| For: | Long ZD, Yu X, Xing ZX, Wang R. Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis. World J Gastrointest Oncol 2025; 17(1): 96598 [PMID: 39817139 DOI: 10.4251/wjgo.v17.i1.96598] |
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| URL: | https://www.wjgnet.com/1948-5204/full/v17/i1/96598.htm |
| Number | Citing Articles |
| 1 |
Yuwei Zhang. Enhancing rectal cancer liver metastasis prediction: Magnetic resonance imaging-based radiomics, bias mitigation, and regulatory considerations. World Journal of Gastrointestinal Oncology 2025; 17(2): 102151 doi: 10.4251/wjgo.v17.i2.102151
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| 2 |
A Review of Artificial Intelligence-Based Approaches for Non-Invasive Liver Disease Diagnosis. International Journal of Latest Technology in Engineering Management & Applied Science 2026; 15(2) doi: 10.51583/IJLTEMAS.2026.15020000120
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| 3 |
Christopher Mejias, Bahar Mansoori, Guilherme Moura Cunha, Joel G. Fletcher, Natally Horvat, Avinash Nehra, Aiming Lu, Achille Mileto. Challenges in rectal cancer MRI: from image acquisition to interpretation. Abdominal Radiology 2025; 51(6) doi: 10.1007/s00261-025-05268-1
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| 4 |
Zhuofu Li, Chao Sun, Xiaoxuan Wang, Song Tian, Zhaoxiang Ye. A Vision Transformer– and Radiomics-based Model for Predicting Liver Metastasis-Free Survival in Patients with Rectal Cancer. Radiology: Imaging Cancer 2026; 8(5) doi: 10.1148/rycan.250440
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| 5 |
Zhanhong Liu, Hao Yang, Lin Nie, Peng Xian, Lin Jiang, Junfan Chen, Jianru Huang, Zhengkang Yao, Tianqi Yuan. A nomogram integrating multi-channel 2.5D twins-SVT deep learning and radiomics for predicting disease-free survival in patients with locally advanced rectal cancer following primary total mesorectal excision. European Journal of Radiology 2026; 205 doi: 10.1016/j.ejrad.2026.113196
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| 6 |
Xu-Xing Ye, Hui-Heng Qu, Chao Yang, Wei-Jun Teng, Yan-Ping Chen, Jun-Mei Lin, Xiao-Bo Wang. Precision medicine in the prediction of metachronous liver metastasis in rectal cancer: Applications and challenges. World Journal of Gastrointestinal Oncology 2025; 17(4): 102469 doi: 10.4251/wjgo.v17.i4.102469
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| 7 |
Yassin Rahnama, Homayoun Pishraft-Sabet, Sara Eghbali, Faeze Salahshour, Sina Delazar, Mojtaba Sedaghat, Amir Keshvari, Alireza Kazemeini, Mohammad Reza Keramati, Mohammad Sadegh Fazeli, Behnam Behboudi, Seyed Mohsen Ahmadi-Tafti. Artificial intelligence for the prediction of synchronous and metachronous liver metastasis in colorectal cancer patients: a systematic review and meta-analysis. Abdominal Radiology 2026; 51(9) doi: 10.1007/s00261-026-05433-0
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| 8 |
Arunkumar Krishnan. Radiomics and machine learning for predicting metachronous liver metastasis in rectal cancer. World Journal of Gastrointestinal Oncology 2025; 17(4): 102324 doi: 10.4251/wjgo.v17.i4.102324
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| 9 |
Lawrence Willis Chinn, Isabelle Nemeh, Natasha R. Chinn. Artificial intelligence-enabled clinical decision support systems in preadmission testing: a scoping review of risk prediction, triage, and perioperative workflows (2020–2025). Journal of Clinical Monitoring and Computing 2026; 40(2) doi: 10.1007/s10877-025-01404-w
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