Radiomics signatures based on contrast-enhanced CT for preoperative prediction of the Ki-67 proliferation state in gastrointestinal stromal tumors.

Abstract

This study aimed to evaluate the Ki-67 proliferation state in patients with gastrointestinal stromal tumors (GISTs) using radiomics prediction signatures based on contrast-enhanced computed tomography (CE-CT). This single-center, retrospective study involved 103 patients (48 men and 55 women, mean age 61.1 ± 10.6years) who had pathologically confirmed GISTs after curative resection, including 63 with low Ki-67 proliferation level (Ki-67 labeling index ≤ 6%) and 40 with high Ki-67 proliferation level (Ki-67 labeling index > 6%). Radiomics features of the delineated lesions were preoperatively extracted from three-phase CE-CT images, including the arterial, venous, and delayed phases. The most relevant features were selected to construct the radiomics signatures using a logistic regression algorithm. Significant demographic characteristics and semantic features on CT were selected to develop a nomogram along with the optimal radiomics feature. We calculated the sensitivity, specificity, accuracy, F1 score, and area under the receiver operating characteristic (ROC) curve to evaluate the predictive performance of radiomics signatures. Ten quantitative radiomics features (two first-order and eight texture features) were selected to construct radiomics signatures. The radiomics signature based on the three-phase CE-CT images showed better predictive performance than that based on the single-phase CE-CT images, with an area under the curve (AUC) of 0.83 (95% CI 0.73-0.92) and F1 score of 82% in the training dataset and an AUC of 0.80 (95% CI 0.63-0.95) and F1 score of 75% in the testing dataset. The nomogram showed good calibration. Radiomics signatures using CE-CT images are generalizable and could be used in clinical practice to determine the proliferation state of Ki-67 in GISTs.