基于深度学习的术前 MRI 分析可预测肝癌的微血管侵犯和预后。
Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.
机构信息
Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
School of Software Engineering, Tongji University, Shanghai, People's Republic of China.
出版信息
World J Surg Oncol. 2022 Jun 8;20(1):189. doi: 10.1186/s12957-022-02645-8.
BACKGROUND
Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC.
METHODS
We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated, and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression.
RESULTS
Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and a-fetoprotein (AFP) were independently associated with MVI: DL-MVI (odds ratio [OR] = 35.738; 95% confidence interval [CI] 14.027-91.056; p < 0.001), AFP (OR = 4.634, 95% CI 2.576-8.336; p < 0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824.
CONCLUSIONS
Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.
背景
术前预测微血管侵犯（MVI）对于肝细胞癌（HCC）患者的治疗策略制定至关重要。我们旨在开发一种基于术前动态对比增强磁共振成像（DCE-MRI）的深度学习（DL）模型，以预测 HCC 患者的 MVI 状态和临床结局。
方法
我们回顾性纳入了总共 321 例经病理证实存在 MVI 状态的 HCC 患者。在本研究中，对这些患者的术前 DCE-MRI 进行了采集、注释和进一步的 DL 分析。采用多变量逻辑回归构建了一个将 DL 预测的 MVI 状态（DL-MVI）和临床参数相结合的 MVI 预测模型。
结果
在 321 例 HCC 患者中，有 136 例患者病理上 MVI 阴性，185 例患者 MVI 阳性。DL 预测的 MVI 阴性和 MVI 阳性患者的无复发生存率（RFS）和总生存率（OS）有显著差异。在所有临床变量中，只有 DL 预测的 MVI 状态和甲胎蛋白（AFP）与 MVI 独立相关：DL-MVI（优势比[OR] = 35.738；95%置信区间[CI] 14.027-91.056；p < 0.001），AFP（OR = 4.634，95% CI 2.576-8.336；p < 0.001）。为了预测 MVI 的存在，DL-MVI 联合 AFP 的曲线下面积（AUC）为 0.824。
结论
我们的预测模型结合了 DL-MVI 和 AFP，在预测 HCC 患者的 MVI 和临床结局方面表现良好。
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