Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

Article  PubMed  Google Scholar 

Benson AB, Venook AP, Al-Hawary MM, Cederquist L, Chen YJ, Ciombor KK, et al. NCCN guidelines ® insights colon cancer, version 2.2018 featured updates to the NCCN guidelines. JNCCN J Natl Compr Cancer Netw. 2018;16(4):359–69.

Article  Google Scholar 

Cheong CK, Nistala KRY, Ng CH, Syn N, Chang HSY, Sundar R, et al. Neoadjuvant therapy in locally advanced colon cancer: a meta-analysis and systematic review. J Gastrointest Oncol. 2020;11(5):847–57.

Article  PubMed  PubMed Central  Google Scholar 

Liu C-J, Lin J-K, Chen W-S, Lin T-C, Yang S-H, Jiang J-K, et al. The efficacy of chemotherapy in patients with high-grade metastatic colon cancer. Hepatogastroenterology. 2011;58(110–111):1495–501.

CAS  PubMed  Google Scholar 

Wuxiao ZJ, Zhou HY, Wang KF, Chen XQ, Hao XB, Da LuY, et al. A prognostic model to predict survival in stage III colon cancer patients based on histological grade, preoperative carcinoembryonic antigen level and the neutrophil lymphocyte ratio. Asian Pac J Cancer Prev. 2015;16(2):747–51.

Article  PubMed  Google Scholar 

Foxtrot Collaborative Group. Feasibility of preoperative chemotherapy for locally advanced, operable colon cancer: the pilot phase of a randomised controlled trial. Lancet Oncol. 2012;13:1152–60.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dehal A, Graff-Baker AN, Vuong B, Fischer T, Klempner SJ, Chang SC, et al. Neoadjuvant chemotherapy improves survival in patients with clinical T4b colon cancer. J Gastrointest Surg. 2018;22:242–9.

Article  PubMed  Google Scholar 

Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis ? Eur J Radiol [Internet]. 2013;82(2):342–8.

Article  PubMed  Google Scholar 

Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging. 2013;13:140–9.

Article  PubMed  PubMed Central  Google Scholar 

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures. They Are Data. Radiology. 2016;278:563–77.

PubMed  Google Scholar 

Kocak B, Durmaz ES, Kaya OK, Kilickesmez O. Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas. Acta Radiol. 2019;61:856–64.

Article  PubMed  Google Scholar 

Meyer HJ, Hamerla G, Höhn AK, Surov A. CT Texture Analysis-Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas. Front Oncol. 2019;9:444.

Google Scholar 

Sakai A, Onishi Y, Matsui M, Adachi H, Teramoto A, Saito K, et al. A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features. Radiol Phys Technol. 2020;13:27–36.

Article  PubMed  Google Scholar 

Cao J, Wang GR, Wang ZW, Jin ZY. CT texture analysis: a potential biomarker for evaluating KRAS mutational status in colorectal cancer. Chin Med Sci J. 2020;35:306–14.

Article  PubMed  Google Scholar 

Li Y, Eresen A, Shangguan J, Yang J, Benson AB 3rd, Yaghmai V, et al. Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning. J Cancer Res Clin Oncol. 2020;146:3165–74.

Article  CAS  PubMed  Google Scholar 

Nakajo M, Kajiya Y, Tani A, Jinguji M, Nakajo M, Kitazono M, et al. A pilot study for texture analysis of 18F-FDG and 18F-FLT-PET/CT to predict tumor recurrence of patients with colorectal cancer who received surgery. Eur J Nucl Med Mol Imaging. 2017;44:2158–68.

Article  PubMed  Google Scholar 

Rabe E, Cioni D, Baglietto L, Fornili M, Gabelloni M, Neri E. Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy? World J Hepatol. 2022;14:244–59.

Article  PubMed  PubMed Central  Google Scholar 

Lubner MG, Stabo N, Lubner SJ, del Rio AM, Song C, Halberg RB, et al. CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging. 2015;40:2331–7.

Article  PubMed  Google Scholar 

Paschke S, Jafarov S, Staib L, Kreuser ED, Maulbecker-Armstrong C, Roitman M, et al. Are colon and rectal cancer two different tumor entities? a proposal to abandon the term colorectal cancer. Int J Mol Sci. 2018;19:2577.

Article  PubMed  PubMed Central  Google Scholar 

Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brook RK, et al. The eighth edition AJCC Cancer staging manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin. 2017;67(2):93–9.

Article  PubMed  Google Scholar 

Ou X, Wang J, Zhou R, Zhu S, Pang F, Zhou Y, et al. Ability of 18F-FDG PET/CT radiomic features to distinguish breast carcinoma from breast lymphoma. Contrast Media Mol Imaging. 2019;2019:4507694.

Article  PubMed  PubMed Central  Google Scholar 

Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;215(78):4786–9.

Article  Google Scholar 

Agbamu DA, Day N, Walsh CJ, Hendrickse CW, Langman G, Pallan A, et al. Feasibility of preoperative chemotherapy for locally advanced, operable colon cancer: the pilot phase of a randomised controlled trial. Lancet Oncol [Internet]. 2012;13:1152–60.

Article  Google Scholar 

Chawla NVBKW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intel Res. 2003;2002:341–78.

Google Scholar 

Mramor M, Leban G, Demsar J, Zupan B. Visualization-based cancer microarray data classification analysis. Bioinformatics. 2007;23(16):2147–54.

Article  CAS  PubMed  Google Scholar 

Liu S, Zheng H, Pan X, Chen L, Shi M, Guan Y, et al. Texture analysis of CT imaging for assessment of esophageal squamous cancer aggressiveness. J Thorac Dis. 2017;9:4724–32.

Article  PubMed  PubMed Central  Google Scholar 

Liu S, Shi H, Ji C, Zheng H, Pan X, Guan W, et al. Preoperative CT texture analysis of gastric cancer: correlations with postoperative TNM staging. Clin Radiol. 2018;73(8):756.e1–756.e9.

Article  CAS  PubMed  Google Scholar 

Kulkarni A, Carrion-Martinez I, Jiang NN, Puttagunta S, Ruo L, Meyers BM, et al. Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features. Eur Radiol. 2020;30:2853–60.

Article  PubMed  Google Scholar 

Eresen A, Li Y, Yang J, Shangguan J, Velichko Y, Yaghmai V, Benson AB 3rd, Zhang Z. Preoperative assessment of lymph node metastasis in Colon Cancer patients using machine learning: a pilot study. Cancer Imaging. 2020;20(1):30.

Article  PubMed  PubMed Central  Google Scholar 

Bhandari A, Ibrahim M, Sharma C, Liong R, Gustafson S, Prior M. CT-based radiomics for differentiating renal tumours: a systematic review. Abdom Radiol NY. 2021;46:2052–63.

Article  PubMed  Google Scholar 

Deng Z, Dong W, Xiong S, Jin D, Zhou H, Zhang L, et al. Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer. Front Oncol. 2023;8(13):1166245.

Article  Google Scholar 

Park YJ, Park YS, Kim ST, Hyun SH. A machine learning approach using [18F]FDG PET-based radiomics for prediction of tumor grade and prognosis in pancreatic neuroendocrine tumor. Mol Imaging Biol. 2023. https://doi.org/10.1007/s11307-023-01832-7.

Article  PubMed  Google Scholar 

Amri R, Bordeianou LG, Berger DL. Effect of high-grade disease on outcomes of surgically treated colon cancer. Ann Surg Oncol. 2016;23:1157–63.

Article  PubMed  Google Scholar 

Nerad E, Lahaye MJ, Maas M, Nelemans P, Bakers FCH, Beets GL, et al. Diagnostic accuracy of CT for local staging of colon cancer: a systematic review and meta-analysis. Am J Roentgenol. 2016;207:984–95.

Article  Google Scholar 

Malmstrøm ML, Brisling S, Klausen TW, Săftoiu A, Perner T, Vilmann P, et al. Staging with computed tomography of patients with colon cancer. Int J Colorectal Dis. 2018;33:9–17.

Article  PubMed  Google Scholar 

Rafaelsen SR, Dam C, Vagn-Hansen C, Møller J, Rahr HB, Sjöström M, et al. CT and 3 tesla MRI in the TN staging of colon cancer: a prospective. Blind Study Curr Oncol. 2022;29:1069–79.

Article  PubMed  Google Scholar