Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

IF 4.7 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Radiology Pub Date : 2025-01-01 Epub Date: 2024-07-19 DOI:10.1007/s00330-024-10941-y

Hong Wei, Tianying Zheng, Xiaolan Zhang, Chao Zheng, Difei Jiang, Yuanan Wu, Jeong Min Lee, Mustafa R Bashir, Emily Lerner, Rongbo Liu, Botong Wu, Hua Guo, Yidi Chen, Ting Yang, Xiaoling Gong, Hanyu Jiang, Bin Song

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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 (cm³) 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.

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基于深度学习的三维定量总肿瘤负荷可预测 BCLC A 和 B 型 HCC 切除术后的早期复发。

研究目的本研究旨在评估由深度学习（DL）辅助的自动三维定量肿瘤负荷核磁共振成像预测肝细胞癌（HCC）术后早期复发（ER）的潜力：这是一项单中心回顾性研究，研究对象为接受 BCLC A 和 B 型 HCC 切除术并进行术前对比增强 MRI 检查的患者。使用 DL 自动分割工具获得定量的肿瘤总体积（立方厘米）和总肿瘤负荷（TTB，%）。放射医师的目测评估用于确保自动分割的质量控制。通过Cox回归分析确定临床病理变量和肿瘤负荷相关参数对ER的预后价值：结果：共纳入了 592 名患者，其中 525 名和 67 名患者分别被分配到 BCLC A 和 B 组（2 年 ER 率分别为 30.0% 和 45.0%）：30.0%对45.3%；危险比（HR）=1.8；P=0.007）。TTB是ER最重要的预测因素（HR = 2.2；p n分期；而BCLC B高TTB患者仍处于BCLC Bn分期。BCLC An 期患者的 2 年 ER 率为 30.5%，而 BCLC Bn 期患者的 2 年 ER 率为 58.1%（HR = 2.8；p 结论：TTB 是最重要的预测指标：通过基于深度学习的磁共振成像自动分割确定的TTB是术后ER的预测性生物标志物，有助于对BCLC A期和B期患者进行细化分类：基于深度学习的核磁共振自动分割得出的总肿瘤负荷可作为预测早期复发的成像生物标志物，从而改善巴塞罗那肝癌诊所肝癌A期和B期肝细胞癌患者肝切除术后的亚分类：总肿瘤负荷（TTB）对巴塞罗那临床肝癌（BCLC）分期非常重要，但却存在异质性。通过基于深度学习的自动分割得出的总肿瘤负荷可预测术后早期复发。将TTB纳入BCLC算法可成功对BCLC A和B患者进行细分。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Radiology 医学-核医学

CiteScore

11.60

自引率

8.50%

发文量

874

审稿时长

2-4 weeks

期刊介绍： European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.