Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively.


Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, No.1838, North Guangzhou Avenue, Guangzhou, 510515, Guangdong Province, China. [Email]


OBJECTIVE : To develop and evaluate a radiomics nomogram for differentiating the malignant risk of gastrointestinal stromal tumours (GISTs).
METHODS : A total of 222 patients (primary cohort: n = 130, our centre; external validation cohort: n = 92, two other centres) with pathologically diagnosed GISTs were enrolled. A Relief algorithm was used to select the feature subset with the best distinguishing characteristics and to establish a radiomics model with a support vector machine (SVM) classifier for malignant risk differentiation. Determinant clinical characteristics and subjective CT features were assessed to separately construct a corresponding model. The models showing statistical significance in a multivariable logistic regression analysis were used to develop a nomogram. The diagnostic performance of these models was evaluated using ROC curves. Further calibration of the nomogram was evaluated by calibration curves.
RESULTS : The generated radiomics model had an AUC value of 0.867 (95% CI 0.803-0.932) in the primary cohort and 0.847 (95% CI 0.765-0.930) in the external cohort. In the entire cohort, the AUCs for the radiomics model, subjective CT findings model, clinical index model and radiomics nomogram were 0.858 (95% CI 0.807-0.908), 0.774 (95% CI 0.713-0.835), 0.759 (95% CI 0.697-0.821) and 0.867 (95% CI 0.818-0.915), respectively. The nomogram showed good calibration.
CONCLUSIONS : This radiomics nomogram predicted the malignant potential of GISTs with excellent accuracy and may be used as an effective tool to guide preoperative clinical decision-making.
CONCLUSIONS : • CT-based radiomics model can differentiate low- and high-malignant-potential GISTs with satisfactory accuracy compared with subjective CT findings and clinical indexes. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings and clinical indexes can achieve individualised risk prediction with improved diagnostic performance. • This study might provide significant and valuable background information for further studies such as response evaluation of neoadjuvant imatinib and recurrence risk prediction.


Classification,Gastrointestinal stromal tumour,Machine learning,Nomogram,Radiomics,