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.
Highlights
Gastrointestinal stromal tumors (GISTs) are the most commonly diagnosed subepithelial tumors in the gastrointestinal tract, histologically heterogeneous, biologically diverse, and challenging to predict their malignant potential [1, 2].Recently, a number of risk classification systems have been developed to predict biological behaviors, including National Institutes of Health (NIH) modified criteriaZhang et al Clin Trans Med (2020) 9:12[2], National Comprehensive Cancer Network (NCCN) criteria [3], and the Armed Forces Institute of Pathology (AFIP) criteria [4]
The radiomic nomogram including the radiomic signature and tumor size demonstrated significant calibration, and discrimination with area under ROC curve (AUC) of 0.801, 0.828, and 0.784 in the training, internal validation and external validation cohort respectively
The radiomic signature from contrast-enhanced computed tomography (CE-CT) was significantly associated with Ki-67 expression in GISTs
Summary
A number of risk classification systems have been developed to predict biological behaviors, including National Institutes of Health (NIH) modified criteria. NIH modified criteria and AFIP criteria are the most commonly used tools to assess the risk of malignant potential in GISTs, a significantly risk is associated with poor survival chances in patients with GISTs [5]. As measured by the mitotic count, Ki-67 is highly expressed in most of the proliferating cells except in G0 cells and is considered as a universal risk factor of malignancy in GISTs [7]. Multiple studies have demonstrated the association of Ki-67 higher expression with larger tumor size, higher mitotic rate, higher risk of malignancy and poor disease prognosis [7–12]. Previous studies have shown that high Ki-67 expression was an independent risk factor for high malignant potential and could help classify GISTs with a combination of mitotic rate and Ki-67 expression [10–12]. To develop and validate radiomic prediction models using contrast-enhanced computed tomography (CE-CT) to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs)
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