Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection
Hong Wei, Tianying Zheng, Xiaolan Zhang, Chao Zheng, Difei Jiang, Yuanan Wu, Jeong Min Lee, Mustafa R. Bashir, Emily Lerner, Rongbo Liu, Tao Wu, Hua Guo, Yidi Chen, Ting Yang, Xiaoling Gong, Hanyu Jiang, Bin Song
Abstract
Abstract Objectives This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC). Materials and methods This was a single-center retrospective study enrolling patients who underwent resection for BCLC A and B HCC and preoperative contrast-enhanced MRI. Quantitative total tumor volume (cm 3 ) and total tumor burden (TTB, %) were obtained using a DL automated segmentation tool. Radiologists’ visual assessment was used to ensure the quality control of automated segmentation. The prognostic value of clinicopathological variables and tumor burden-related parameters for ER was determined by Cox regression analyses. Results A total of 592 patients were included, with 525 and 67 patients assigned to BCLC A and B, respectively (2-year ER rate: 30.0% vs. 45.3%; hazard ratio (HR) = 1.8; p = 0.007). TTB was the most important predictor of ER (HR = 2.2; p < 0.001). Using 6.84% as the threshold of TTB, two ER risk strata were obtained in overall ( p < 0.001), BCLC A ( p < 0.001), and BCLC B ( p = 0.027) patients, respectively. The BCLC B low-TTB patients had a similar risk for ER to BCLC A patients and thus were reassigned to a BCLC A n stage; whilst the BCLC B high-TTB patients remained in a BCLC B n stage. The 2-year ER rate was 30.5% for BCLC A n patients vs. 58.1% for BCLC B n patients (HR = 2.8; p < 0.001). Conclusions TTB determined by DL-based automated segmentation at MRI was a predictive biomarker for postoperative ER and facilitated refined subcategorization of patients within BCLC stages A and B. Clinical relevance statement Total tumor burden derived by deep learning-based automated segmentation at MRI may serve as an imaging biomarker for predicting early recurrence, thereby improving subclassification of Barcelona Clinic Liver Cancer A and B hepatocellular carcinoma patients after hepatectomy. Key Points Total tumor burden (TTB) is important for Barcelona Clinic Liver Cancer (BCLC) staging, but is heterogenous. TTB derived by deep learning-based automated segmentation was predictive of postoperative early recurrence. Incorporating TTB into the BCLC algorithm resulted in successful subcategorization of BCLC A and B patients.