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Cited by in CrossRef
For: Wang PP, Deng CL, Wu B. Magnetic resonance imaging-based artificial intelligence model in rectal cancer. World J Gastroenterol 2021; 27(18): 2122-2130 [PMID: 34025068 DOI: 10.3748/wjg.v27.i18.2122]
URL: https://www.wjgnet.com/1007-9327/full/v27/i18/2122.htm
Number Citing Articles
1
Cristian-Constantin Volovat, Dragos-Viorel Scripcariu, Diana Boboc, Simona-Ruxandra Volovat, Ingrid-Andrada Vasilache, Corina Ursulescu-Lupascu, Liliana Gheorghe, Luiza-Maria Baean, Constantin Volovat, Viorel Scripcariu. Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological DataDiagnostics 2024; 14(6): 625 doi: 10.3390/diagnostics14060625
2
Gregor Duwe, Dominique Mercier, Crispin Balthasar Wiesmann, Markus Junker, Axel Haferkamp, Andreas Dengel, Thomas Höfner. Technologien und Technologiemanagement im Gesundheitswesen2024; : 699 doi: 10.1007/978-3-658-43860-9_36
3
Bhamini Vadhwana, Munir Tarazi, Vanash Patel. The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-AnalysisDiagnostics 2023; 13(20): 3267 doi: 10.3390/diagnostics13203267
4
Markus von Wardenburg, Johannes Wessling. Die multiparametrische MRT zum Staging des Rektumkarzinoms – eine ÜbersichtRadiopraxis 2024; 17(01): 7 doi: 10.1055/a-2102-8157
5
Lingkai Cai, Xiao Yang, Jie Yu, Qiang Shao, Gongcheng Wang, Baorui Yuan, Juntao Zhuang, Kai Li, Qikai Wu, Peikun Liu, Ruixi Yu, Qiang Cao, Pengchao Li, Xueying Sun, Yuan Zou, Xue Fu, Xiangming Fang, Chunxiao Chen, Qiang Lu. Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical studyScientific Reports 2025; 15(1) doi: 10.1038/s41598-024-82909-3
6
Yumeng Zhang, Huaqing Tan, Bin Huang, Xinjian Guo, Yuntai Cao. Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancerAbdominal Radiology 2024; 50(3): 1069 doi: 10.1007/s00261-024-04556-6
7
Abdul Wahab, Muhammad Suhail, Tatiana Eggers, Khurram Shehzad, Ozioma Udochukwu Akakuru, Zahoor Ahmad, Zhichao Sun, M. Zubair Iqbal, Xiangdong Kong. Innovative perspectives on metal free contrast agents for MRI: Enhancing imaging efficacy, and AI-driven future diagnosticsActa Biomaterialia 2025; 193: 83 doi: 10.1016/j.actbio.2025.01.005
8
Zhiqiang Bai, Lumin Xu, Zujun Ding, Yi Cao, Zepeng Wang, Wenjie Yang, Wei Xu, Hang Li. Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysisEuropean Radiology 2025;  doi: 10.1007/s00330-025-11519-y
9
Afsal Latheef Tayyil Purayil, Rahul M Joseph, Arjun Raj, Aswathy Kooriyattil, Nihala Jabeen, Saima Fazila Beevi, Najiyah Lathief, Fasil Latheif. Role of Artificial Intelligence in MRI-Based Rectal Cancer Staging: A Systematic ReviewCureus 2024;  doi: 10.7759/cureus.76185
10
Qiao-yi Huang, Hui-da Zheng, Bin Xiong, Qi-ming Huang, Kai Ye, Shu Lin, Jian-hua Xu. Preoperative prediction of multiple biological characteristics in colorectal cancer using MRI and machine learningHeliyon 2025; 11(2): e41852 doi: 10.1016/j.heliyon.2025.e41852
11
Yin Li, Shuang Li, Ruolin Xiao, Xi Li, Yongju Yi, Liangyou Zhang, You Zhou, Yun Wan, Chenhua Wei, Liming Zhong, Wei Yang, Lin Yao. A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancerFrontiers in Oncology 2025; 15 doi: 10.3389/fonc.2025.1496820
12
Francesca Maccioni, Ludovica Busato, Alessandra Valenti, Sara Cardaccio, Alessandro Longhi, Carlo Catalano. Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future ProspectivesDiagnostics 2023; 13(14): 2410 doi: 10.3390/diagnostics13142410
13
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
14
Zhe Zhang, Xiawei Wei. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapySeminars in Cancer Biology 2023; 90: 57 doi: 10.1016/j.semcancer.2023.02.005
15
Minsung Kim, Taeyong Park, Bo Young Oh, Min Jeong Kim, Bum-Joo Cho, Il Tae Son. Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic reviewAnnals of Coloproctology 2024; 40(1): 13 doi: 10.3393/ac.2023.00892.0127
16
Joseph Do Woong Choi, Talia Shepherd, Amy Cao, Toufic El‐Khoury, Nimalan Pathma‐Nathan, James Wei Tatt Toh. Is centralization for rectal cancer surgery necessary?Colorectal Disease 2024; 26(9): 1753 doi: 10.1111/codi.17119
17
Mengze Xu, Zhiyi Chen, Junxiao Zheng, Qi Zhao, Zhen Yuan. Artificial intelligence-aided optical imaging for cancer theranosticsSeminars in Cancer Biology 2023; 94: 62 doi: 10.1016/j.semcancer.2023.06.003
18
Stephanie Taha-Mehlitz, Silvio Däster, Laura Bach, Vincent Ochs, Markus von Flüe, Daniel Steinemann, Anas Taha. Modern Machine Learning Practices in Colorectal Surgery: A Scoping ReviewJournal of Clinical Medicine 2022; 11(9): 2431 doi: 10.3390/jcm11092431
19
Cristian-Constantin Volovat, Dragos-Viorel Scripcariu, Diana Boboc, Simona-Ruxandra Volovat, Ingrid-Andrada Vasilache, Corina Lupascu-Ursulescu, Liliana Gheorghe, Luiza-Maria Baean, Constantin Volovat, Viorel Scripcariu. Predicting the Feasibility of Curative Resection in Low Rectal Cancer: Insights from a Prospective Observational Study on Preoperative Magnetic Resonance Imaging AccuracyMedicina 2024; 60(2): 330 doi: 10.3390/medicina60020330
20
Gregor Duwe, Dominique Mercier, Crispin Wiesmann, Verena Kauth, Kerstin Moench, Markus Junker, Christopher C. M. Neumann, Axel Haferkamp, Andreas Dengel, Thomas Höfner. Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncologyCancer Medicine 2024; 13(12) doi: 10.1002/cam4.7398
21
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