Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI

Abstract

IntroductionPreoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction.MethodsA total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier.ResultsThe hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927–0.968), 0.964 (95% CI: 0.930–0.978), and 90.8 (95% CI: 88.0–93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828–1.000), 0.941 (95% CI: 0.792–1.000), and 93.6% (95% CI: 79.3–98.2) in the test set, respectively.DiscussionThe deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery

“…This is not only because enhanced CT provides richer underlying imaging information for MH-GIST, but more importantly, Delta Radscore quantifies its radiographic texture differences and reflects the essence of the tumor. 25 - 28 …”

Development and validation of a computed tomography-based radiomics signature to predict “highest-risk” from patients with high-risk gastrointestinal stromal tumor

“…In recent years, radiomics has been demonstrated as a promising tool to explore lesions in radiological images that cannot be analyzed visually ( 8 ). Through extensive mathematical analysis, radiomics has been successfully applied to differential diagnosis, Ki67 expression, risk stratification, prediction of prognosis, and assessment of mutational status of GIST ( 9 - 11 ). Researchers have indicated the feasibility and superiority of radiomics application in the different aspects described above in patients with GIST compared with purely clinical factors.…”