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Cited by in CrossRef
For: 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(19): 2888-2904 [PMID: 37274803 DOI: 10.3748/wjg.v29.i19.2888]
URL: https://www.wjgnet.com/1007-9327/full/v29/i19/2888.htm
Number Citing Articles
1
Zhi-Da Long, Xiao Yu, Zhi-Xiang Xing, Rui Wang. Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasisWorld Journal of Gastrointestinal Oncology 2025; 17(1): 96598 doi: 10.4251/wjgo.v17.i1.96598
2
Vincenza Granata, Roberta Fusco, Maria Chiara Brunese, Gerardo Ferrara, Fabiana Tatangelo, Alessandro Ottaiano, Antonio Avallone, Vittorio Miele, Nicola Normanno, Francesco Izzo, Antonella Petrillo. Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging AssessmentDiagnostics 2024; 14(2): 152 doi: 10.3390/diagnostics14020152
3
Brigitte Scott. Updates in BRAF V600E-Mutated Metastatic Colorectal CancerEMJ Oncology 2024; : 2 doi: 10.33590/emjoncol/JDXK9403
4
Mingzhu Yuan, Qi Long, Xufang Sun. OCTA-based research on changes of retinal microcirculation in digestive tract malignancyPhotodiagnosis and Photodynamic Therapy 2024; 49: 104270 doi: 10.1016/j.pdpdt.2024.104270
5
Haiming Zhang, Zhenyu Li, Fengtao Zhang, Hengguo Li. CT-based radiomics features for the differential diagnosis of nodular goiter and papillary thyroid carcinoma: an analysis employing propensity score matchingFrontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1465941
6
Jia Zhang, Wenxiang Huang, Yang Li, Xuan Zhang, Yong Chen, Shaohao Chen, Qiu Ming, Qing Jiang, Yingjie Xv. Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort StudyAcademic Radiology 2025; 32(7): 3788 doi: 10.1016/j.acra.2025.03.042
7
Lu-Na Bai, Lu-Xian Zhang. Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancerWorld Journal of Gastrointestinal Surgery 2024; 16(7): 2135-2144 doi: 10.4240/wjgs.v16.i7.2135
8
Qiaoling Chen, Qianwen Zhang, Zhihui Li, Shaoting Zhang, Yuwei Xia, Hao Wang, Yong Lu, Anqi Zheng, Chengwei Shao, Fu Shen. MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter studyEuropean Radiology 2025;  doi: 10.1007/s00330-025-11997-0
9
Yongna Cheng, Ziming Feng, Xiangming Wang. Construction and Value Analysis of a Prognostic Assessment Model Based on Radiomics and Genetic Data for Colorectal CancerBritish Journal of Hospital Medicine 2025; 86(3): 1 doi: 10.12968/hmed.2024.0620
10
Hao Jiang, Wei Guo, Xue Lin, Zhuo Yu, Yudie Qin, Zhongqi Sun, Hongbo Hu, Jinping Li, Linhan Zhang, Qiong Wu, Huijie Jiang. Multiparametric MRI radiomics nomogram predicts synchronous distant metastasis in rectal cancerScientific Reports 2026;  doi: 10.1038/s41598-026-35973-w
11
Shuai Ren, Bin Qin, Marcus J Daniels, Liang Zeng, Ying Tian, Zhong-Qiu Wang. Developing and validating a computed tomography radiomics strategy to predict lymph node metastasis in pancreatic cancerWorld Journal of Radiology 2025; 17(8): 109373 doi: 10.4329/wjr.v17.i8.109373
12
Jeba Karunya Ramireddy, A. Sathya, Balu Krishna Sasidharan, Amal Joseph Varghese, Arvind Sathyamurthy, Neenu Oliver John, Anuradha Chandramohan, Ashish Singh, Anjana Joel, Rohin Mittal, Dipti Masih, Kripa Varghese, Grace Rebekah, Thomas Samuel Ram, Hannah Mary T. Thomas. Can Pretreatment MRI and Planning CT Radiomics Improve Prediction of Complete Pathological Response in Locally Advanced Rectal Cancer Following Neoadjuvant Treatment?Journal of Gastrointestinal Cancer 2024; 55(3): 1199 doi: 10.1007/s12029-024-01073-z
13
Zhiyun Xu, Yijiang Lu, Fengyi Zuo, Hanlin Ding, Yipeng Feng, Xiaokang Shen, Xuming Song, Wenjie Xia, Qixing Mao, Bing Chen, Rutao Li, Hui Wang, Lin Xu, Gaochao Dong, Feng Jiang. Integration of habitat radiomics and traditional radiomic features for predicting pathological complete response in esophageal squamous cell carcinoma following neoadjuvant immunotherapy and chemotherapy: a multicenter comparative studyJournal of Translational Medicine 2026;  doi: 10.1186/s12967-025-07522-y
14
Junfeng Yan, Qiushuang Wang, Qiang Li, Jiatong Lu, Qiang Tong. Preoperative magnetic resonance imaging–based radiomics nomogram model for predicting postoperative anastomotic leakage in colorectal cancerAnnals of Coloproctology 2025; 41(6): 554 doi: 10.3393/ac.2025.00689.0098
15
Tingting Gong, Ying Gao, He Li, Jianqiu Wang, Zili Li, Qinghai Yuan. Research progress in multimodal radiomics of rectal cancer tumors and peritumoral regions in MRIAbdominal Radiology 2025; 50(12): 5677 doi: 10.1007/s00261-025-04965-1
16
I. Valle, C. Conticello, D. Ravizza, D. Lavacchi, D. Pallaoro, G.L. Grazi, D. Rossini, L. Antonuzzo. Navigating Disappearing Liver Metastases in Colorectal Cancer: A Review of Surgical and Non-Surgical ApproachesClinical Oncology 2026; 49: 103981 doi: 10.1016/j.clon.2025.103981
17
Luwen Zhang, Yubing Shen, Wentao Gu, Peng Wu. Multimodal learning in gastrointestinal diseasesGastroenterology & Endoscopy 2025; 3(4): 251 doi: 10.1016/j.gande.2025.10.001
18
Sunya Fu, Dawei Chen, Yuqin Zhang, Xiao Yu, Lu Han, Jiazi Yu, Yupeng Zheng, Liang Zhao, Yidong Xu, Ying Tan, Mian Yang. A CT-based radiomics tumor quality and quantity model to predict early recurrence after radical surgery for colorectal liver metastasesClinical and Translational Oncology 2024; 27(3): 1198 doi: 10.1007/s12094-024-03645-8
19
Gui-Wen Lyu, Tong Tong, Gen-Dong Yang, Jing Zhao, Zi-Fan Xu, Na Zheng, Zhi-Fang Zhang. Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncologyFrontiers in Medicine 2024; 11 doi: 10.3389/fmed.2024.1501652
20
Malik Laich, Mathias Brugel, Pierre Henri Conze, Marwan Abbas, Mohammed Ben Abdelghanie, Faiza Khemissa, Sylvie Kircher, Karine Le Malicot, Côme Lepage, Thomas Aparicio, Christine Hoeffel, Olivier Bouché, Claire Carlier. Association between induced organ atrophy assessed by artificial intelligence-generated automatic segmentation and efficacy of bevacizumab in combination with chemotherapy in metastatic colorectal cancerCancer Imaging 2025; 25(1) doi: 10.1186/s40644-025-00951-4
21
Joanna Urbaniec-Stompór, Maciej Michalak, Janusz Godlewski. Correlating Ultrastructural Changes in the Invasion Area of Colorectal Cancer with CT and MRI ImagingInternational Journal of Molecular Sciences 2024; 25(18): 9905 doi: 10.3390/ijms25189905
22
PelvEx Collaborative. The Utility of T2-Weighted MRI Radiomics in the Prediction of Post-Exenteration Disease Recurrence: A Multi-Centre Externally Validated Study via the PelvEx CollaborativeCancers 2025; 17(18): 3061 doi: 10.3390/cancers17183061
23
Jian-Ping Wang, Ze-Ning Zhang, Ding-Bo Shu, Ya-Nan Huang, Wei Tang, Hong-Bo Zhao, Zhen-Hua Zhao, Ji-Hong Sun. Machine learning‑based radiomics models for the prediction of metachronous liver metastases in patients with colorectal cancer:  A multimodal studyOncology Letters 2025; 30(2): 1 doi: 10.3892/ol.2025.15140
24
Nicolò Gennaro, Moataz Soliman, Amir A. Borhani, Linda Kelahan, Hatice Savas, Ryan Avery, Kamal Subedi, Tugce A. Trabzonlu, Chase Krumpelman, Vahid Yaghmai, Young Chae, Jochen Lorch, Devalingam Mahalingam, Mary Mulcahy, Al Benson, Ulas Bagci, Yuri S. Velichko. Delta Radiomics and Tumor Size: A New Predictive Radiomics Model for Chemotherapy Response in Liver Metastases from Breast and Colorectal CancerTomography 2025; 11(3): 20 doi: 10.3390/tomography11030020
25
Benjamin Keel, Aaron Quyn, David Jayne, Samuel David Relton. State-of-the-art performance of deep learning methods for pre-operative radiologic staging of colorectal cancer lymph node metastasis: a scoping reviewBMJ Open 2024; 14(12): e086896 doi: 10.1136/bmjopen-2024-086896
26
Yanjun Liu, Huanying Yin, Jiaojiao Li, Zimeng Wang, Wenjiang Wang, Shujun Cui. CT-Based radiomics and deep learning for the preoperative prediction of peritoneal metastasis in ovarian cancersAbdominal Radiology 2025;  doi: 10.1007/s00261-025-05162-w
27
Kaitlyn M. Weyman, Anai N. Kothari. Artificial Intelligence for the Treatment and Management of Colorectal Liver MetastasesClinics in Colon and Rectal Surgery 2025;  doi: 10.1055/a-2769-1413
28
Petr Tsarkov, Vladimir Balaban, Harutyun Babajanyan, Abe Fingerhut, Inna Tulina, Mingze He. Lateral pelvic lymph node positivity (LPLNP) score: predictive clinic-radiological model of lateral pelvic lymph node involvement in rectal cancer patientsInternational Journal of Colorectal Disease 2024; 39(1) doi: 10.1007/s00384-024-04717-5
29
Long Wu, Huan Wu, Chen Li, Baofang Zhang, Xiaoyun Li, Yunhuan Zhen, Haiyang Li. Radiomics in colorectal canceriRADIOLOGY 2023; 1(3): 236 doi: 10.1002/ird3.29
30
Roberto Cannella. Combined quantitative and radiomics model can predict outcomes in patients with initially unresectable colorectal liver metastases treated with chemotherapyAbdominal Radiology 2024; 50(2): 1051 doi: 10.1007/s00261-024-04529-9
31
A. Brunetti, G. M. Zaccaria, E. Sibilano, S. Marzi, A. Vidiri, V. Bevilacqua. Development and independent validation of explainable radiomics-based machine learning models for prognosis in colorectal liver metastasesFrontiers in Digital Health 2026; 7 doi: 10.3389/fdgth.2025.1752699
32
Simona Marzi, Antonello Vidiri, Anna Ianiro, Chiara Parrino, Sergio Ruggiero, Claudio Trobiani, Leonardo Teodoli, Giulio Vallati, Giovanni Trillò, Maria Ciolina, Francesca Sperati, Andrea Scarinci, Matteo Virdis, Michele Droz Dit Busset, Tommaso Stecca, Marco Massani, Giovanni Morana, Gian Luca Grazi. CT-Based Radiomics Models with External Validation for Prediction of Recurrence and Disease-Specific Mortality After Radical Surgery of Colorectal Liver MetastasesAnnals of Surgical Oncology 2025; 32(13): 9584 doi: 10.1245/s10434-025-18248-y
33
Alessio Danilo Inchingolo, Alessandra Laforgia, Angelo Michele Inchingolo, Giulia Latini, Carmen Pezzolla, Paola Nardelli, Andrea Palermo, Francesco Inchingolo, Giuseppina Malcangi, Gianna Dipalma. Rapid palate expansion's impact on nasal breathing: A systematic reviewInternational Journal of Pediatric Otorhinolaryngology 2025; 190: 112248 doi: 10.1016/j.ijporl.2025.112248
34
Yu-Yin Wang, Cui-Ping Zhang, Qing-Biao Zhang, Xing-Yan Le, Jun-Bang Feng, Chuan-Ming Li. Future directions of image-guided thermal ablation in colorectal cancer lung oligometastasesWorld Journal of Gastroenterology 2026; 32(2): 114727 doi: 10.3748/wjg.v32.i2.114727
35
Antonio Galluzzo, Ginevra Danti, Linda Calistri, Diletta Cozzi, Daniele Lavacchi, Daniele Rossini, Lorenzo Antonuzzo, Sebastiano Paolucci, Francesca Castiglione, Luca Messerini, Fabio Cianchi, Vittorio Miele. Prediction of Microsatellite Instability in Colorectal Cancer Using Two Internally Validated Radiomic ModelsTomography 2025; 11(11): 126 doi: 10.3390/tomography11110126
36
Jingui Wang, Kexin Wang, Junling Zhang, Yingchao Wu, Yong Jiang, Guowei Chen, Zhanbing Liu, Tao Wu, Yuanlian Wan, Xiaoying Wang, Xin Wang. Development of a CT radiomics model for detection of bladder invasion by colorectal carcinomaScientific Reports 2025; 15(1) doi: 10.1038/s41598-025-99222-2
37
Boqi Zhou, Huaqing Tan, Yuxuan Wang, Bin Huang, Zhijie Wang, Shihui Zhang, Xiaobo Zhu, Zhan Wang, Junlin Zhou, Yuntai Cao. A computed tomography-based radiomics prediction model for BRAF mutation status in colorectal cancerAbdominal Radiology 2025; 50(11): 5162 doi: 10.1007/s00261-025-04983-z
38
Hongwei Zhang, Kexin Wang, Shurong Liu, Guowei Chen, Yong Jiang, Yingchao Wu, Xiaocong Pang, Xiaoying Wang, Junling Zhang, Xin Wang. Development and validation of machine learning models for predicting no. 253 lymph node metastasis in left-sided colorectal cancer using clinical and CT-based radiomic featuresCancer Imaging 2025; 25(1) doi: 10.1186/s40644-025-00876-y
39
Ilya D Klabukov, Anna Smirnova, Irina Kondrasheva, Denis S Baranovskii, Elena Yatsenko. Predictive model based on magnetic resonance imaging for chemotherapy response in colorectal cancer: Toward a radiologic biopsy approachWorld Journal of Gastrointestinal Oncology 2026; 18(1): 115117 doi: 10.4251/wjgo.v18.i1.115117
40
Wen-Yu Luan, Hao Lin, Yan-Dong Miao. Recommendations for articles and reviews in colorectal cancer-related research at the year-end of 2023World Journal of Gastroenterology 2024; 30(30): 3548-3553 doi: 10.3748/wjg.v30.i30.3548
41
M.C. de Grandis, I. Baraibar, O. Prior, M. Balaguer-Montero, F. Salvà, J. Ros, M. Rodríguez-Castells, J. Tabernero, S. Lonardi, R. Perez-Lopez, E. Élez. Differentiating low tumor burden from oligometastatic disease in colorectal cancer: a call for individualized therapeutic approachesESMO Open 2025; 10(8): 105520 doi: 10.1016/j.esmoop.2025.105520
42
Francesco Celotto, Giulia Capelli, Stefania Ferrari, Marco Scarpa, Salvatore Pucciarelli, Gaya Spolverato. Application and use of artificial intelligence in colorectal cancer surgery: where are we?Artificial Intelligence Surgery 2024; 4(4): 348 doi: 10.20517/ais.2024.26
43
Ningxin Chen, Boyang Zang, Yangjia Chen, Yuki Arita. Predicting one-year post-surgical recurrence in colorectal liver metastasis using CT radiomics and machine learningPLOS One 2025; 20(8): e0330828 doi: 10.1371/journal.pone.0330828
44
Francesco Pisu, Luca Saba. Colorectal Imaging2025; : 225 doi: 10.1016/B978-0-443-29048-0.00001-X
45
Liqiang Shi, Xipeng Wang, Chengqiang Li, Yaya Bai, Yajie Zhang, Hecheng Li. Radiomics applications in the modern management of esophageal squamous cell carcinomaMedical Oncology 2025; 42(7) doi: 10.1007/s12032-025-02775-5
46
Yuhao Fan, Rong Niu, Jianxiong Gao, Yan Sun, Jinbao Feng, Yaoting Zhu, Mengyue Hu, Yunmei Shi, Yuetao Wang, Xiaonan Shao, Qianyun Wang. New Advances in Imaging-Based Preoperative Prediction of STAS in Lung Adenocarcinoma: From CT and PET/CT to Radiomics and Deep LearningAcademic Radiology 2026; 33(1): 281 doi: 10.1016/j.acra.2025.10.009
47
聪 刘. Application of Multimodal Radiomics in Assessing Neoadjuvant Therapeutic Efficacy for Breast CancerJournal of Clinical Personalized Medicine 2026; 5(01): 324 doi: 10.12677/jcpm.2026.51047
48
Tingting Hong, Heng Zhang, Qiming Zhao, Li Liu, Jun Sun, Shudong Hu, Yong Mao. A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal CancerAcademic Radiology 2025; 32(5): 2630 doi: 10.1016/j.acra.2024.12.022
49
Fei-Wen Feng, Fei-Yu Jiang, Yuan-Qing Liu, Qi Sun, Rong Hong, Chun-Hong Hu, Su Hu. Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancerEuropean Radiology 2024; 35(1): 105 doi: 10.1007/s00330-024-10918-x
50
Sara Dalmonte, Maria Adriana Cocozza, Dajana Cuicchi, Daniel Remondini, Lorenzo Faggioni, Paolo Castellucci, Andrea Farolfi, Emilia Fortunati, Alberta Cappelli, Riccardo Biondi, Arrigo Cattabriga, Gilberto Poggioli, Stefano Fanti, Gastone Castellani, Francesca Coppola, Nico Curti. Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approachHeliyon 2025; 11(1): e41404 doi: 10.1016/j.heliyon.2024.e41404
51
Monica Kassavin, Kevin J. Chang. Computed Tomography ColonographyRadiologic Clinics of North America 2025; 63(3): 405 doi: 10.1016/j.rcl.2024.09.009