CT Radiomics for the Preoperative Prediction of Ki67 Index in Gastrointestinal Stromal Tumors: A Multi-Center Study

Abstract

PurposeThis study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs).Materials and MethodsA total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model’s performance and generalization ability.ResultsAfter dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761–0.908) in the training set and 0.784 (95% CI: 0.691–0.874) in the external validation cohort.ConclusionThe radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.

Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors

“…After step-by-step screening, 12 features were filtered, including four wavelet transform features and seven Gaussian filter features, and only one feature was obtained from the original images. Therefore, we speculate that the wavelet transform and Gaussian filter can highlight the details in the original images and show more information, and thus can better reflect the heterogeneity between tumors, which is consistent with the conclusions of Qi et al and Zhao et al 32,37 GLCM is a type of image analysis technology that can describe the distribution and shape of image pixels in the form of a grey matrix. 38 GLRLM is the same as GLCM, which can evaluate the distributions of discrete grayscale in the images; however, GLCM evaluates gray symbiosis between adjacent pixels or voxels, and GLRLM evaluates the run length.…”

“…In previous studies, radiomics showed great potential for GISTs, which can effectively predict the malignant potential of GISTs, 28,29 mitotic index, 30 and Ki-67 expression index. 31,32 In addition, a preliminary study has been conducted on the use of radiomics for the classification and prediction of GISTs gene mutations. Xu et al showed that CT texture analysis is helpful in distinguishing whether GISTs have KIT exon 11 mutations on enhanced CT images.…”

Gastrointestinal Stromal Tumors: Recurrence and Survival Analysis of 49 Patients

“…Zhao and colleagues have reported that patients who are receiving adjuvant therapy with more than 8% Ki67 levels may have a poorer prognosis. 30 Lopez Gordo et al also indicated a relevance between Ki67 levels and overall survival, however there it was not associated with recurrence. 18 According to National Cancer Institute (Surveillance, Epidemiology and End Results: Localized, Regional a Distant), the 5-Year overall survival rate of all stages of GISTs including all primary tumor sites is 83% (28).…”

Virtual biopsy in abdominal pathology: where do we stand?

“…In particular, Song et al 11 and Zhang et al 28 developed models based on CT scans, capable of stratifying patients between low and high risk, yielding an AUC of 0.85 and 0.94, respectively. Other studies, such as those from Zhang et al 30 and Zhao et al 29 developed radiogenomics models to evaluate Ki-67 expression, to predict disease-specific survival and risk of recurrence. Of the above, preliminary assessment of the Ki-67 expression was the most promising (AUC = 0.78 in both studies).…”