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For: Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: Current update and future perspectives. World J Gastroenterol 2021; 27(32): 5306-5321 [PMID: 34539134 DOI: 10.3748/wjg.v27.i32.5306]
URL: https://www.wjgnet.com/2644-3260/full/v27/i32/5306.htm
Number Citing Articles
1
Ruobing Hu, Xiuling Li, Xiaomin Zhou, Songze Ding. Development and validation of a competitive risk model in patients with rectal cancer: based on SEER databaseEuropean Journal of Medical Research 2023; 28(1) doi: 10.1186/s40001-023-01357-3
2
Lin Peng, Dongqing Wang, Zijian Zhuang, Xingchi Chen, Jing Xue, Haitao Zhu, Lirong Zhang. Preoperative Noninvasive Evaluation of Tumor Budding in Rectal Cancer Using Multiparameter MRI RadiomicsAcademic Radiology 2024; 31(6): 2334 doi: 10.1016/j.acra.2023.11.023
3
Yu-quan Wu, Rui-zhi Gao, Peng Lin, Rong Wen, Hai-yuan Li, Mei-yan Mou, Feng-huan Chen, Fen Huang, Wei-jie Zhou, Hong Yang, Yun He, Ji Wu. An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancerBMC Medical Imaging 2022; 22(1) doi: 10.1186/s12880-022-00813-6
4
Elisavet Stamoulou, Constantinos Spanakis, Georgios C. Manikis, Georgia Karanasiou, Grigoris Grigoriadis, Theodoros Foukakis, Manolis Tsiknakis, Dimitrios I. Fotiadis, Kostas Marias. Harmonization Strategies in Multicenter MRI-Based RadiomicsJournal of Imaging 2022; 8(11): 303 doi: 10.3390/jimaging8110303
5
Meng Liang, Xiaohong Ma, Leyao Wang, Dengfeng Li, Sicong Wang, Hongmei Zhang, Xinming Zhao. Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgeryCancer Imaging 2022; 22(1) doi: 10.1186/s40644-022-00485-z
6
Bo Deng, Qian Wang, Yuanqing Liu, Yanwei Yang, Xiaolong Gao, Hui Dai. A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancerAbdominal Radiology 2024; 49(6): 1850 doi: 10.1007/s00261-023-04164-w
7
Jianguo Zhou, Mingli Zhao, Zhou Yang, Liping Chen, Xiaoli Liu. Exploring the Value of MRI Measurement of Hippocampal Volume for Predicting the Occurrence and Progression of Alzheimer’s Disease Based on Artificial Intelligence Deep Learning Technology and Evidence-Based Medicine Meta-AnalysisJournal of Alzheimer's Disease 2024; 97(3): 1275 doi: 10.3233/JAD-230733
8
Thomas DeSilvio, Jacob T. Antunes, Kaustav Bera, Prathyush Chirra, Hoa Le, David Liska, Sharon L. Stein, Eric Marderstein, William Hall, Rajmohan Paspulati, Jayakrishna Gollamudi, Andrei S. Purysko, Satish E. Viswanath. Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader studyFrontiers in Medicine 2023; 10 doi: 10.3389/fmed.2023.1149056
9
Joseph P. Doyle, Pranav H. Patel, Nikoletta Petrou, Joshua Shur, Matthew Orton, Sacheen Kumar, Ricky H. Bhogal. Radiomic applications in upper gastrointestinal cancer surgeryLangenbeck's Archives of Surgery 2023; 408(1) doi: 10.1007/s00423-023-02951-z
10
Amir L. Rifi, Febe Geirnaert, Camille Raets, Chaïmae El Aisati, Inès Dufait, Mark De Ridder, Kurt Barbé. Murine in vivo tumor model to explain the interpretability of radiomic features.2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2023; : 1 doi: 10.1109/MeMeA57477.2023.10171852
11
Filippo Crimì, Carlo D’Alessandro, Chiara Zanon, Francesco Celotto, Christian Salvatore, Matteo Interlenghi, Isabella Castiglioni, Emilio Quaia, Salvatore Pucciarelli, Gaya Spolverato. A Machine Learning Model Based on MRI Radiomics to Predict Response to Chemoradiation Among Patients with Rectal CancerLife 2024; 14(12): 1530 doi: 10.3390/life14121530
12
Alessia Pepe, Filippo Crimì, Federica Vernuccio, Giulio Cabrelle, Amalia Lupi, Chiara Zanon, Sebastiano Gambato, Anna Perazzolo, Emilio Quaia. Medical Radiology: Current ProgressDiagnostics 2023; 13(14): 2439 doi: 10.3390/diagnostics13142439
13
Yana A. Dayneko, Tatiana P. Berezovskaya, Oleg Mirzeabasov, Sergey Olegovich Starkov, Sofiya A. Myalina, Aleksey A. Nevolskikh, Sergey А. Ivanov, Andrey D. Kaprin. Prediction of the effectiveness of neoadjuvant chemoradiotherapy in patients with colorectal cancer based on a texture analysis of pretreatment T2-WI magnetic resonance imaging.Digital Diagnostics 2024;  doi: 10.17816/DD628304
14
Shuai Fu, Ting Xia, Zhenhui Li, Junying Zhu, Zhiming Zeng, Biao Li, Sidong Xie, Wenru Li, Peiyi Xie. Baseline MRI-based radiomics improving the recurrence risk stratification in rectal cancer patients with negative carcinoembryonic antigen: A multicenter cohort studyEuropean Journal of Radiology 2025; 182: 111839 doi: 10.1016/j.ejrad.2024.111839
15
Satvik Tripathi, Azadeh Tabari, Arian Mansur, Harika Dabbara, Christopher P. Bridge, Dania Daye. From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic CancerDiagnostics 2024; 14(2): 174 doi: 10.3390/diagnostics14020174
16
Sikhumbuzo Z. Mbatha, Rupert C. Ecker, Zodwa Dlamini. Colorectal Cancer Treatment Resistance2025; : 323 doi: 10.1016/B978-0-443-33475-7.00005-6
17
Enhancing the Identification of Brain Tumours Using the CNN Ensemble ModelMalaysian Journal of Science and Advanced Technology 2024; : 338 doi: 10.56532/mjsat.v4i3.264
18
Xueting Qu, Liang Zhang, Weina Ji, Jizheng Lin, Guohua Wang. Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomicsFrontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.1267838
19
Zheng-Hao Cai, Qun Zhang, Zhan-Wei Fu, Abraham Fingerhut, Jing-Wen Tan, Lu Zang, Feng Dong, Shu-Chun Li, Shi-Lin Wang, Jun-Jun Ma. Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosisWorld Journal of Gastroenterology 2023; 29(3): 536-548 doi: 10.3748/wjg.v29.i3.536
20
Ameesha Paliwal, Kevin Faust, Azhar Alshoumer, Phedias Diamandis. Standardizing analysis of intra‐tumoral heterogeneity with computational pathologyGenes, Chromosomes and Cancer 2023; 62(9): 526 doi: 10.1002/gcc.23146
21
Ruilong Zong, Xijuan Ma, Yibing Shi, Li Geng. Can Machine Learning Models Based on Computed Tomography Radiomics and Clinical Characteristics Provide Diagnostic Value for Epstein-Barr Virus–Associated Gastric Cancer?Journal of Computer Assisted Tomography 2024; 48(6): 859 doi: 10.1097/RCT.0000000000001636
22
K. A. Zamyatina, M. V. Godzenko, G. G. Kаrmаzаnovsky, A. Sh. Revishvili. Radiomics in liver and pancreatic disorders: a reviewAnnaly khirurgicheskoy gepatologii = Annals of HPB Surgery 2022; 27(1): 40 doi: 10.16931/1995-5464.2022-1-40-47
23
Jiali Lyu, Zhenzhu Pang, Jihong Sun. Radiomics prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancerRadiology Science 2024; 3(1) doi: 10.15212/RADSCI-2023-0005
24
Niall J. O’Sullivan, Michael E. Kelly. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future DirectionsCurrent Oncology 2023; 30(5): 4936 doi: 10.3390/curroncol30050372
25
Kylene M. Harold, William M. MacCuaig, Jennifer Holter-Charkabarty, Kirsten Williams, Kaitlyn Hill, Alex X. Arreola, Malika Sekhri, Steven Carter, Jorge Gomez-Gutierrez, George Salem, Girish Mishra, Lacey R. McNally. Advances in Imaging of Inflammation, Fibrosis, and Cancer in the Gastrointestinal TractInternational Journal of Molecular Sciences 2022; 23(24): 16109 doi: 10.3390/ijms232416109
26
Wenlong Ming, Yanhui Zhu, Fuyu Li, Yunfei Bai, Wanjun Gu, Yun Liu, Xiao Sun, Xiaoan Liu, Hongde Liu. Identifying Associations between DCE-MRI Radiomic Features and Expression Heterogeneity of Hallmark Pathways in Breast Cancer: A Multi-Center Radiogenomic StudyGenes 2022; 14(1): 28 doi: 10.3390/genes14010028
27
Kriti Das, Maanvi Paltani, Pankaj Kumar Tripathi, Rajnish Kumar, Saniya Verma, Subodh Kumar, Chakresh Kumar Jain. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancerExploration of Targeted Anti-tumor Therapy 2023; 4(6): 1286 doi: 10.37349/etat.2023.00197
28
Qiong Ma, Yinqiao Yi, Tiejun Liu, Xinnian Wen, Fei Shan, Feng Feng, Qinqin Yan, Jie Shen, Guang Yang, Yuxin Shi. MRI-based radiomics signature for identification of invisible basal cisterns changes in tuberculous meningitis: a preliminary multicenter studyEuropean Radiology 2022; 32(12): 8659 doi: 10.1007/s00330-022-08911-3
29
Yanqing Ma, Zheng Guan, Lifeng Qiu, Kaili Shang, Yuguo Wei, Yuan Hang. A multiparameter MRI-radiomics and clinical nomogram to predict the positive circumferential resection margin of rectal carcinomaChinese Journal of Academic Radiology 2023; 6(2): 73 doi: 10.1007/s42058-023-00118-9
30
Zhiqiang Wang, Weiran Li, Di Jin, Bing Fan. Radiomics in the Diagnosis of Gastric Cancer: Current Status and Future PerspectivesCurrent Medical Imaging Reviews 2023; 20(1) doi: 10.2174/0115734056246452231011042418
31
Meiyan Mou, Ruizhi Gao, Yuquan Wu, Peng Lin, Hongxia Yin, Fenghuan Chen, Fen Huang, Rong Wen, Hong Yang, Yun He. Endoscopic Rectal Ultrasound‐Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal CancerJournal of Ultrasound in Medicine 2024; 43(2): 361 doi: 10.1002/jum.16369
32
Jiaxuan Peng, Wei Wang, Hui Jin, Xue Qin, Jie Hou, Zhang Yang, Zhenyu Shu. Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learningBMC Cancer 2023; 23(1) doi: 10.1186/s12885-023-10855-w
33
Jia Wang, Jingjing Chen, Ruizhi Zhou, Yuanxiang Gao, Jie Li. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patientsBMC Cancer 2022; 22(1) doi: 10.1186/s12885-022-09518-z
34
Hugo C. Temperley, Niall J. O’Sullivan, Caitlin Waters, Alison Corr, Brian J. Mehigan, Grainne O’Kane, Paul McCormick, Charles Gillham, Emanuele Rausa, John O. Larkin, James F. Meaney, Ian Brennan, Michael E. Kelly. Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic ReviewThe American Surgeon™ 2024; 90(3): 445 doi: 10.1177/00031348231216494
35
Kalpesh Mody. CT Colonography for Radiographers2023; : 313 doi: 10.1007/978-3-031-30866-6_22
36
Fangnan Zhao, Fangshun Tan, Lu Tang, Zhuoying Du, Xiaoya Chen, Yuzhi Yang, Gang Zhou, Chengfu Yuan. Long Non-coding RNA DLGAP1-AS1 and DLGAP1-AS2: Two Novel Oncogenes in Multiple CancersCurrent Medicinal Chemistry 2023; 30(25): 2822 doi: 10.2174/0929867329666220919114919
37
Pak Kin Wong, In Neng Chan, Hao-Ming Yan, Shan Gao, Chi Hong Wong, Tao Yan, Liang Yao, Ying Hu, Zhong-Ren Wang, Hon Ho Yu. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireviewWorld Journal of Gastroenterology 2022; 28(45): 6363-6379 doi: 10.3748/wjg.v28.i45.6363
38
Lilang Lv, Bowen Xin, Yichao Hao, Ziyi Yang, Junyan Xu, Lisheng Wang, Xiuying Wang, Shaoli Song, Xiaomao Guo. Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CTJournal of Translational Medicine 2022; 20(1) doi: 10.1186/s12967-022-03262-5
39
Que N. N. Tran, Minh-Khang Le, Tetsuo Kondo, Takeshi Moriguchi. A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 CasesAdvances in Respiratory Medicine 2023; 91(4): 310 doi: 10.3390/arm91040025
40
Xiaowei Xing, Dongxue Li, Jiaxuan Peng, Zhenyu Shu, Yang Zhang, Qiaowei Song. A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancerScientific Reports 2024; 14(1) doi: 10.1038/s41598-024-62584-0
41
Giulia Pacella, Maria Chiara Brunese, Eleonora D’Imperio, Marco Rotondo, Andrea Scacchi, Mattia Carbone, Germano Guerra. Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and PrognosisJournal of Clinical Medicine 2023; 12(23): 7380 doi: 10.3390/jcm12237380