Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.

BACKGROUND: To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS: A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS: Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION: The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

The value of preoperative diagnosis of colorectal adenocarcinoma pathological T staging based on dual-layer spectral-detector computed tomography extracellular volume fraction: a preliminary study.

PURPOSE: To investigate the value of preoperative diagnosis of colorectal adenocarcinoma (CRAC) pathological T staging based on dual-layer spectral-detector computed tomography (DLCT) extracellular volume fraction (ECV) of CRAC lesions. METHODS: We prospectively collected clinical and DLCT imaging data from 165 patients with CRAC who attended two hospitals from June 2022 to April 2023. The enrolled patients were divided into a training group (n = 110, from Hospital 1) and an external validation group (n = 55, from Hospital 2). Measuring and calculating DLCT parameters of lesions, including CT values of 40 and 100 keV virtual mono-energetic images (VMI), iodine concentration (IC) and effective atomic number (Eff-Z) in the arterial phases (AP) and venous phases (VP), and ECV in the delayed phase (DP). The differences in clinical characteristics and DLCT parameters were compared between different pT subgroups. The correlation between DLCT parameters and pT stages were evaluated by Spearman correlation analysis. A multifactorial binary logistic stepwise forward regression analysis was performed to obtain independent influences associated with pT stage. Receiver operating characteristic curves (ROCs) were used to assess diagnostic efficacy and were expressed as area under the curve (AUC). RESULTS: Each DLCT parameter was higher in pT3 stage tumors than in pT1-2 stage tumors (all P < 0.05). The highest correlation was found between ECV and pT stage (r = 0.637). ECV were independent influences associated with pT stage. ECV had excellent diagnostic efficacy for CRAC pT staging in both the training and external validation groups (AUC = 0.919 and 0.892). CONCLUSION: ECV based on DLCT measurement can be used for preoperative noninvasive diagnosis of CRAC pT staging with excellent diagnostic efficacy. It can provide a new imaging marker for the preoperative evaluation of CRAC and help clinicians formulate individualized treatment earlier. However, it needs to be confirmed with a larger sample size.