Personalized CT‐based radiomics nomogram preoperative predicting Ki‐67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort

Abstract

Background and AimTo develop and validate radiomic prediction models using contrast‐enhanced computed tomography (CE‐CT) to preoperatively predict Ki‐67 expression in gastrointestinal stromal tumors (GISTs). MethodA total of 339 GIST patients from four centers were categorized into the training, internal validation, and external validation cohort. By filtering unstable features, minimum redundancy, maximum relevance, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, a radiomic signature was built to predict the malignant potential of GISTs. Individual nomograms of Ki‐67 expression incorporating the radiomic signature or clinical factors were developed using the multivariate logistic model and evaluated regarding its calibration, discrimination, and clinical usefulness. ResultsThe radiomic signature, consisting of 6 radiomic features had AUC of 0.787 [95% confidence interval (CI) 0.632–0.801], 0.765 (95% CI 0.683–0.847), and 0.754 (95% CI 0.666–0.842) in the prediction of high Ki‐67 expression in the training, internal validation and external validation cohort, respectively. The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with AUC of 0.801 (95% CI 0.726–0.876), 0.828 (95% CI 0.681–0.974), and 0.784 (95% CI 0.701–0.868) in the training, internal validation and external validation cohort respectively. Based on the Decision curve analysis, the radiomics nomogram was found to be clinically significant and useful. ConclusionsThe radiomic signature from CE‐CT was significantly associated with Ki‐67 expression in GISTs. A nomogram consisted of radiomic signature, and tumor size had maximum accuracy in the prediction of Ki‐67 expression in GISTs. Results from our study provide vital insight to make important preoperative clinical decisions.

New advances in radiomics of gastrointestinal stromal tumors

“…Existing articles of radiomics in GISTs (Table 1 ) have been performed with heterogeneous methodology regarding the imaging studies, type of radiomics features and analysis[ 30 - 44 ]. Up to May 2020, all the radiomics research studies on GISTs were performed in retrospective population, and only four studies were multicentric[ 31 , 32 , 35 , 44 ]. The number of included GISTs widely ranged from 15 to 440 lesions.…”

“…Validation of radiomics models in independent cohorts was performed in 47% of studies. However only three of them[ 31 , 32 , 44 ] included external validation cohorts.…”

“…Finally, Ki67 index represents a marker of proliferation of tumor cells, which have also been associated with poor prognosis in GISTs[ 55 ]. In a study of 339 GISTs[ 44 ], radiomics signature from contrast-enhanced CT have demonstrated a significant correlation with Ki67 expression, providing an added value for prognosis assessment.…”

Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging

“…In theory, the judgment efficiency of texture analysis of GISTs risk stratification is better than the conventional imaging [ 24 , 25 ]. Nine studies have researched the performance of CT-derived radiomic signature for risk stratification [ 24 – 32 ], and one study evaluated EUS-derived texture [ 33 ] associated with risk stratification. The details are summarized in Table 2 .…”

“…The details are summarized in Table 2 . In CT-derived analysis, four studies have applied NIH criterion or modified NIH criterion for GISTs malignant risk classification [ 25 – 28 ], while three studies were determined on NCCN guideline [ 24 , 29 , 30 ] and one study without clear guideline [ 33 ] and one study used Ki-67 expression standard [ 32 ]. Two of the four NIH studies based on NIH risk classification only evaluated CT textural parameters [ 26 , 27 ].…”