Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor

ARTICLE HIGHLIGHTS

Research background

The assessment of KIT and PDGFRA mutations plays a vital role in establishing the pathological diagnosis of gastrointestinal stromal tumors (GISTs), predicting tumor prognosis, and guiding the administration of tyrosine kinase inhibitor therapy. For patients who are ineligible for genetic analysis, possessing information regarding the mutational status of GISTs is of paramount importance for the purpose of customizing personalized precision therapy.

Research motivation

Currently, tumor mutation status can only be obtained after surgical resection or conventional invasive biopsy, making preoperative genotyping of GISTs more challenging.

Research objectives

To develop and validate a radiomic model to predict the genotypes of gastric GISTs using contrast-enhanced computed tomography (CE-CT) images.

Research methods

The models for predicting GISTs with KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: Model_{CT sign}, model_{CT sign + rad}, and model_{CT sign + rad + clinic}. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test.

Research results

The ROC analyses demonstrated the performance of different models in predicting KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. In the training cohort, the models_{CT sign,} model_{CT sign + rad}, and model_{CT sign + rad + clinic} achieved area under the curve (AUC) values of 0.743, 0.818, and 0.915, respectively, for predicting KIT exon 11 mutation. In the validation cohort, the corresponding AUC values were 0.670, 0.781, and 0.811. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.72 for model_{CT sign}, model_{CT sign + rad}, and model_{CT sign + rad + clinic}, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795. Furthermore, the decision curve analysis confirmed the clinical significance and utility of the CT sign + rad + clinic model.

Research conclusions

Our study demonstrated that the radiomics model based on CE-CT images exhibited satisfactory performance in distinguishing gastric GISTs with KIT exon 11 mutation and GISTs with KIT exon 11 codons 557-558 deletions.

Research perspectives

This study focuses specifically on gastric GISTs and aims to develop a prediction model for genotypes using CE-CT images.