Gastrointestinal stromal tumors (GISTs) are rare mesenchymal tumors of the gastrointestinal (GI) tract. The size, the presence of a c-KIT gene mutation and the mitotic index are used to determine the prognosis and to direct systemic treatment choices. This study evaluated the use of radiomics, a technique which uses algorithms for diagnosing and predicting the c-KIT mutational status and mitotic index of GISTs from medical imaging features.
A combination of machine learning methods was used to distinguish treatment-naive GISTs from other GI tumors resembling GISTs on imaging, based on clinical and molecular characteristics, as well as imaging features extracted from the contrast-enhanced venous phase computed tomography scans. Evaluation was performed in a 100x random-split cross-validation with 20% of the data for testing.
A total of 242 tumors were used for distinguishing GISTs (n = 123) from non-GISTs (n = 119) including leiomyoma (n = 25), schwannoma (n = 21), gastric carcinoma (n = 25), lymphoma (n = 23) and leiomyosarcoma (n = 25).The non-GISTs were located either gastric (23%) or non-gastric (77%) and the GISTs were located either gastric (63%) or non-gastric (37%). A c-KIT mutation was present in the majority of the GISTs (exon 9 n = 10, exon 11 n = 56, exon 13 n = 2). The dataset originated from 65 different scanners, leading to heterogeneity in the imaging protocols. Imaging feature analyses showed a mean area under the curve (mAUC) of 0.70 (95% confidence interval [CI] 0.63-0.76) for distinguishing GIST from non-GISTs. A mAUC of 0.52 (95% CI 0.32-0.72) was found for predicting all c-KIT mutations, a mAUC of 0.51 (95% CI 0.29-0.72) for predicting a c-KIT exon 9 mutation, and a mAUC of 0.61 (95% CI 0.47-0.74) for predicting a c-KIT exon 11 mutation. The mitotic index was available in 83 patients (≤5/50 high power fields (HPFs) n = 53, 43.1%, >5/50 HPFs n = 33, 26.8%), and showed a mAUC of 0.60 (95% CI 0.47-0.73).
This pilot study showed the potential of radiomics to distinguish GIST from other GI tumors, but no potential in predicting c-KIT mutational status and mitotic index of GISTs. Further optimization and validation of the radiomics model is required to incorporate radiomics in the diagnostic routine of GISTs.
Clinical trial identification
M.J. Timbergen and M.P.A. Starmans contributed equally to this study.
Legal entity responsible for the study
The Netherlands Organization for Scientific Research.
All authors have declared no conflicts of interest.