Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: a retrospective study.

objectiveThe aim of this study was to develop and assess a radiomics model utilizing multiparametric magnetic resonance imaging (MRI) for the prediction of preoperative risk assessment in gastrointestinal stromal tumors (GISTs). MATERIAL AND

methodsAn analysis was performed retrospectively on a group of 121 patients who received a histological diagnosis of GIST. They were then divided into two sets, with 85 in the training set and 36 in the validation set through random partitioning. Radiomics features from five MRI sequences, totaling 600 per patient, were extracted and subjected to feature selection utilizing a random forest algorithm. The discriminatory efficacy of the models was evaluated through receiver operating characteristic (ROC) and precision-recall (P-R) curve analyses. Model calibration was assessed via calibration curves. Subgroup analysis was performed on GISTs with a pathological maximum diameter equal to or less than 5 cm. Furtherly, Kaplan-Meier (K-M) curves and log-rank tests were used to compare the differences in survival status among different groups. Cox regression analysis was employed to identify independent prognostic factors and to construct a prognostic prediction model.

resultsThe clinical model (Model

conclusionOur investigation underscores the value of integrating radiomics analysis in conjunction with machine learning algorithms for prognostic risk stratification in GIST, presenting promising implications for informing clinical decision-making processes as well as optimizing management strategies.