Xian Zhong1, Haiyi Long1, Liya Su1, Ruiying Zheng1, Wei Wang1, Yu Duan1, Hangtong Hu1, Manxia Lin2, Xiaoyan Xie3. 1. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China. 2. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China. linmxia@mail.sysu.edu.cn. 3. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China. xiexyan@mail.sysu.edu.cn.
Abstract
PURPOSE: To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models. RESULTS: Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82-0.89), 0.87 (95% CI 0.83-0.92), and 0.74 (95% CI 0.67-0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region. CONCLUSION: Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.
PURPOSE: To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models. RESULTS: Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82-0.89), 0.87 (95% CI 0.83-0.92), and 0.74 (95% CI 0.67-0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region. CONCLUSION: Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.
Authors: Martin J Heslin; Brandon A Singletary; Kaitlin C Benos; Laura Read Lee; Charles Fry; Brenessa Lindeman Journal: Ann Surg Date: 2019-09 Impact factor: 12.969