformed angle, and suprasellar extension. Delta-radiomics of the optic chiasm were calculated based on features extracted from preoperative and postoperative coronal T2-weighted images, followed by machine learning modeling using least absolute shrinkage and selection operator wrapped with support vector machine through fivefold cross-validation in the development set. The delta-radiomic model was independently evaluated in the test set, and compared with the combined model that incorporated delta-radiomics, significant clinical and morphological parameters. RESULTS Postoperative morphological changes of the optic chiasm could not significantly be used as predictors for the visual outcome. In contrast, the delta-radiomics model represented good performances in predicting visual recovery, with an AUC of 0.821 in the development set and 0.811 in the independent test set. Moreover, the combined model that incorporated age and delta-radiomics features of the optic chiasm achieved the highest AUC of 0.841 and 0.840 in the development set and independent test set, respectively. CONCLUSIONS Our proposed machine learning models based on delta-radiomics of the optic chiasm can be used to predict postoperative visual recovery of pituitary adenoma patients. CLINICAL RELEVANCE STATEMENT Our delta-radiomics-based models from MRI enable accurate visual recovery predictions in pituitary adenoma patients who underwent endoscopic endonasal transsphenoidal surgery, facilitating better clinical decision-making and ultimately improving patient outcomes. KEY POINTS • Prediction of the postoperative visual outcome for pituitary adenoma patients is important but challenging. • Delta-radiomics of the optic chiasm after surgical decompression represented better prognostic performances compared with its morphological changes. • The proposed machine learning models can serve as novel approaches to predict visual recovery for pituitary adenoma patients in clinical practice.
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Affiliation(s)
- Yang Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Junkai Zheng
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Zhouyang Huang
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Yuen Teng
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
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13
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Jo SW, Kim ES, Yoon DY, Kwon MJ. Changes in radiomic and radiologic features in meningiomas after radiation therapy. BMC Med Imaging 2023; 23:164. [PMID: 37858048 PMCID: PMC10588231 DOI: 10.1186/s12880-023-01116-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/30/2023] [Indexed: 10/21/2023] Open
Abstract
OBJECTIVES This study evaluated the radiologic and radiomic features extracted from magnetic resonance imaging (MRI) in meningioma after radiation therapy and investigated the impact of radiation therapy in treating meningioma based on routine brain MRI. METHODS Observation (n = 100) and radiation therapy (n = 62) patients with meningioma who underwent MRI were randomly divided (7:3 ratio) into training (n = 118) and validation (n = 44) groups. Radiologic findings were analyzed. Radiomic features (filter types: original, square, logarithm, exponential, wavelet; feature types: first order, texture, shape) were extracted from the MRI. The most significant radiomic features were selected and applied to quantify the imaging phenotype using random forest machine learning algorithms. Area under the curve (AUC), sensitivity, and specificity for predicting both the training and validation sets were computed with multiple-hypothesis correction. RESULTS The radiologic difference in the maximum area and diameter of meningiomas between two groups was statistically significant. The tumor decreased in the treatment group. A total of 241 series and 1691 radiomic features were extracted from the training set. In univariate analysis, 24 radiomic features were significantly different (P < 0.05) between both groups. Best subsets were one original, three first-order, and six wavelet-based features, with an AUC of 0.87, showing significant differences (P < 0.05) in multivariate analysis. When applying the model, AUC was 0.76 and 0.79 for the training and validation set, respectively. CONCLUSION In meningioma cases, better size reduction can be expected after radiation treatment. The radiomic model using MRI showed significant changes in radiomic features after radiation treatment.
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Affiliation(s)
- Sang Won Jo
- Department of Radiology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, South Korea
| | - Eun Soo Kim
- Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, 22, Gwanpyeong-ro 170beon-gil, Dongan-gu, Anyang-si, 14068, Gyeonggi-do, Republic of Korea.
| | - Dae Young Yoon
- Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, South Korea
| | - Mi Jung Kwon
- Department of Pathology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si, Gyeonggi-do, South Korea
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14
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Jin Y, Wang Y, Zhu Y, Li W, Tang F, Liu S, Song B. A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective study. Medicine (Baltimore) 2023; 102:e34865. [PMID: 37832071 PMCID: PMC10578668 DOI: 10.1097/md.0000000000034865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 07/31/2023] [Indexed: 10/15/2023] Open
Abstract
The objective is to develop and validate a combined model for noninvasive preoperative differentiating tumor deposits (TDs) from lymph node metastasis (LNM) in patients with rectal cancer (RC). A total of 204 patients were enrolled and randomly divided into 2 sets (training and validation set) at a ratio of 8:2. Radiomics features of tumor and peritumor fat were extracted by using Pyradiomics software from the axial T2-weighted imaging of MRI. Rad-score based on extracted Radiomics features were calculated by combination of feature selection and the machine learning method. Factors (Rad-score, laboratory test factor, clinical factor, traditional characters of tumor on MRI) with statistical significance were integrated to build a combined model. The combined model was visualized by a nomogram, and its distinguish ability, diagnostic accuracy, and clinical utility were evaluated by the receiver operating characteristic curve (ROC) analysis, calibration curve, and clinical decision curve, respectively. Carbohydrate antigen (CA) 19-9, MRI reported node stage (MRI-N stage), tumor volume (cm3), and Rad-score were all included in the combined model (odds ratio = 3.881 for Rad-score, 2.859 for CA19-9, 0.411 for MRI-N stage, and 1.055 for tumor volume). The distinguish ability of the combined model in the training and validation cohorts was area under the summary receiver operating characteristic curve (AUC) = 0.863, 95% confidence interval (CI): 0.8-0.911 and 0.815, 95% CI: 0.663-0.919, respectively. And the combined model outperformed the clinical model in both training and validation cohorts (AUC = 0.863 vs 0.749, 0.815 vs 0.627, P = .0022, .0302), outperformed the Rad-score model only in training cohorts (AUC = 0.863 vs 0.819, P = .0283). The combined model had highest net benefit and showed good diagnostic accuracy. The combined model incorporating Rad-score and clinical factors could provide a preoperative differentiation of TD from LNM and guide clinicians in making individualized treatment strategy for patients with RC.
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Affiliation(s)
- Yumei Jin
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
- Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China
| | - Yewu Wang
- Department of Joint and Sports Medicine, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Yonghua Zhu
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Wenzhi Li
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Fengqiong Tang
- Department of Medicine Imaging Center, Kunming Medical University, Qujing First People’s Hospital, Yunnan, China
| | - Shengmei Liu
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
| | - Bin Song
- Department of Radiology, Sichuan University, West China Hospital, Sichuan, China
- Department of Radiology, Sanya People’s Hospital, Sanya, Hainan, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Sichuan University, West China Hospital, Sichuan, China
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15
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Adachi T, Nakamura M, Iramina H, Matsumoto K, Ishihara Y, Tachibana H, Kurokawa S, Cho S, Tanaka K, Fukumoto K, Nishiyama T, Kito S, Mizowaki T. Identification of reproducible radiomic features from on-board volumetric images: A multi-institutional phantom study. Med Phys 2023; 50:5585-5596. [PMID: 36932977 DOI: 10.1002/mp.16376] [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: 10/07/2022] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Radiomics analysis using on-board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. PURPOSE This study investigated the factors that influence the reproducibility of radiomic features extracted from on-board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features. METHODS The phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On-board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone-beam computed tomography (kV-CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV-CBCT, megavoltage-CBCT (MV-CBCT), and megavoltage computed tomography (MV-CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first-order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter-based, and 744 (i.e., 93 × 8) wavelet filter-based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature. RESULTS For internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter-tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter-based and seven wavelet filter-based features, were indicated as highly reproducible features. The gray-level run-length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray-level dependence matrix (N = 7) and gray-level co-occurrence matrix (N = 1) features. CONCLUSIONS We developed the standard phantom for radiomics analysis of kV-CBCT, MV-CBCT, and MV-CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on-board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter-based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.
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Affiliation(s)
- Takanori Adachi
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Hiraku Iramina
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Kazushige Matsumoto
- Department of Radiology, National Hospital Organization Kyoto Medical Center, Fushimi-ku, Kyoto, Japan
| | - Yoshitomo Ishihara
- Department of Radiation Oncology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Hidenobu Tachibana
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Shogo Kurokawa
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - SangYong Cho
- Division of Radiation Oncology, Chiba Cancer Center, Chuo-ku, Chiba, Japan
| | - Kazunori Tanaka
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Kenta Fukumoto
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Tomohiro Nishiyama
- Department of Radiation Oncology, Kyoto-Katsura Hospital, Nishikyo-ku, Kyoto, Japan
| | - Satoshi Kito
- Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Bunkyo-ku, Tokyo, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
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16
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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Fang Z, Pu H, Chen XL, Yuan Y, Zhang F, Li H. MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer. Abdom Radiol (NY) 2023; 48:2270-2283. [PMID: 37085730 DOI: 10.1007/s00261-023-03910-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/01/2023] [Accepted: 04/05/2023] [Indexed: 04/23/2023]
Abstract
PURPOSE To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan-Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up. RESULTS Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838-0.953) and validation cohort (AUC = 0.900,95%CI:0.771-1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838-0.953) in the training and (AUC = 0.912,95%CI:0.771-1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649-1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864-1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030). CONCLUSION Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage.
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Affiliation(s)
- Zhu Fang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Xiao-Li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, 55#Four Section of South Renmin Road, Wuhou District, Chengdu, 610000, China
| | - Yi Yuan
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Feng Zhang
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China
| | - Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China.
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Chuong MD, Palm RF, Tjong MC, Hyer DE, Kishan AU. Advances in MRI-Guided Radiation Therapy. Surg Oncol Clin N Am 2023; 32:599-615. [PMID: 37182995 DOI: 10.1016/j.soc.2023.02.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Image guidance for radiation therapy (RT) has evolved over the last few decades and now is routinely performed using cone-beam computerized tomography (CBCT). Conventional linear accelerators (LINACs) that use CBCT have limited soft tissue contrast, are not able to image the patient's internal anatomy during treatment delivery, and most are not capable of online adaptive replanning. RT delivery systems that use MRI have become available within the last several years and address many of the imaging limitations of conventional LINACs. Herein, the authors review the technical characteristics and advantages of MRI-guided RT as well as emerging clinical outcomes.
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Affiliation(s)
- Michael D Chuong
- Department of Radiation Oncology, Miami Cancer Institute, 8900 North Kendall Drive, Miami, FL 33176, USA.
| | - Russell F Palm
- Department of Radiation Oncology, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL 33612, USA
| | - Michael C Tjong
- Department of Radiation Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
| | - Daniel E Hyer
- Department of Radiation Oncology, University of Iowa, 200 Hawkins Dr, Iowa City, IA 52242, USA
| | - Amar U Kishan
- Department of Radiation Oncology, University of California Los Angeles, 1338 S Hope Street, Los Angeles, CA 90015, USA
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Wang K, Dohopolski M, Zhang Q, Sher D, Wang J. Towards reliable head and neck cancers locoregional recurrence prediction using delta-radiomics and learning with rejection option. Med Phys 2023; 50:2212-2223. [PMID: 36484346 PMCID: PMC10121744 DOI: 10.1002/mp.16132] [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: 10/21/2021] [Revised: 11/08/2022] [Accepted: 11/20/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE A reliable locoregional recurrence (LRR) prediction model is important for the personalized management of head and neck cancers (HNC) patients who received radiotherapy. This work aims to develop a delta-radiomics feature-based multi-classifier, multi-objective, and multi-modality (Delta-mCOM) model for post-treatment HNC LRR prediction. Furthermore, we aim to adopt a learning with rejection option (LRO) strategy to boost the reliability of Delta-mCOM model by rejecting prediction for samples with high prediction uncertainties. METHODS In this retrospective study, we collected PET/CT image and clinical data from 224 HNC patients who received radiotherapy (RT) at our institution. We calculated the differences between radiomics features extracted from PET/CT images acquired before and after radiotherapy and used them in conjunction with pre-treatment radiomics features as the input features. Using clinical parameters, PET radiomics features, and CT radiomics features, we built and optimized three separate single-modality models. We used multiple classifiers for model construction and employed sensitivity and specificity simultaneously as the training objectives for each of them. Then, for testing samples, we fused the output probabilities from all these single-modality models to obtain the final output probabilities of the Delta-mCOM model. In the LRO strategy, we estimated the epistemic and aleatoric uncertainties when predicting with a trained Delta-mCOM model and identified patients associated with prediction of higher reliability (low uncertainty estimates). The epistemic and aleatoric uncertainties were estimated using an AutoEncoder-style anomaly detection model and test-time augmentation (TTA) with predictions made from the Delta-mCOM model, respectively. Predictions with higher epistemic uncertainty or higher aleatoric uncertainty than given thresholds were deemed unreliable, and they were rejected before providing a final prediction. In this study, different thresholds corresponding to different low-reliability prediction rejection ratios were applied. Their values are based on the estimated epistemic and aleatoric uncertainties distribution of the validation data. RESULTS The Delta-mCOM model performed significantly better than the single-modality models, whether trained with pre-, post-treatment radiomics features or concatenated BaseLine and Delta-Radiomics Features (BL-DRFs). It was numerically superior to the PET and CT fused BL-DRF model (nonstatistically significant). Using the LRO strategy for the Delta-mCOM model, most of the evaluation metrics improved as the rejection ratio increased from 0% to around 25%. Utilizing both epistemic and aleatoric uncertainty for rejection yielded nonstatistically significant improved metrics compared to each alone at approximately a 25% rejection ratio. Metrics were significantly better than the no-rejection method when the reject ratio was higher than 50%. CONCLUSIONS The inclusion of the delta-radiomics feature improved the accuracy of HNC LRR prediction, and the proposed Delta-mCOM model can give more reliable predictions by rejecting predictions for samples of high uncertainty using the LRO strategy.
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Affiliation(s)
- Kai Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Michael Dohopolski
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qiongwen Zhang
- Department of Head and Neck Oncology, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - David Sher
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jing Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
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20
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Staal FC, Taghavi M, Hong EK, Tissier R, van Treijen M, Heeres BC, van der Zee D, Tesselaar ME, Beets-Tan RG, Maas M. CT-based radiomics to distinguish progressive from stable neuroendocrine liver metastases treated with somatostatin analogues: an explorative study. Acta Radiol 2023; 64:1062-1070. [PMID: 35702011 DOI: 10.1177/02841851221106598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment. PURPOSE To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment. MATERIAL AND METHODS A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model. RESULTS In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1-50 months). Radiomics models could not accurately classify response (area under the curve 0.44-0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model. CONCLUSION Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.
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Affiliation(s)
- Femke Cr Staal
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, 1228Netherlands Cancer Institute Amsterdam/University Medical Center Utrecht, Utrecht, The Netherlands
| | - M Taghavi
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Eun K Hong
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Radiology, 26725Seoul National University Hospital, Seoul, Republic of Korea
| | - Renaud Tissier
- Biostatistics Center, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Mark van Treijen
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, 1228Netherlands Cancer Institute Amsterdam/University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Endocrine Oncology, 8124University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Birthe C Heeres
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Margot Et Tesselaar
- Center for Neuroendocrine Tumors, ENETS Center of Excellence, 1228Netherlands Cancer Institute Amsterdam/University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Medical Oncology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina Gh Beets-Tan
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Monique Maas
- Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Xue C, Chu WCW, Yuan J, Poon DMC, Yang B, Zhou Y, Yu SK, Cheung KY. Determining the reliable feature change in longitudinal radiomics studies: A methodological approach using the reliable change index. Med Phys 2023; 50:958-969. [PMID: 36251320 DOI: 10.1002/mp.16046] [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: 04/07/2022] [Revised: 07/28/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Determination of reliable change of radiomics feature over time is essential and vital in delta-radiomics, but has not yet been rigorously examined. This study attempts to propose a methodological approach using reliable change index (RCI), a statistical metric to determine the reliability of quantitative biomarker changes by accounting for the baseline measurement standard error, in delta-radiomics. The use of RCI was demonstrated with the MRI data acquired from a group of prostate cancer (PCa) patients treated by 1.5 T MRI-guided radiotherapy (MRgRT). METHODS Fifty consecutive PCa patients who underwent five-fractionated MRgRT were retrospectively included, and 1023 radiomics features were extracted from the clinical target volume (CTV) and planning target volume (PTV). The two MRI datasets acquired at the first fraction (MRI11 and MRI21) were used to calculate the baseline feature reliability against image acquisition using intraclass correlation coefficient (ICC). The RCI was constructed based on the baseline feature measurement standard deviation, ICC, and feature value differences at two time points between the fifth (MRI51) and the first fraction MRI (MRI11). The reliable change of features was determined in each patient only if the calculated RCI was over 1.96 or smaller than -1.96. The feature changes between MRI51 and MRI11 were correlated to two patient-reported quality-of-life clinical endpoints of urinary domain summary score (UDSS) and bowel domain summary score (BDSS) in 35 patients using the Spearman correlation test. Only the significant correlations between a feature that was reliably changed in ≥7 patients (20%) by RCI and an endpoint were considered as true significant correlations. RESULTS The 352 (34.4%) and 386 (37.7%) features among all 1023 features were determined by RCI to be reliably changed in more than five (10%) patients in the CTV and PTV, respectively. Nineteen features were found reliably changed in the CTV and 31 features in the PTV, respectively, in 10 (20%) or more patients. These features were not necessarily associated with significantly different longitudinal feature values (group p-value < 0.05). Most reliably changed features in more than 10 patients had excellent or good baseline test-retest reliability ICC, while none showed poor reliability. The RCI method ruled out the features to be reliably changed when substantial feature measurement bias was presented. After applying the RCI criterion, only four and five true significant correlations were confirmed with UDSS and BDSS in the CTV, respectively, with low true significance correlation rates of 10.8% (4/37) and 17.9% (5/28). No true significant correlations were found in the PTV. CONCLUSIONS The RCI method was proposed for delta-radiomics and demonstrated using PCa MRgRT data. The RCI has advantages over some other statistical metrics commonly used in the previous delta-radiomics studies, and is useful to reliably identify the longitudinal radiomics feature change on an individual basis. This proposed RCI method should be helpful for the development of essential feature selection methodology in delta-radiomics.
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Affiliation(s)
- Cindy Xue
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China.,Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Darren M C Poon
- Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Bin Yang
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Yihang Zhou
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
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Liu Z, Wang Y, Shen F, Zhang Z, Gong J, Fu C, Shen C, Li R, Jing G, Cai S, Zhang Z, Sun Y, Tong T. Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine. EPMA J 2022; 13:633-647. [PMID: 36505889 PMCID: PMC9727035 DOI: 10.1007/s13167-022-00303-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/01/2022] [Indexed: 11/14/2022]
Abstract
Background Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient. Aims This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment. Methods A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM. Findings The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80-0.93] vs. 0.71 [95% CI: 0.59-0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76-0.90] vs. 0.68 [95% CI: 0.56-0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976-29.853], p < 0.001; HR = 6.427 [95% CI: 2.265-13.036], p = 0.002). Conclusion Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00303-3.
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Affiliation(s)
- Zonglin Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yueming Wang
- Department of Anatomy and Physiology, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Zhiyuan Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Caixia Fu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Changqing Shen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Sanjun Cai
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Zhen Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yiqun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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23
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Xie F, Zhao Q, Li S, Wu S, Li J, Li H, Chen S, Jiang W, Dong A, Wu L, Liu L, Huang H, Xu S, Shao Y, Liu L, Li L, Cai P. Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer. Front Oncol 2022; 12:901287. [PMID: 36408187 PMCID: PMC9669703 DOI: 10.3389/fonc.2022.901287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 10/20/2022] [Indexed: 04/17/2024] Open
Abstract
In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.
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Affiliation(s)
- Fei Xie
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Qin Zhao
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Shuqi Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Shuangshuang Wu
- School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, China
| | - Jinli Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haojiang Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Shenghuan Chen
- Department of Radiology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China
| | - Wu Jiang
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Annan Dong
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Liqing Wu
- Department of Radiology, Guangzhou Concord Cancer Center, Guangzhou, China
| | - Long Liu
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Huabin Huang
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Shuoyu Xu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Yuanzhi Shao
- School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, China
| | - Lizhi Liu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
- Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Li Li
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
| | - Peiqiang Cai
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China
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MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy. Sci Rep 2022; 12:18631. [PMID: 36329116 PMCID: PMC9633752 DOI: 10.1038/s41598-022-22826-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/β = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.
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MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study. Clin Transl Radiat Oncol 2022; 38:175-182. [DOI: 10.1016/j.ctro.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
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Jia LL, Zheng QY, Tian JH, He DL, Zhao JX, Zhao LP, Huang G. Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1026216. [PMID: 36313696 PMCID: PMC9597310 DOI: 10.3389/fonc.2022.1026216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models. Methods We searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity. Results We selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score. Conclusions Radiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.
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Affiliation(s)
- Lu-Lu Jia
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Qing-Yong Zheng
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Jin-Hui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Di-Liang He
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Jian-Xin Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China
| | - Lian-Ping Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Gang Huang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
- *Correspondence: Gang Huang,
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Azadikhah A, Varghese BA, Lei X, Martin-King C, Cen SY, Duddalwar VA. Radiomics quality score in renal masses: a systematic assessment on current literature. Br J Radiol 2022; 95:20211211. [PMID: 35671097 PMCID: PMC10996962 DOI: 10.1259/bjr.20211211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To perform a systematic assessment and analyze the quality of radiomics methodology in current literature in the evaluation of renal masses using the Radiomics Quality Score (RQS) approach. METHODS We systematically reviewed recent radiomics literature in renal masses published in PubMed, EMBASE, Elsevier, and Web of Science. Two reviewers blinded by each other's scores evaluated the quality of radiomics methodology in studies published from 2015 to August 2021 using the RQS approach. Owing to the diversity in the imaging modalities and radiomics applications, a meta-analysis could not be performed. RESULTS Based on our inclusion/exclusion criteria, a total of 87 published studies were included in our study. The highest RQS was noted in three categories: reporting of clinical utility, gold standard, and feature reduction. The average RQS of the two reviewers ranged from 5 ≤ RQS≤19, with the maximum attainable RQS being 36. Very few (7/87 i.e., 8%) studies received an average RQS that ranged from 17 < RQS≤19, which represents studies with the highest RQS in our study. Many (39/87 i.e., 45%) studies received an average RQS that ranged from 13 < RQS≤15. No significant interreviewer scoring differences were observed. CONCLUSIONS We report that the overall scientific quality and reporting of radiomics studies in renal masses is suboptimal, and subsequent studies should bolster current deficiencies to improve reporting of radiomics methodologies. ADVANCES IN KNOWLEDGE The RQS approach is a meaningful quantitative scoring system to assess radiomics methodology quality and supports a comprehensive evaluation of the radiomics approach before its incorporation into clinical practice.
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Affiliation(s)
- Afshin Azadikhah
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Bino Abel Varghese
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Xiaomeng Lei
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Chloe Martin-King
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Steven Yong Cen
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
| | - Vinay Anant Duddalwar
- USC Radiomics Laboratory, Department of Radiology, Keck School
of Medicine, University of Southern California,
Los Angeles, United States
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28
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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29
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Qin Y, Zhu LH, Zhao W, Wang JJ, Wang H. Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer. Front Oncol 2022; 12:913683. [PMID: 36016617 PMCID: PMC9395725 DOI: 10.3389/fonc.2022.913683] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 06/23/2022] [Indexed: 12/20/2022] Open
Abstract
By breaking the traditional medical image analysis framework, precision medicine-radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially 125I CT-guided radioactive seed implant brachytherapy.
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Affiliation(s)
- Yun Qin
- School of Physics, Beihang University, Beijing, China
| | - Li-Hua Zhu
- School of Physics, Beihang University, Beijing, China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Jun-Jie Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Hao Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
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30
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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31
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Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery. Cancers (Basel) 2022; 14:cancers14123004. [PMID: 35740669 PMCID: PMC9221458 DOI: 10.3390/cancers14123004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery. Abstract We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.
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32
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Shahzadi I, Zwanenburg A, Lattermann A, Linge A, Baldus C, Peeken JC, Combs SE, Diefenhardt M, Rödel C, Kirste S, Grosu AL, Baumann M, Krause M, Troost EGC, Löck S. Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models. Sci Rep 2022; 12:10192. [PMID: 35715462 PMCID: PMC9205935 DOI: 10.1038/s41598-022-13967-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/17/2022] [Indexed: 11/21/2022] Open
Abstract
Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.
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Affiliation(s)
- Iram Shahzadi
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alex Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Annika Lattermann
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Annett Linge
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Christian Baldus
- Department of Radiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Jan C Peeken
- German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München, München, Germany.,Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
| | - Stephanie E Combs
- German Cancer Consortium (DKTK) partner site Munich, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiation Oncology, Klinikum rechts der Isar, Technische Universität München, München, Germany.,Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
| | - Markus Diefenhardt
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) partner site Frankfurt, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute, Frankfurt, Germany
| | - Claus Rödel
- Department of Radiotherapy and Oncology, Goethe-University Frankfurt, Frankfurt am Main, Germany.,German Cancer Consortium (DKTK) partner site Frankfurt, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,Frankfurt Cancer Institute, Frankfurt, Germany
| | - Simon Kirste
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) partner site Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anca-Ligia Grosu
- Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK) partner site Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Baumann
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mechthild Krause
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Esther G C Troost
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Steffen Löck
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany. .,German Cancer Consortium (DKTK) partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
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Boldrini L, Lenkowicz J, Orlandini LC, Yin G, Cusumano D, Chiloiro G, Dinapoli N, Peng Q, Casà C, Gambacorta MA, Valentini V, Lang J. Applicability of a pathological complete response magnetic resonance-based radiomics model for locally advanced rectal cancer in intercontinental cohort. Radiat Oncol 2022; 17:78. [PMID: 35428267 PMCID: PMC9013126 DOI: 10.1186/s13014-022-02048-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/04/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Predicting pathological complete response (pCR) in patients affected by locally advanced rectal cancer (LARC) who undergo neoadjuvant chemoradiotherapy (nCRT) is a challenging field of investigation, but many of the published models are burdened by a lack of reliable external validation. Aim of this study was to evaluate the applicability of a magnetic resonance imaging (MRI) radiomic-based pCR model developed and validated in Europe, to a different cohort of patients from an intercontinental cancer center. METHODS The original model was based on two clinical and two radiomics features extracted from T2-weighted 1.5 T MRI of 161 LARC patients acquired before nCRT, considered as training set. Such model is here validated using the T2-w 1.5 and 3 T staging MRI of 59 LARC patients with different clinical characteristics consecutively treated in mainland Chinese cancer center from March 2017 to January 2018. Model performance were evaluated in terms of area under the receiver operator characteristics curve (AUC) and relative parameters, such as accuracy, specificity, negative and positive predictive value (NPV and PPV). RESULTS An AUC of 0.83 (CI 95%, 0.71-0.96) was achieved for the intercontinental cohort versus a value of 0.75 (CI 95%, 0.61-0.88) at the external validation step reported in the original experience. Considering the best cut-off threshold identified in the first experience (0.26), the following predictive performance were obtained: 0.65 as accuracy, 0.64 as specificity, 0.70 as sensitivity, 0.91 as NPV and 0.28 as PPV. CONCLUSIONS Despite the introduction of significant different factors, the proposed model appeared to be replicable on a real-world data extra-European patients' cohort, achieving a TRIPOD 4 level.
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Affiliation(s)
- Luca Boldrini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jacopo Lenkowicz
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Lucia Clara Orlandini
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Gang Yin
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Davide Cusumano
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Nicola Dinapoli
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Qian Peng
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Calogero Casà
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Vincenzo Valentini
- grid.414603.4Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Jinyi Lang
- grid.54549.390000 0004 0369 4060Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Wang F, Tan BF, Poh SS, Siow TR, Lim FLWT, Yip CSP, Wang MLC, Nei W, Tan HQ. Predicting outcomes for locally advanced rectal cancer treated with neoadjuvant chemoradiation with CT-based radiomics. Sci Rep 2022; 12:6167. [PMID: 35418656 PMCID: PMC9008122 DOI: 10.1038/s41598-022-10175-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 03/31/2022] [Indexed: 12/24/2022] Open
Abstract
A feasibility study was performed to determine if CT-based radiomics could play an augmentative role in predicting neoadjuvant rectal score (NAR), locoregional failure free survival (LRFFS), distant metastasis free survival (DMFS), disease free survival (DFS) and overall survival (OS) in locally advanced rectal cancer (LARC). The NAR score, which takes into account the pathological tumour and nodal stage as well as clinical tumour stage, is a validated surrogate endpoint used for early determination of treatment response whereby a low NAR score (< 8) has been correlated with better outcomes and high NAR score (> 16) has been correlated with poorer outcomes. CT images of 191 patients with LARC were used in this study. Primary tumour (GTV) and mesorectum (CTV) were contoured separately and radiomics features were extracted from both segments. Two NAR models (NAR > 16 and NAR < 8) models were constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and the survival models were constructed using regularized Cox regressions. Area under curve (AUC) and time-dependent AUC were used to quantify the performance of the LASSO and Cox regression respectively, using ten folds cross validations. The NAR > 16 and NAR < 8 models have an average AUCs of 0.68 ± 0.13 and 0.59 ± 0.14 respectively. There are statistically significant differences between the clinical and combined model for LRFFS (from 0.68 ± 0.04 to 0.72 ± 0.04), DMFS (from 0.68 ± 0.05 to 0.70 ± 0.05) and OS (from 0.64 ± 0.06 to 0.66 ± 0.06). CTV radiomics features were also found to be more important than GTV features in the NAR prediction model. The most important clinical features are age and CEA for NAR > 16 and NAR < 8 models respectively, while the most significant clinical features are age, surgical margin and NAR score across all the four survival models.
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Affiliation(s)
- Fuqiang Wang
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.
| | - Boon Fei Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Sharon Shuxian Poh
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Tian Rui Siow
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | | | - Connie Siew Poh Yip
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | | | - Wenlong Nei
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore.
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Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022; 42:1-13. [PMID: 35671432 DOI: 10.1200/edbk_350931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.
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Affiliation(s)
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH
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Boca (Petresc) B, Caraiani C, Popa L, Lebovici A, Feier DS, Bodale C, Buruian MM. The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study. BIOLOGY 2022; 11:biology11030452. [PMID: 35336825 PMCID: PMC8945327 DOI: 10.3390/biology11030452] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/14/2022] [Indexed: 11/16/2022]
Abstract
Simple Summary Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases. Abstract This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.
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Affiliation(s)
- Bianca Boca (Petresc)
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.B.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Cosmin Caraiani
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Department of Radiology, Regional Institute of Gastroenterology and Hepatology “Prof. Dr. Octavian Fodor”, 400158 Cluj-Napoca, Romania
- Correspondence: (C.C.); (L.P.)
| | - Loredana Popa
- Department of Medical Imaging, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Correspondence: (C.C.); (L.P.)
| | - Andrei Lebovici
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Diana Sorina Feier
- Department of Radiology, Emergency Clinical County Hospital Cluj-Napoca, 400006 Cluj-Napoca, Romania; (A.L.); (D.S.F.)
- Department of Radiology, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Carmen Bodale
- Department of Oncology, Amethyst Radiotherapy Center Cluj, 407280 Florești, Romania;
- Department of Medical Oncology and Radiotherapy, “Iuliu Hațieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Mircea Marian Buruian
- Department of Radiology, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (B.B.); (M.M.B.)
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Liu C, Li M, Xiao H, Li T, Li W, Zhang J, Teng X, Cai J. Advances in MRI‐guided precision radiotherapy. PRECISION RADIATION ONCOLOGY 2022. [DOI: 10.1002/pro6.1143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Chenyang Liu
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Mao Li
- Department of Radiation Oncology Philips Healthcare Chengdu China
| | - Haonan Xiao
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Tian Li
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Wen Li
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Jiang Zhang
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Xinzhi Teng
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
| | - Jing Cai
- Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China
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Chiloiro G, Cusumano D, Boldrini L, Romano A, Placidi L, Nardini M, Meldolesi E, Barbaro B, Coco C, Crucitti A, Persiani R, Petruzziello L, Ricci R, Salvatore L, Sofo L, Alfieri S, Manfredi R, Valentini V, Gambacorta MA. THUNDER 2: THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI guided radiotherapy. BMC Cancer 2022; 22:67. [PMID: 35033008 PMCID: PMC8760695 DOI: 10.1186/s12885-021-09158-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/24/2021] [Indexed: 02/07/2023] Open
Abstract
Background Neoadjuvant chemoradiation therapy (nCRT) is the standard treatment modality in locally advanced rectal cancer (LARC). Since response to radiotherapy (RT) is dose dependent in rectal cancer, dose escalation may lead to higher complete response rates. The possibility to predict patients who will achieve complete response (CR) is fundamental. Recently, an early tumour regression index (ERI) was introduced to predict pathological CR (pCR) after nCRT in LARC patients. The primary endpoints will be the increase of CR rate and the evaluation of feasibility of delta radiomics-based predictive MRI guided Radiotherapy (MRgRT) model. Methods Patients affected by LARC cT2-3, N0-2 or cT4 for anal sphincter involvement N0-2a, M0 without high risk features will be enrolled in the trial. Neoadjuvant CRT will be administered using MRgRT. The initial RT treatment will consist in delivering 55 Gy in 25 fractions on Gross Tumor Volume (GTV) plus the corresponding mesorectum and 45 Gy in 25 fractions on the drainage nodes. Chemotherapy with 5-fluoracil (5-FU) or oral capecitabine will be administered continuously. A 0.35 Tesla MRI will be acquired at simulation and every day during MRgRT. At fraction 10, ERI will be calculated: if ERI will be inferior than 13.1, the patient will continue the original treatment; if ERI will be higher than 13.1 the treatment plan will be reoptimized, intensifying the dose to the residual tumor at the 11th fraction to reach 60.1 Gy. At the end of nCRT instrumental examinations are to be performed in order to restage patients. In case of stable disease or progression, the patient will undergo surgery. In case of major or complete clinical response, conservative approaches may be chosen. Patients will be followed up to evaluate toxicity and quality of life. The number of cases to be enrolled will be 63: all the patients will be treated at Fondazione Policlinico Universitario A. Gemelli IRCCS in Rome. Discussion This clinical trial investigates the impact of RT dose escalation in poor responder LARC patients identified using ERI, with the aim of increasing the probability of CR and consequently an organ preservation benefit in this group of patients. Trial registration ClinicalTrials.gov Identifier: NCT04815694 (25/03/2021).
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Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Matteo Nardini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Elisa Meldolesi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Claudio Coco
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Antonio Crucitti
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Roberto Persiani
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Lucio Petruzziello
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Riccardo Ricci
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Lisa Salvatore
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Luigi Sofo
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Sergio Alfieri
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
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Sushentsev N, Rundo L, Blyuss O, Nazarenko T, Suvorov A, Gnanapragasam VJ, Sala E, Barrett T. Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance. Eur Radiol 2022; 32:680-689. [PMID: 34255161 PMCID: PMC8660717 DOI: 10.1007/s00330-021-08151-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/27/2021] [Accepted: 06/13/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - Leonardo Rundo
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Oleg Blyuss
- School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield, UK
- Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Tatiana Nazarenko
- Department of Mathematics and Institute for Women's Health, University College London, London, UK
| | - Aleksandr Suvorov
- World-Class Research Center "Digital Biodesign and Personalised Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vincent J Gnanapragasam
- Division of Urology, Department of Surgery, University of Cambridge, Cambridge, UK
- Cambridge Urology Translational Research and Clinical Trials Office, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
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Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D'Ippolito E, Annunziata S, Farchione A, Belfiore MP, Desideri I, Cappabianca S. Delta radiomics: a systematic review. Radiol Med 2021; 126:1571-1583. [PMID: 34865190 DOI: 10.1007/s11547-021-01436-7] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/18/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool. RESULTS Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Luca Boldrini
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Giovanna Vacca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Emma D'Ippolito
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Salvatore Annunziata
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Alessandra Farchione
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Isacco Desideri
- Department of Biomedical, Experimental and Clinical Sciences "M. Serio", University of Florence, Florence, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
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Bonde A, Smith DA, Kikano E, Yoest JM, Tirumani SH, Ramaiya NH. Overview of serum and tissue markers in colorectal cancer: a primer for radiologists. Abdom Radiol (NY) 2021; 46:5521-5535. [PMID: 34415413 DOI: 10.1007/s00261-021-03243-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/05/2021] [Accepted: 08/07/2021] [Indexed: 12/17/2022]
Abstract
Serum and tissue tumor markers provide crucial information in the diagnosis, treatment, and follow-up of colorectal cancers. Tissue tumor markers are increasingly used for determination of targeted chemotherapy planning based on genotyping of tumor cells. Recently, plasma-based technique of liquid biopsy is being evaluated for providing tumor biomarkers in the management of colorectal cancer. Tumor markers are commonly used in conjunction with imaging during initial staging, treatment determination, response assessment, and determination of recurrence or metastatic disease. Knowledge of tumor markers and their association with radiological findings is thus crucial for radiologists. Additionally, various novel imaging techniques are being evaluated as potential noninvasive imaging biomarkers to predict tumor genotypes, features, and tumor response. We review and discuss the potential role of these newer imaging techniques.
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Affiliation(s)
- Apurva Bonde
- Department of Radiology, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, 78229, USA.
| | - Daniel A Smith
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Elias Kikano
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Jennifer M Yoest
- Department of Pathology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Sree H Tirumani
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Nikhil H Ramaiya
- Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
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Chiloiro G, Cusumano D, de Franco P, Lenkowicz J, Boldrini L, Carano D, Barbaro B, Corvari B, Dinapoli N, Giraffa M, Meldolesi E, Manfredi R, Valentini V, Gambacorta MA. Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development. Radiol Med 2021; 127:11-20. [PMID: 34725772 DOI: 10.1007/s11547-021-01421-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 10/14/2021] [Indexed: 12/23/2022]
Abstract
PURPOSE Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC). RESULTS The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM. CONCLUSION Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.
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Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Paola de Franco
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy.
| | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Davide Carano
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Barbara Corvari
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Nicola Dinapoli
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Martina Giraffa
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Elisa Meldolesi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
| | - Maria Antonietta Gambacorta
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Roma, Italy
- Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Roma, Italy
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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Korte JC, Cardenas C, Hardcastle N, Kron T, Wang J, Bahig H, Elgohari B, Ger R, Court L, Fuller CD, Ng SP. Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer. Sci Rep 2021; 11:17633. [PMID: 34480036 PMCID: PMC8417253 DOI: 10.1038/s41598-021-96600-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
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Affiliation(s)
- James C. Korte
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XDepartment of Biomedical Engineering, University of Melbourne, Melbourne, Australia
| | - Carlos Cardenas
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Nicholas Hardcastle
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1007.60000 0004 0486 528XCentre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Tomas Kron
- grid.1055.10000000403978434Department of Physical Science, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000 Australia ,grid.1008.90000 0001 2179 088XSir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Jihong Wang
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Houda Bahig
- grid.410559.c0000 0001 0743 2111Radiation Oncology Department, Centre Hospitalier de l’Université de Montréal, Montreal, Canada
| | - Baher Elgohari
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.10251.370000000103426662Clinical Oncology & Nuclear Medicine Department, Mansoura University, Mansoura, Egypt
| | - Rachel Ger
- grid.470142.40000 0004 0443 9766Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ USA
| | - Laurence Court
- grid.240145.60000 0001 2291 4776Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Clifton D. Fuller
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | - Sweet Ping Ng
- grid.240145.60000 0001 2291 4776Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, USA ,grid.1055.10000000403978434Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia ,grid.482637.cDepartment of Radiation Oncology, Olivia Newton-John Cancer Wellness and Research Centre, Melbourne, Australia
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Su CW, Lee JC, Chang YF, Su NW, Lee PH, Dai KY, Tai HC, Leu YS, Chen YJ. Delta-volume radiomics of induction chemotherapy to predict outcome of subsequent chemoradiotherapy for locally advanced hypopharyngeal cancer. TUMORI JOURNAL 2021; 108:450-460. [PMID: 34423708 DOI: 10.1177/03008916211039018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) is recommended for larynx-preserving treatment of locally advanced hypopharyngeal cancer (LAHC). However, the conventional evaluation of response is not robust enough to predict the outcome of subsequent treatments. This study aimed to develop an imaging biomarker using changes in radiomic features in invasive tumor front (ITF) by IC to predict treatment outcome of subsequent CCRT in LAHC. METHODS From 2006 to 2018, 59 computed tomography (CT) scan images before and after IC in patients with LAHC were used to contour the gross tumor volumes (GTVs). A total of 48 delta-volume radiomics features were acquired from the absolute spatial difference of GTVs (delta-GTV) before and after IC, conceptually representing a consistent portion of ITF. Least absolute shrinkage and selection operator regression (LASSO) was used to select features for establishing the model generating radiomic score (R score). RESULTS A model including 5 radiomic features from delta-GTV to predict better progression-free survival (PFS) of patients receiving subsequent CCRT was established. The R score was validated with all datasets (area under the curve 0.77). Low R score (<-0.16) was associated with improved PFS (p < 0.05). CONCLUSIONS The established radiomic model for ITF from radiomic features of delta-GTV after IC might be a potential imaging biomarker for predicting clinical outcome of subsequent CCRT in LAHC.
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Affiliation(s)
- Che-Wei Su
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Jehn-Chuan Lee
- Department of Otorhinolaryngology, MacKay Memorial Hospital, Taipei
| | - Yi-Fang Chang
- Department of Hematology and Oncology, MacKay Memorial Hospital, Taipei
| | - Nai-Wen Su
- Department of Hematology and Oncology, MacKay Memorial Hospital, Taipei
| | - Pei-Hsuan Lee
- Department of International Business, National Chengchi University, Taipei
| | - Kun-Yao Dai
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Hung-Chi Tai
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei
| | - Yi-Shing Leu
- Department of Otorhinolaryngology, MacKay Memorial Hospital, Taipei
| | - Yu-Jen Chen
- Department of Radiation Oncology, MacKay Memorial Hospital, Taipei.,Department of Nursing, MacKay Junior College of Medicine, Nursing, and Management, Taipei.,Department of Medical Research, China Medical University Hospital, Taichung
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Zhang G, Chen L, Liu A, Pan X, Shu J, Han Y, Huan Y, Zhang J. Comparable Performance of Deep Learning-Based to Manual-Based Tumor Segmentation in KRAS/NRAS/BRAF Mutation Prediction With MR-Based Radiomics in Rectal Cancer. Front Oncol 2021; 11:696706. [PMID: 34395262 PMCID: PMC8358773 DOI: 10.3389/fonc.2021.696706] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/15/2021] [Indexed: 12/22/2022] Open
Abstract
Radiomic features extracted from segmented tumor regions have shown great power in gene mutation prediction, while deep learning–based (DL-based) segmentation helps to address the inherent limitations of manual segmentation. We therefore investigated whether deep learning–based segmentation is feasible in predicting KRAS/NRAS/BRAF mutations of rectal cancer using MR-based radiomics. In this study, we proposed DL-based segmentation models with 3D V-net architecture. One hundred and eight patients’ images (T2WI and DWI) were collected for training, and another 94 patients’ images were collected for validation. We evaluated the DL-based segmentation manner and compared it with the manual-based segmentation manner through comparing the gene prediction performance of six radiomics-based models on the test set. The performance of the DL-based segmentation was evaluated by Dice coefficients, which are 0.878 ± 0.214 and 0.955 ± 0.055 for T2WI and DWI, respectively. The performance of the radiomics-based model in gene prediction based on DL-segmented VOI was evaluated by AUCs (0.714 for T2WI, 0.816 for DWI, and 0.887 for T2WI+DWI), which were comparable to that of corresponding manual-based VOI (0.637 for T2WI, P=0.188; 0.872 for DWI, P=0.181; and 0.906 for T2WI+DWI, P=0.676). The results showed that 3D V-Net architecture could conduct reliable rectal cancer segmentation on T2WI and DWI images. All-relevant radiomics-based models presented similar performances in KRAS/NRAS/BRAF prediction between the two segmentation manners.
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Affiliation(s)
- Guangwen Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Lei Chen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Aie Liu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xianpan Pan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jun Shu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Ye Han
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jinsong Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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47
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Sun R, Lerousseau M, Henry T, Carré A, Leroy A, Estienne T, Niyoteka S, Bockel S, Rouyar A, Alvarez Andres É, Benzazon N, Battistella E, Classe M, Robert C, Scoazec JY, Deutsch É. [Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]. Cancer Radiother 2021; 25:630-637. [PMID: 34284970 DOI: 10.1016/j.canrad.2021.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/19/2021] [Indexed: 12/24/2022]
Abstract
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.
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Affiliation(s)
- R Sun
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France.
| | - M Lerousseau
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - T Henry
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de médecine nucléaire, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - A Carré
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - A Leroy
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - T Estienne
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Niyoteka
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - S Bockel
- Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - A Rouyar
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - É Alvarez Andres
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; TheraPanacea, Paris, France
| | - N Benzazon
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | - E Battistella
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France
| | | | - C Robert
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
| | - J Y Scoazec
- Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France; Département de biologie et pathologie médicales, Gustave-Roussy Cancer Campus, 94800 Villejuif, France
| | - É Deutsch
- Université Paris-Saclay, institut Gustave-Roussy, Inserm, Radiothérapie moléculaire et innovation thérapeutique, 94800 Villejuif, France; Département de radiothérapie, Gustave-Roussy Cancer Campus, 94800 Villejuif, France; Faculté de médecine, université Paris-Sud Paris-Saclay, 94270 Kremlin-Bicêtre, France
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Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021; 13:cancers13143590. [PMID: 34298803 PMCID: PMC8303203 DOI: 10.3390/cancers13143590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy. Abstract Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Milan, Italy;
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
- Correspondence: ; Tel.: +39-055-7947719
| | - Carlotta Becherini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Daniela Greto
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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Ouyang G, Yang X, Deng X, Meng W, Yu Y, Wu B, Jiang D, Shu P, Wang Z, Yao J, Wang X. Predicting Response to Total Neoadjuvant Treatment (TNT) in Locally Advanced Rectal Cancer Based on Multiparametric Magnetic Resonance Imaging: A Retrospective Study. Cancer Manag Res 2021; 13:5657-5669. [PMID: 34285586 PMCID: PMC8286103 DOI: 10.2147/cmar.s311501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/19/2021] [Indexed: 02/05/2023] Open
Abstract
Purpose To investigate the potential value of magnetic resonance imaging (MRI) in predicting response relevance to total neoadjuvant treatment (TNT) in locally advanced rectal cancer. Methods We analyzed MRI of 71 patients underwent TNT from 2015 to 2017 retrospectively. We categorized the response of TNT as CR (complete response) vs non-CR, and high vs moderate vs low sensitivity. Logistic regression analysis was used to identify the best predictors of response. Diagnostic performance was assessed using receiver operating characteristic curve analysis. Results Post-ICT (induction chemotherapy) ∆TL (tumor length), post-CRT (concurrent chemoradiotherapy) ∆LNN (the numbers of lymph node metastases), post-CCT (consolidation chemotherapy) ∆SDWI (maximum cross-sectional area of tumor on diffusion-weighted imaging), post-CCT ADCT (the mean apparent diffusion coefficient values of tumor) and post-CCT ∆LNV (volume of lymph node) were the best CR predictors. Post-ICT ∆TL, post-CRT EMVI (extramural vascular invasion) and post-CCT ∆ST2 (S on T2-weight) were the best significant factors for high sensitivity. Conclusion Post-ICT ∆TL may be an early predictor of CR and high sensitivity to TNT. Dynamic analysis based on MRI between baseline and post-CCT could provide the most valuable prediction of CR. The grouping modality of CR vs non-CR may be more suitable for treatment response prediction than high vs moderate vs low sensitivity.
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Affiliation(s)
- Ganlu Ouyang
- Department of Radiation Oncology/Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Xibiao Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Xiangbing Deng
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Wenjian Meng
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Yongyang Yu
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Bing Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Dan Jiang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Pei Shu
- Department of Radiation Oncology/Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Ziqiang Wang
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Xin Wang
- Department of Radiation Oncology/Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
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Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging. Radiother Oncol 2021; 161:183-190. [PMID: 34139211 DOI: 10.1016/j.radonc.2021.06.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/08/2021] [Accepted: 06/08/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION To develop an image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance (MR) imaging. MATERIALS AND METHODS A total of 466 patients with locally advanced rectal cancer who received preoperative chemoradiotherapy followed by surgical resection were collected from single center, among whom 113 (24.3%) were allocated to the holdout testing set. Complete response (pCR) was defined as Dworak tumor regression grade (TRG) 4, while good response (GR) was defined as TRG 3 or 4. Based on post-chemoradiotherapy T2-weighted axial MR images, two deep learning models were developed to predict pCR and GR, respectively. The prediction performance of the deep learning models was evaluated in the testing set and was compared to that of a senior radiologist and a radiation oncologist. RESULTS The deep learning model showed an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 0.76, 0.30, 0.96, 0.67, 0.87, and 85.0% for predicting pCR and 0.72, 0.54, 0.81, 0.60, 0.77, and 71.7% for predicting GR, respectively. The deep learning model had a superior predictive performance than the observers. Fair agreement between the ground truth and the model was shown for pCR prediction (kappa = 0.34) and GR prediction (kappa = 0.36). CONCLUSIONS The post-chemoradiotherapy T2-weighted axial MR image-based deep learning model showed acceptable performance in predicting pCR or GR in patients with rectal cancer, compared with human observers.
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