Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma. Issue 156 (November 2022)

• Record Type: Journal Article
• Authors: Wang, Hairui; Liu, Yuchan; Xu, Nan; Sun, Yuanyuan; Fu, Shihan; Wu, Yunuo; Liu, Chunhe; Cui, Lei; Liu, Zhaoyu; Chang, Zhihui; Li, Shu; Deng, Kexue; Song, Jiangdian
• Is Part Of: European journal of radiology. Issue 156(2022)
• Publication Date: 2022-11
• Subjects: Hepatocellular carcinoma -- Convolutional neural network -- Computed tomography -- Prognosis -- Machine learning; AFP alpha-fetoprotein -- BCLC Barcelona Clinic Liver Cancer -- C-index concordance index -- CI confidence interval -- CLIP Cancer of the Liver Italian Program -- CT computed tomography -- HCC hepatocellular carcinoma -- HR hazard ratio -- OS overall survival -- ROI region of interest -- TACE transcatheter arterial chemoembolization -- TTP time to progression; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110527
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: The EfficientNetV2 model enables to predict treatment-naïve, intermediate-stage HCC patient who show better response to TACE. The EfficientNetV2 model showed better prognostic capability for OS than the three radiomics and three clinical models. The HCC patients with lower EfficientNetV2 scores potentially associated with better survival prognosis of TACE. Abstract: Purpose: We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naïve patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models. Methods: EfficientNetV2 was used to build a prognosis model for treatment-naïve patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and validation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis. Three radiomics and three clinical models were then constructed for comparison. Results: Patients with EfficientNetV2-based model score ≤ 0.5 had better TTP than those with higher scores (hazard ratio [HR]: 0.32, 95%CI: 0.22–0.46, P < 0.0001; HR: 0.28, 95%CI: 0.20–0.41, P < 0.0001; and HR: 0.55, 95%CI: 0.36–0.88, P = 0.005 in the training, validation, and test datasets, respectively). Patients with model score ≤ 0.5 had better OS (38.8 months vs 20.9 months, HR: 0.58, 95%CI: 0.37–0.90, P = 0.008). Compared with the radiomics (intra-tumoral and peri-tumoral) and three clinical models, the EfficientNetV2-based model showed better survival prognosis for TACE (P < 0.05) in the test dataset. Conclusions: The EfficientNetV2-based model enables assessment of both TTP and OS prognosis of TACE in treatment-naïve, intermediate-stage HCC. Patients with lower scores will benefit from TACE. The model can potentially be used by clinicians to improve decision making regarding TACE treatment choices.