ABSTRACT
OBJECTIVE: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC). MATERIALS AND METHODS: A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs - (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model's efficacy. RESULTS: The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035). CONCLUSION: Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies. CLINICAL RELEVANCE STATEMENT: The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making. KEY POINTS: Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge. The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs. The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.
ABSTRACT
Recent studies have indicated that platelets may play a role in the advancement of pancreatic cancer by supporting tumor growth and increasing resistance to chemotherapy. This study aims to develop a prognostic model for pancreatic cancer using a platelet-related gene risk score. Prognostic platelet-related genes (PRGs) were identified from public databases and analyzed using cluster analysis. We investigated the microenvironment signatures and gene mutation patterns across different PRG-based molecular subtypes of pancreatic cancer. A prognostic model based on PRGs was developed using LASSO-Cox Regression Analysis. Additionally, we examined the correlation between the risk score and tumor clinical characteristics, as well as drug sensitivity. Two molecular subtypes, cluster C1 and C2, were identified. Cluster C2 was associated with a poorer prognosis compared to Cluster C1. The C1 group exhibited higher scores for activated CD8+ T cells, central memory CD4+ T cells, and natural killer T cells. The C2 group demonstrated a higher frequency of gene mutations. We established and validated a novel prognostic prediction model and platelet-related gene risk score for pancreatic cancer. The risk score was positively correlated with T stage, N stage, and tumor grade, and it presented a significant prognostic value compared to other clinical factors. In conclusion, a novel prognostic prediction model focusing on platelet involvement in pancreatic cancer has been developed, offering potential benefits for future drug therapies and clinical prognostic assessments.
