1. 1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3): 229-63. [DOI:10.3322/caac.21834]

2. Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, Rödel C, Cervantes A, et al. (2017) Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology, 28(suppl_4): iv22-iv40. [DOI:10.1093/annonc/mdx224]

3. Gelsomino F, Barbolini M, Spallanzani A, Pugliese G, Cascinu S (2016) The evolving role of microsatellite instability in colorectal cancer: A review. Cancer Treat Rev, 51: 19-26. [DOI:10.1016/j.ctrv.2016.10.005]

4. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. (2015) PD-1 blockade in tumors with mismatch-repair deficiency. The New England Journal of Medicine, 372(26): 2509-20.

5. André T, Shiu K-K, Kim TW, Jensen BV, Jensen LH, Punt C, et al. (2020) Pembrolizumab in microsatellite-instability-high advanced colorectal cancer. The New England journal of medicine, 383(23): 2207-18. [DOI:10.1056/NEJMoa2017699]

6. Trojan J, Stintzing S, Haase O, Koch C, Ziegler P, Demes M, et al. (2021) Complete pathological response after neoadjuvant short-course immunotherapy with ipilimumab and nivolumab in locally advanced MSI-H/dMMR rectal cancer. Oncologist, 26(12): e2110-e4. [DOI:10.1002/onco.13955]

7. Benson AB, Venook AP, Al-Hawary MM, Cederquist L, Chen Y-J, Ciombor KK, et al. (2018) Rectal cancer, version 2.2018, NCCN clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network : JNCCN, 16(7): 874-901. [DOI:10.6004/jnccn.2018.0061]

8. Gilson P, Merlin J-L, Harlé A (2021) Detection of microsatellite instability: State of the art and future applications in circulating tumour DNA (ctDNA). Cancers (Basel), 13(7): 1491. [DOI:10.3390/cancers13071491]

9. Russano M, Napolitano A, Ribelli G, Iuliani M, Simonetti S, Citarella F, et al. (2020) Liquid biopsy and tumor heterogeneity in metastatic solid tumors: the potentiality of blood samples. J Exp Clin Cancer Res, 39(1): 95. [DOI:10.1186/s13046-020-01601-2]

10. Styk J, Pös Z, Pös O, Radvanszky J, Turnova EH, Buglyó G, et al. (2023) Microsatellite instability assessment is instrumental for predictive, preventive and personalised medicine: status quo and outlook. EPMA J, 14(1): 143-65. [DOI:10.1007/s13167-023-00312-w]

11. Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, et al. (2020) Introduction to radiomics. J Nucl Med, 61(4): 488-95. [DOI:10.2967/jnumed.118.222893]

12. Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. (2019) The applications of radiomics in precision diagnosis and treatment of oncology: Opportunities and challenges. Theranostics, 9(5): 1303-22. [DOI:10.7150/thno.30309]

13. Beets-Tan RGH, Lambregts DMJ, Maas M, Bipat S, Barbaro B, Curvo-Semedo L, et al. (2018) Magnetic resonance imaging for clinical management of rectal cancer: Updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. European radiology, 28(4): 1465-75. [DOI:10.1007/s00330-017-5026-2]

14. Shaish H, Aukerman A, Vanguri R, Spinelli A, Armenta P, Jambawalikar S, et al. (2020) Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. European radiology, 30(11): 6263-73. [DOI:10.1007/s00330-020-06968-6]

15. Liu Z, Zhang J, Wang H, Chen X, Song J, Xu D, et al. (2024) MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer. J Robot Surg, 18(1): 229. [DOI:10.1007/s11701-024-01978-8]

16. Ao W, Wu S, Wang N, Mao G, Wang J, Hu J, et al. (2025) Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer. Scientific Reports, 15(1): 12089. [DOI:10.1038/s41598-025-96618-y]

17. Liu J, Sun L, Lu X, Geng Y, Zhang Z (2023) Development and validation of a stacking nomogram for predicting regional lymph node metastasis status in rectal cancer via deep learning and hand-crafted radiomics. International Journal of Radiation Research, 21(2): 267-74.

18. Liu J, Liu K, Cao F, Hu P, Bi F, Liu S, et al. (2025) MRI-based radiomic nomogram for predicting disease-free survival in patients with locally advanced rectal cancer. Abdominal Radiology (New York), 50(6): 2388-400. [DOI:10.1007/s00261-024-04710-0]

19. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372: n71. [DOI:10.1136/bmj.n71]

20. Wang Q, Xu J, Wang A, Chen Y, Wang T, Chen D, et al. (2023) Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer. La radiologia medica, 128(2): 136-48. [DOI:10.1007/s11547-023-01593-x]

21. Capello Ingold G, Martins Da Fonseca J, Kolenda Zloić S, Verdan Moreira S, Kago Marole K, Finnegan E, et al. (2025) Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis. Abdominal Radiology, 50(11): 5148-61. [DOI:10.1007/s00261-025-04981-1]

22. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 14(12): 749-62. [DOI:10.1038/nrclinonc.2017.141]

23. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 155(8): 529-36. [DOI:10.7326/0003-4819-155-8-201110180-00009]

24. Li Z, Dai H, Liu Y, Pan F, Yang Y, Zhang M (2021) Radiomics analysis of multi-sequence MR images for predicting microsatellite instability status preoperatively in rectal cancer. Frontiers in Oncology, 11: 697497. [DOI:10.3389/fonc.2021.697497]

25. Zhang W, Huang Z, Zhao J, He D, Li M, Yin H, et al. (2021) Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer. Annals of Translational Medicine, 9(2): 134. [DOI:10.21037/atm-20-7673]

26. Zhang W, Yin H, Huang Z, Zhao J, Zheng H, He D, et al. (2021) Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer. Cancer Medicine, 10(12): 4164‐73. [DOI:10.1002/cam4.3957]

27. Jing G, Chen Y, Ma X, Li Z, Lu H, Xia Y, et al. (2022) Predicting mismatch‐repair status in rectal cancer using multiparametric MRI‐based radiomics models: A preliminary study. BioMed Research International, 2022(1): 6623574. [DOI:10.1155/2022/6623574]

28. Jin J, Wang H, Chen Y (2023) Predictive value of multimodal magnetic resonance imaging based radiomics model for microsatellite instability of rectal cancer. Chinese Journal of Digestive Surgery, 22(6): 779-87.

29. Li Z, Zhang J, Zhong Q, Feng Z, Shi Y, Xu L, et al. (2023) Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study. European Radiology, 33(3): 1835-43. [DOI:10.1007/s00330-022-09160-0]

30. Xiang S, Zheng LB, Zhu L, Gao Y, Wang DS, Liu SL, et al. (2023) Radiomics-based prediction of microsatellite instability in stage II and III rectal cancer patients based on T2WI MRI and diffusion-weighted imaging. Chinese Journal of Digestive Surgery, 61(9): 782‐7.

31. Zhang Y, Liu J, Wu C, Peng J, Wei Y, Cui S (2023) Preoperative prediction of microsatellite instability in rectal cancer using five machine learning algorithms based on multiparametric MRI radiomics. Diagnostics, 13(2): 269. [DOI:10.3390/diagnostics13020269]

32. Cai Z, Xu Z, Chen Y, Zhang R, Guo B, Chen H, et al. (2024) Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: a multicenter study. International Journal of Surgery (London, England), 110(7): 4310-9. [DOI:10.1097/JS9.0000000000001335]

33. Xing X, Li D, Peng J, Shu Z, Zhang Y, Song Q (2024) A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer. Scientific Reports, 14(1): 11760. [DOI:10.1038/s41598-024-62584-0]

34. Wang Y, Xie B, Wang K, Zou W, Liu A, Xue Z, et al. (2025) Multi-parametric MRI habitat radiomics based on interpretable machine learning for preoperative assessment of microsatellite instability in rectal cancer. Academic Radiology, 32(7): 3975-88. [DOI:10.1016/j.acra.2025.02.009]

35. Yao X, Deng S, Han X, Huang D, Cao Z, Ning X, et al. (2025) Deep learning algorithm‑based MRI radiomics and pathomics for predicting microsatellite instability status in rectal cancer: a multicenter study. Academic Radiology, 32(4): 1934‐45. [DOI:10.1016/j.acra.2024.09.008]

36. Zheng C-Y, Zhang J-M, Lin Q-S, Lian T, Shi L-P, Chen J-Y, et al. (2025) Noninvasive prediction of microsatellite instability in stage II/III rectal cancer using dynamic contrast-enhanced magnetic resonance imaging radiomics. World Journal of Gastrointestinal Oncology, 17(8): 108362. [DOI:10.4251/wjgo.v17.i8.108362]

37. Ma W, Hou C, Yang M, Wei Y, Mao J, Guan L, et al. (2025) Different MRI-based radiomics machine learning models to predict CD3+ tumor-infiltrating lymphocytes in rectal cancer. Frontiers in Oncology, 15: 1509207. [DOI:10.3389/fonc.2025.1509207]

38. Valizadeh P, Jannatdoust P, Pahlevan-Fallahy MT, Hassankhani A, Amoukhteh M, Bagherieh S, et al. (2025) Diagnostic accuracy of radiomics and artificial intelligence models in diagnosing lymph node metastasis in head and neck cancers: a systematic review and meta-analysis. Neuroradiology, 67(2): 449-67. [DOI:10.1007/s00234-024-03485-x]

39. Charilaou P, Battat R (2022) Machine learning models and over-fitting considerations. World Journal of Gastroenterology, 28(5): 605-7. [DOI:10.3748/wjg.v28.i5.605]

40. Zabor EC, Reddy CA, Tendulkar RD, Patil S (2022) Logistic regression in clinical studies. Int J Radiat Oncol, Biol, Phys, 112(2): 271-7. [DOI:10.1016/j.ijrobp.2021.08.007]

41. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2): 328-38. [DOI:10.1148/radiol.2020191145]

42. Whybra P, Zwanenburg A, Andrearczyk V, Schaer R, Apte AP, Ayotte A, et al. (2024) The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights. Radiology, 310(2): e231319. [DOI:10.1148/radiol.231319]

43. Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, et al. (2021) Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med, 4(1): 65. [DOI:10.1038/s41746-021-00438-z]

44. Li M, Jiang Y, Zhang Y, Zhu H (2023) Medical image analysis using deep learning algorithms. Front Public Health, 11: 1273253. [DOI:10.3389/fpubh.2023.1273253]

45. Viswam D, Abraham Francis A, Dawale S, Kanakaraj R, Sharma Y, Karki L, et al. (2025) Artificial intelligence in radiology and diagnostic imaging. Bioinformation, 21(7): 1891-4. [DOI:10.6026/973206300211891]

46. Cheng CH, Shi S-S (2025) Artificial intelligence in cancer: applications, challenges, and future perspectives. Mol Cancer, 24(1): 274. [DOI:10.1186/s12943-025-02450-3]