A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study

Boland CR, Goel A (2010) Microsatellite instability in colorectal cancer. Gastroenterology 138(6):2073–2087

Article  PubMed  Google Scholar 

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

Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

Sun BL (2021) Current microsatellite instability testing in management of colorectal cancer. Clin Colorectal Cancer 20(1):e12–e20

Article  PubMed  Google Scholar 

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures. Data Radiol 278(2):563–577

Article  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

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

Article  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Article  Google Scholar 

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

Article  PubMed  Google Scholar 

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

Google Scholar 

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

Article  Google Scholar 

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