Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach

TL;DR

This study evaluates a radiomics approach using CT images to differentiate gastrointestinal stromal tumors (GISTs) from other intra-abdominal tumors and attempts to predict molecular features, achieving comparable accuracy to radiologists for diagnosis.

Contribution

The paper introduces a radiomics model that effectively distinguishes GISTs from other tumors on CT images, but does not predict molecular mutations or mitotic index.

Findings

Radiomics model achieved AUC of 0.82 for GIST vs. non-GIST classification.

Radiomics model's AUC was comparable to radiologists.

The model could not reliably predict c-KIT mutations or mitotic index.

Abstract

Distinguishing gastrointestinal stromal tumors (GISTs) from other intra-abdominal tumors and GISTs molecular analysis is necessary for treatment planning, but challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA,BRAF mutational status and mitotic index (MI). All 247 included patients (125 GISTS, 122 non-GISTs) underwent a contrast-enhanced venous phase CT. The GIST vs. non-GIST radiomics model, including imaging, age, sex and location, had a mean area under the curve (AUC) of 0.82. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. Hence, our radiomics model was able to distinguish GIST from non-GISTS with a performance similar to three radiologists, but was not able to predict the c-KIT mutation or MI.