BACKGROUND Gastrointestinal stromal tumors(GIST)are prevalent neoplasm originating from the gastrointestinal mesenchyme.Approximately 50%of GIST patients experience tumor recurrence within 5 years.Thus,there is a pres...BACKGROUND Gastrointestinal stromal tumors(GIST)are prevalent neoplasm originating from the gastrointestinal mesenchyme.Approximately 50%of GIST patients experience tumor recurrence within 5 years.Thus,there is a pressing need to accurately evaluate risk stratification preoperatively.AIM To assess the application of a deep learning model(DLM)combined with computed tomography features for predicting risk stratification of GISTs.METHODS Preoperative contrast-enhanced computed tomography(CECT)images of 551 GIST patients were retrospectively analyzed.All image features were independently analyzed by two radiologists.Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy.Patients were randomly assigned to the training(n=386)and validation cohorts(n=165).A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort.RESULTS Among the analyzed CECT image features,tumor size,ulceration,and enlarged feeding vessels were identified as significant risk predictors(P<0.05).In DLM,the overall area under the receiver operating characteristic curve(AUROC)was 0.88,with the accuracy(ACC)and AUROCs for each stratification being 87%and 0.96 for low-risk,79%and 0.74 for intermediate-risk,and 84%and 0.90 for high-risk,respectively.The overall ACC and AUROC were 84%and 0.94 in the combined model.The ACC and AUROCs for each risk stratification were 92%and 0.97 for low-risk,87%and 0.83 for intermediate-risk,and 90%and 0.96 for high-risk,respectively.Differences in AUROCs for each risk stratification between the two models were significant(P<0.05).CONCLUSION A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data,demonstrating superiority compared to DLM.展开更多
基金Supported by The Chinese National Key Research and Development Project,No.2021YFC2500400 and No.2021YFC2500402Tianjin Key Medical Discipline(Specialty)Construction Project,No.TJYXZDXK-009A.
文摘BACKGROUND Gastrointestinal stromal tumors(GIST)are prevalent neoplasm originating from the gastrointestinal mesenchyme.Approximately 50%of GIST patients experience tumor recurrence within 5 years.Thus,there is a pressing need to accurately evaluate risk stratification preoperatively.AIM To assess the application of a deep learning model(DLM)combined with computed tomography features for predicting risk stratification of GISTs.METHODS Preoperative contrast-enhanced computed tomography(CECT)images of 551 GIST patients were retrospectively analyzed.All image features were independently analyzed by two radiologists.Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy.Patients were randomly assigned to the training(n=386)and validation cohorts(n=165).A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort.RESULTS Among the analyzed CECT image features,tumor size,ulceration,and enlarged feeding vessels were identified as significant risk predictors(P<0.05).In DLM,the overall area under the receiver operating characteristic curve(AUROC)was 0.88,with the accuracy(ACC)and AUROCs for each stratification being 87%and 0.96 for low-risk,79%and 0.74 for intermediate-risk,and 84%and 0.90 for high-risk,respectively.The overall ACC and AUROC were 84%and 0.94 in the combined model.The ACC and AUROCs for each risk stratification were 92%and 0.97 for low-risk,87%and 0.83 for intermediate-risk,and 90%and 0.96 for high-risk,respectively.Differences in AUROCs for each risk stratification between the two models were significant(P<0.05).CONCLUSION A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data,demonstrating superiority compared to DLM.
