Boland CR, Goel A (2010) Microsatellite instability in colorectal cancer. Gastroenterology 138(6):2073–2087
Sinicrope FA, Sargent DJ (2012) Molecular pathways: microsatellite instability in colorectal cancer: prognostic, predictive, and therapeutic implications. Clin Cancer Res 18(6):1506–1512
Article PubMed PubMed Central Google Scholar
Andre T, Shiu K-K, Kim TW, Jensen BV, Jensen LH, Punt CJ, Smith DM, Garcia-Carbonero R, Benavides M, Gibbs P (2020) Pembrolizumab versus chemotherapy for microsatellite instability-high/mismatch repair deficient metastatic colorectal cancer: the phase 3 KEYNOTE-177 study. American Society of Clinical Oncology, Alexandria
Dudley JC, Lin M-T, Le DT, Eshleman JR (2016) Microsatellite instability as a biomarker for PD-1 blockade. Clin Cancer Res 22(4):813–820
Berardinelli GN, Duraes R, Mafra da Costa A, Bragagnoli A, Antônio de Oliveira M, Pereira R, Scapulatempo-Neto C, Guimaraes DP, Reis RM (2022) Association of microsatellite instability (MSI) status with the 5-year outcome and genetic ancestry in a large Brazilian cohort of colorectal cancer. Eur J Hum Genet 30(7):824–832
Article PubMed PubMed Central Google Scholar
Benson AB, Venook AP, Al-Hawary MM, Arain MA, Chen Y-J, Ciombor KK, Cohen S, Cooper HS, Deming D, Farkas L (2021) Colon cancer, version 2.2021, NCCN clinical practice guidelines in oncology. J Natl Compr Cancer Netw 19(3):329–359
Sun BL (2021) Current microsatellite instability testing in management of colorectal cancer. Clin Colorectal Cancer 20(1):e12–e20
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures. Data Radiol 278(2):563–577
Pei Q, Yi X, Chen C, Pang P, Fu Y, Lei G, Chen C, Tan F, Gong G, Li Q (2022) Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer. Eur Radiol 32:714–724
Chen X, He L, Li Q, Liu L, Li S, Zhang Y, Liu Z, Huang Y, Mao Y, Chen X (2023) Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm–enhanced artificial neural network–based CT radiomics signature. Eur Radiol 33(1):11–22
Li M, Xu G, Cui Y, Wang M, Wang H, Xu X, Duan S, Shi J, Feng F (2023) CT-based radiomics nomogram for the preoperative prediction of microsatellite instability and clinical outcomes in colorectal cancer: a multicentre study. Clin Radiol 78(10):e741–e751
Fan S, Li X, Cui X, Zheng L, Ren X, Ma W, Ye Z (2019) Computed tomography-based radiomic features could potentially predict microsatellite instability status in stage II colorectal cancer: a preliminary study. Acad Radiol 26(12):1633–1640
Golia Pernicka JS, Gagniere J, Chakraborty J, Yamashita R, Nardo L, Creasy JM, Petkovska I, Do RR, Bates DD, Paroder V (2019) Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation. Abdom Radiol 44:3755–3763
Ying M, Pan J, Lu G, Zhou S, Fu J, Wang Q, Wang L, Hu B, Wei Y, Shen J (2022) Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer. BMC Cancer 22(1):1–13
Wang Q, Xu J, Wang A, Chen Y, Wang T, Chen D, Zhang J, Brismar TB (2023) Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer. Radiol Med (Torino) 128(2):136–148
Chen X, Wang X, Zhang K, Fung K-M, Thai TC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y (2022) Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal 79:102444
Article PubMed PubMed Central Google Scholar
Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380(14):1347–1358
Cao W, Hu H, Guo J, Qin Q, Lian Y, Li J, Wu Q, Chen J, Wang X, Deng Y (2023) CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study. J Transl Med 21(1):1–10
Zhang W, Yin H, Huang Z, Zhao J, Zheng H, He D, Li M, Tan W, Tian S, Song B (2021) Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer. Cancer Med 10(12):4164–4173
Article PubMed PubMed Central Google Scholar
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:201011929
Jiang X, Zhao H, Saldanha OL, Nebelung S, Kuhl C, Amygdalos I, Lang SA, Wu X, Meng X, Truhn D (2023) An MRI deep learning model predicts outcome in rectal cancer. Radiology 307(5):e222223
Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211
Van Calster B, Wynants L, Verbeek JF, Verbakel JY, Christodoulou E, Vickers AJ, Roobol MJ, Steyerberg EW (2018) Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol 74(6):796–804
Article PubMed PubMed Central Google Scholar
Bilal M, Raza SEA, Azam A, Graham S, Ilyas M, Cree IA, Snead D, Minhas F, Rajpoot NM (2021) Novel deep learning algorithm predicts the status of molecular pathways and key mutations in colorectal cancer from routine histology images. MedRxiv:2021.2001. 2019.21250122
Echle A, Grabsch HI, Quirke P, van den Brandt PA, West NP, Hutchins GG, Heij LR, Tan X, Richman SD, Krause J (2020) Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 159(4):1406–1416
Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B, Higgins J, Rubin DL, Shen J (2021) Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol 22(1):132–141
Lee SH, Song IH, Jang HJ (2021) Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 149(3):728–740
Liu X, Guo S, Zhang H, He K, Mu S, Guo Y, Li X (2019) Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network. Med Phys 46(8):3532–3542
Dou M, Chen Z, Tang Y, Sheng L, Zhou J, Wang X, Yao Y (2023) Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network. Med Biol Eng Compu 61(9):2379–2389
Li D, Wang J, Yang J, Zhao J, Yang X, Cui Y, Zhang K (2023) RTAU-Net: A novel 3D rectal tumor segmentation model based on dual path fusion and attentional guidance. Comput Methods Programs Biomed 242:107842
Xie Y, Zhang J, Xia Y, Wu Q (2022) Unimiss: universal medical self-supervised learning via breaking dimensionality barrier. In: European conference on computer vision. Springer, pp 558–575
Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48–62
Hu W, Li X, Li C, Li R, Jiang T, Sun H, Huang X, Grzegorzek M, Li X (2023) A state-of-the-art survey of artificial neural networks for whole-slide image analysis: from popular convolutional neural networks to potential visual transformers. Comput Biol Med 161:107034
He K, Gan C, Li Z, Rekik I, Yin Z, Ji W, Gao Y, Wang Q, Zhang J, Shen D (2023) Transformers in medical image analysis. Intell Med 3(1):59–78
Matsoukas C, Haslum JF, Söderberg M, Smith K (2021) Is it time to replace cnns with transformers for medical images? arXiv preprint arXiv:210809038
Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, Fu H (2023) Transformers in medical imaging: a survey. Med Image Anal 88:102802
Steiner A, Kolesnikov A, Zhai X, Wightman R, Uszkoreit J, Beyer L (2021) How to train your vit? Data, augmentation, and regularization in vision transformers. arXiv preprint arXiv:210610270
Lu Z, Xie H, Liu C, Zhang Y (2022) Bridging the gap between vision transformers and convolutional neural networks on small datasets. Adv Neural Inf Process Syst 35:14663–14677
Zhou Q, Ye S, Wen M, Huang Z, Ding M, Zhang X (2022) Multi-modal medical image fusion based on densely-connected high-resolution CNN and hybrid transformer. Neural Comput Appl 34(24):21741–21761
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