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Yang L, Wang B, Shi X, Li B, Xie J, Wang C. Application research of radiomics in colorectal cancer: A bibliometric study. Medicine (Baltimore) 2024; 103:e37827. [PMID: 38608072 PMCID: PMC11018182 DOI: 10.1097/md.0000000000037827] [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: 12/06/2023] [Accepted: 03/15/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Radiomics has shown great potential in the clinical field of colorectal cancer (CRC). However, few bibliometric studies have systematically analyzed existing research in this field. The purpose of this study is to understand the current research status and future development directions of CRC. METHODS Search the English documents on the application of radiomics in the field of CRC research included in the Web of Science Core Collection from its establishment to October 2023. VOSviewer and CiteSpace software were used to conduct bibliometric and visual analysis of online publications related to countries/regions, authors, journals, references, and keywords in this field. RESULTS A total of 735 relevant documents published from Web of Science Core Collection to October 2023 were retrieved, and a total of 419 documents were obtained based on the screening criteria, including 376 articles and 43 reviews. The number of publications is increasing year by year. Among them, China publishes the most relevant documents (n = 238), which is much higher than Italy (n = 69) and the United States (n = 63). Tian Jie is the author with the most publications and citations (n = 17, citations = 2128), GE Healthcare is the most productive institution (n = 26), Frontiers in Oncology is the journal with the most publications (n = 60), and European Radiology is the most cited journal (n = 776). Hot spots for the application of radiomics in CRC include magnetic resonance, neoadjuvant chemoradiotherapy, survival, texture analysis, and machine learning. These directions are the current hot spots for the application of radiomics research in CRC and may be the direction of continued development in the future. CONCLUSION Through bibliometric analysis, the application of radiomics in CRC has been increasing year by year. The application of radiomics improves the accuracy of preoperative diagnosis, prediction, and prognosis of CRC. The results of bibliometrics analysis provide a valuable reference for the research direction of radiomics. However, radiomics still faces many challenges in the future, such as the single nature of the data source which may affect the comprehensiveness of the results. Future studies can further expand the data sources and build a multicenter public database to more comprehensively reflect the research status and development trend of CRC radiomics.
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Affiliation(s)
- Lihong Yang
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Binjie Wang
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Xiaoying Shi
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Bairu Li
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Jiaqiang Xie
- Department of Breast and Thyroid Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Changfu Wang
- Department of Radiology and Medical Imaging Research Institute, Huaihe Hospital of Henan University, Kaifeng, Henan, China
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Zhou Y, Zhan Y, Zhao J, Zhong L, Tan Y, Zeng W, Zeng Q, Gong M, Li A, Gong L, Liu L. CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study. Acad Radiol 2024:S1076-6332(24)00009-6. [PMID: 38302388 DOI: 10.1016/j.acra.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/01/2024] [Accepted: 01/04/2024] [Indexed: 02/03/2024]
Abstract
RATIONALE AND OBJECTIVES Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs). MATERIALS AND METHODS The present study included 201 patients with PPGLs from three hospitals (training set: n = 125; external validation set: n = 45; external test set: n = 31). Patients were divided into low-risk and high-risk groups using a staging system for adrenal pheochromocytoma and paraganglioma (GAPP). We extracted and selected CT radiomics features, and built radiomics models using support vector machines (SVM), k-nearest neighbors, random forests, and multilayer perceptrons. Using receiver operating characteristic curve analysis to select the optimal radiomics model, a combined model was built using the output of the optimal radiomics model and clinical radiological features, and its accuracy and clinical applicability were evaluated using calibration curves and clinical decision curve analysis (DCA). RESULTS Finally, 13 radiomics features were selected to construct machine learning models. In the radiomics model, the SVM model demonstrated higher accuracy and stability, with an AUC value of 0.915 in the training set, 0.846 in external validation set, and 0.857 in external test set. Combining the outputs of SVM models with two clinical radiological features, a combined model constructed has demonstrated optimal risk stratification ability for PPGLs with an AUC of 0.926 for the training set, 0.883 for the external validation set, and 0.899 for the external test set. The calibration curve and DCA show good calibration accuracy and clinical effectiveness for the combined model. CONCLUSION Combined model that integrates radiomics and clinical radiological features can discriminate the risk stratification of PPGLs.
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Affiliation(s)
- Yongjie Zhou
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China; The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China; Jiangxi Clinical Research Center for Cancer, Nanchang, China
| | - Yuan Zhan
- Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Jinhong Zhao
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Linhua Zhong
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Yongming Tan
- Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Wei Zeng
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Qiao Zeng
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Mingxian Gong
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Aihua Li
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China; The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China; Jiangxi Clinical Research Center for Cancer, Nanchang, China.
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DeVries DA, Tang T, Alqaidy G, Albweady A, Leung A, Laba J, Lagerwaard F, Zindler J, Hajdok G, Ward AD. Dual-center validation of using magnetic resonance imaging radiomics to predict stereotactic radiosurgery outcomes. Neurooncol Adv 2023; 5:vdad064. [PMID: 37358938 PMCID: PMC10289521 DOI: 10.1093/noajnl/vdad064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Abstract
Background MRI radiomic features and machine learning have been used to predict brain metastasis (BM) stereotactic radiosurgery (SRS) outcomes. Previous studies used only single-center datasets, representing a significant barrier to clinical translation and further research. This study, therefore, presents the first dual-center validation of these techniques. Methods SRS datasets were acquired from 2 centers (n = 123 BMs and n = 117 BMs). Each dataset contained 8 clinical features, 107 pretreatment T1w contrast-enhanced MRI radiomic features, and post-SRS BM progression endpoints determined from follow-up MRI. Random decision forest models were used with clinical and/or radiomic features to predict progression. 250 bootstrap repetitions were used for single-center experiments. Results Training a model with one center's dataset and testing it with the other center's dataset required using a set of features important for outcome prediction at both centers, and achieved area under the receiver operating characteristic curve (AUC) values up to 0.70. A model training methodology developed using the first center's dataset was locked and externally validated with the second center's dataset, achieving a bootstrap-corrected AUC of 0.80. Lastly, models trained on pooled data from both centers offered balanced accuracy across centers with an overall bootstrap-corrected AUC of 0.78. Conclusions Using the presented validated methodology, radiomic models trained at a single center can be used externally, though they must utilize features important across all centers. These models' accuracies are inferior to those of models trained using each individual center's data. Pooling data across centers shows accurate and balanced performance, though further validation is required.
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Affiliation(s)
- David A DeVries
- Department of Medical Biophysics, Western University, London, ON, Canada
- Gerald C. Baines Centre, London Health Sciences Centre, London, ON, Canada
| | - Terence Tang
- Department of Radiation Oncology, London Regional Cancer Program, London, ON, Canada
| | - Ghada Alqaidy
- Radiodiagnostic and Medical Imaging Department, King Fahad Armed Forces Hospital, Jeddah, Saudi Arabia
| | - Ali Albweady
- Department of Radiology, Unaizah College of Medicine and Medical Sciences, Qassim University, Unaizah, Saudi Arabia
| | - Andrew Leung
- Department of Medical Imaging, Western University, London, ON, Canada
| | - Joanna Laba
- Department of Radiation Oncology, London Regional Cancer Program, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
| | - Frank Lagerwaard
- Department of Radiation Oncology, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Jaap Zindler
- Department of Radiation Oncology, Haaglanden Medical Centre, Den Haag, The Netherlands
- Holland Proton Therapy Centre, Delft, The Netherlands
| | - George Hajdok
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Aaron D Ward
- Department of Medical Biophysics, Western University, London, ON, Canada
- Gerald C. Baines Centre, London Health Sciences Centre, London, ON, Canada
- Department of Oncology, Western University, London, ON, Canada
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Yardimci AH, Kocak B, Sel I, Bulut H, Bektas CT, Cin M, Dursun N, Bektas H, Mermut O, Yardimci VH, Kilickesmez O. Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI. Jpn J Radiol 2023; 41:71-82. [PMID: 35962933 DOI: 10.1007/s11604-022-01325-7] [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: 06/22/2022] [Accepted: 08/02/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC. MATERIALS AND METHODS Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal-Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1-3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA). RESULTS Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity, and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%, and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model. CONCLUSIONS ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.
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Affiliation(s)
- Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey.
| | - Ipek Sel
- Department of Radiology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Hasan Bulut
- Department of Radiology, University of Health Sciences, Dr. Sami Ulus Maternity and Children Research and Training Hospital, Ankara, Turkey
| | - Ceyda Turan Bektas
- Department of Radiology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Merve Cin
- Department of Pathology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, Turkey
| | - Nevra Dursun
- Department of Pathology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Hasan Bektas
- Department of General Surgery, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Ozlem Mermut
- Department of Radiation Oncology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, Turkey
| | | | - Ozgur Kilickesmez
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
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Wang L, Wu X, Tian R, Ma H, Jiang Z, Zhao W, Cui G, Li M, Hu Q, Yu X, Xu W. MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy. Front Oncol 2023; 13:1133008. [PMID: 36925913 PMCID: PMC10013156 DOI: 10.3389/fonc.2023.1133008] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
Objectives To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT). Methods Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC). Results There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model. Conclusion MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.
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Affiliation(s)
- Likun Wang
- Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,Department of Molecular Imaging and Nuclear Medicine, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Department of Ultrasound Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Xueliang Wu
- Graduate School, Tianjin Medical University, Tianjin, China.,Department of Gastrointestinal Surgery, Tianjin Medical University Nankai Hospital, Tianjin, China
| | - Ruoxi Tian
- Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Hongqing Ma
- Department of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zekun Jiang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Weixin Zhao
- College of Computer Science, Sichuan University, Chengdu, China
| | - Guoqing Cui
- Medical Image Center, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Meng Li
- Graduate School, Hebei North University, Zhangjiakou, China
| | - Qinsheng Hu
- Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiangyang Yu
- Department of Gastrointestinal Surgery, Tianjin Medical University Nankai Hospital, Tianjin, China
| | - Wengui Xu
- Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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