Siegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10–45.

Article  PubMed  PubMed Central  Google Scholar 

Bonneville R, Krook MA, Kautto EA, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017. https://doi.org/10.1200/PO.17.00073.

Article  PubMed  PubMed Central  Google Scholar 

De’ Angelis GL, Bottarelli L, Azzoni C, et al. Microsatellite instability in colorectal cancer. Acta Biomed. 2018;89(9-S):97–101.

CAS  PubMed  Google Scholar 

Gilson P, Merlin JL, Harlé A. Detection of microsatellite instability: state of the art and future applications in circulating tumour DNA (ctDNA). Cancers (Basel). 2021;13(7):1491.

Article  CAS  PubMed  PubMed Central  Google Scholar 

GoliaPernicka JS, Gagniere J, Chakraborty J, et al. Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation. Abdom Radiol (NY). 2019;44(11):3755–63.

Article  Google Scholar 

Yunlong W, Tongtong L, Hua Z. The efficiency of neoadjuvant chemotherapy in colon cancer with mismatch repair deficiency. Cancer Med. 2023;12(3):2440–52.

Article  PubMed  Google Scholar 

Hasan S, Renz P, Wegner RE, et al. Microsatellite instability (MSI) as an independent predictor of pathologic complete response (PCR) in locally advanced rectal cancer: a national cancer database (NCDB) analysis. Ann Surg. 2020;271(4):716–23.

Article  PubMed  Google Scholar 

Benson AB, Venook AP, Al-Hawary MM, et al. Anal carcinoma, version 22018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2018;16(7):852–71.

Article  PubMed  PubMed Central  Google Scholar 

Glynne-Jones R, Wyrwicz L, Tiret E, Brown G, et al. Rectal cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2018;29(Suppl 4):iv263.

Article  CAS  PubMed  Google Scholar 

Sepulveda AR, Hamilton SR, Allegra CJ, et al. molecular biomarkers for the evaluation of colorectal cancer: guideline summary from the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and American Society of Clinical Oncology. J Oncol Pract. 2017;13(5):333–7.

Article  PubMed  Google Scholar 

Ma Y, Shi Z, Wei Y, et al. Exploring the value of multiple preprocessors and classifiers in constructing models for predicting microsatellite instability status in colorectal cancer. Sci Rep. 2024;14(1):20305.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Harada S, Morlote D. Molecular pathology of colorectal cancer. Adv Anat Pathol. 2020;27(1):20–6.

Article  CAS  PubMed  Google Scholar 

Bodalal Z, Hong EK, Trebeschi S, et al. Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study. Eur Radiol Exp. 2024;8(1):98.

Article  PubMed  PubMed Central  Google Scholar 

Cao Y, Zhang G, Zhang J, et al. Predicting microsatellite instability status in colorectal cancer based on triphasic enhanced computed tomography radiomics signatures: a multicenter study. Front Oncol. 2021;10(6): 687771.

Article  Google Scholar 

Xu ZY, Huang LW, Yang YJ, et al. Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast-enhanced CT imaging: a multicenter study. Cancer Med. 2024;13(12): e7407.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Luetkens JA, Klein S, Träber F, et al. Quantification of liver fibrosis at T1 and T2 mapping with extracellular volume fraction MRI: preclinical results. Radiology. 2018;288(3):748–54.

Article  PubMed  Google Scholar 

Tago K, Tsukada J, Sudo N, et al. Comparison between CT volumetry and extracellular volume fraction using liver dynamic CT for the predictive ability of liver fibrosis in patients with hepatocellular carcinoma. Eur Radiol. 2022;34(12):7555–65.

Article  Google Scholar 

Bandula S, Punwani S, Rosenberg WM, et al. Equilibrium contrast-enhanced CT imaging to evaluate hepatic fibrosis: initial validation by comparison with histopathologic sampling. Radiology. 2015;275(1):136–43.

Article  PubMed  Google Scholar 

Han D, Lin A, Kuronuma K, et al. Cardiac computed tomography for quantification of myocardial extracellular volume fraction: a systematic review and meta-analysis. JACC Cardiovasc Imaging. 2023;16(10):1306–17.

Article  PubMed  Google Scholar 

Luo Y, Liu L, Liu D, et al. Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Eur Radiol. 2023;33(6):4042–51.

Article  CAS  PubMed  Google Scholar 

Li Q, Bao J, Zhang Y, et al. Predictive value of CT-based extracellular volume fraction in the preoperative pathologic grading of rectal adenocarcinoma: a preliminary study. Eur J Radiol. 2023;163(6): 110811.

Article  PubMed  Google Scholar 

Yu Y, Wu D, Lan Z, et al. Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification. Eur Radiol. 2024;35(7):3871–82.

Article  PubMed  Google Scholar 

Zhang H, Guo H, Liu G, et al. CT for the evaluation of myocardial extracellular volume with MRI as reference: a systematic review and meta-analysis. Eur Radiol. 2023;33(12):8464–76.

Article  PubMed  Google Scholar 

Jablonowski R, Wilson MW, Do L, et al. Multidetector CT measurement of myocardial extracellular volume in acute patchy and contiguous infarction: validation with microscopic measurement. Radiology. 2015;274(2):370–8.

Article  PubMed  Google Scholar 

Hamdy A, Kitagawa K, Goto Y, et al. Comparison of the different imaging time points in delayed phase cardiac CT for myocardial scar assessment and extracellular volume fraction estimation in patients with old myocardial infarction. Int J Cardiovasc Imaging. 2019;35:917–26.

Article  PubMed  Google Scholar 

Yu F, Yang M, He C, et al. CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage. Eur Radiol. 2025;35(1):6–19.

Article  PubMed  Google Scholar 

Chen X, He L, Li Q, et al. Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm–enhanced artificial neural network–based CT radiomics signature. Eur Radiol. 2022;33(1):11–22.

Article  PubMed  Google Scholar 

Zhang Y, Liu J, Wu C, et al. preoperative prediction of microsatellite instability in rectal cancer using five machine learning algorithms based on multiparametric MRI radiomics. Diagnostics (Basel). 2023;13(2):269.

Article  CAS  PubMed  Google Scholar 

Parker AL, Bowman E, Zingone A, et al. Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma. Genome Med. 2022;14(1):126.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Folkman J. Tumor angiogenesis: therapeutic implications. N Engl J Med. 1971;285(21):1182–6.

Article  CAS  PubMed  Google Scholar 

Bali MA, Metens T, Denolin V, et al. Tumoral and nontumoral pancreas: correlation between quantitative dynamic contrast-enhanced MR imaging and histopathologic parameters. Radiology. 2011;261(2):456–66.

Article  PubMed  Google Scholar 

Wenting MA, Yuanhui ZHU, Zhaokun WEI, et al. Dynamic contrast enhanced-MRI and diffusion weighted imaging parameters for predicting microsatellite instability of colorectal cancer. Chin J Med Imaing Technol. 2023;39(7):1526–30.

Google Scholar 

Hu W, Zhao Y, Ji H, et al. Nomogram based on dual-energy CT-derived extracellular volume fraction for the prediction of microsatellite instability status in gastric cancer. Front Oncol. 2024;14(5):1370031.

Article  CAS  PubMed  PubMed Central