能否通过患者特征和治疗前 MRI 特征预测肝癌（HCC）立体定向消融放疗（SABR）治疗后的生存情况：初步评估。

Can Patient Characteristics and Pre-Treatment MRI Features Predict Survival After Stereotactic Ablative Radiotherapy (SABR) Treatment in Hepatocellular Carcinoma (HCC): Preliminary Assessment.

机构信息

Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK.

Leeds Institute of Medical Research, Faculty of Medicine and Health, University of Leeds, Leeds LS2 9JT, UK.

出版信息

Curr Oncol. 2024 Oct 19;31(10):6384-6394. doi: 10.3390/curroncol31100474.

DOI:10.3390/curroncol31100474

PMID:39451778

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11506294/

Abstract

BACKGROUND

The study purpose was to develop a machine learning (ML)-based predictive model for event-free survival (EFS) in patients with hepatocellular carcinoma (HCC) undergoing stereotactic ablative radiotherapy (SABR).

METHODS

Patients receiving SABR for HCC at a single institution, between 2017 and 2020, were included in the study. They were split into training and test (85%:15%) cohorts. Events of interest were HCC recurrence or death. Three ML models were trained, the features were selected, and the hyperparameters were tuned. The performance was measured using Harrell's C index with the best-performing model being tested on the unseen cohort.

RESULTS

Overall, 41 patients were included (training = 34, test = 7) and 64 lesions were analysed (training = 50, test = 14), resulting in 30 events (60% rate) in the training set (death = 6, recurrence = 24) and 8 events (57% rate) in the test set (death = 5, recurrence = 3). A Cox regression model, using age at treatment, albumin, and intra-lesional fat identified through MRI as variables, had the best performance with a mean training score of 0.78 (standard deviation (SD) 0.02), a mean validation of 0.78 (SD 0.18), and a test score of 0.94.

CONCLUSIONS

Predicting the outcomes in patients with HCC, following SABR, using a novel model is feasible and warrants further evaluation.

摘要

背景

本研究旨在开发一种基于机器学习（ML）的预测模型，用于预测接受立体定向消融放疗（SABR）的肝细胞癌（HCC）患者的无事件生存（EFS）。

方法

本研究纳入了 2017 年至 2020 年期间在一家机构接受 SABR 治疗的 HCC 患者。患者被分为训练和测试（85%:15%）队列。感兴趣的事件为 HCC 复发或死亡。训练了三种 ML 模型，选择了特征，并调整了超参数。使用 Harrell's C 指数评估性能，最佳表现模型在未观察到的队列中进行测试。

结果

共纳入 41 例患者（训练组=34 例，测试组=7 例）和 64 个病灶（训练组=50 个，测试组=14 个），训练组中有 30 例事件（60%的发生率）（死亡=6 例，复发=24 例），测试组中有 8 例事件（57%的发生率）（死亡=5 例，复发=3 例）。使用治疗时的年龄、白蛋白和 MRI 确定的瘤内脂肪作为变量的 Cox 回归模型具有最佳性能，训练组的平均评分 0.78（标准差 0.02），验证组的平均评分 0.78（标准差 0.18），测试组的评分 0.94。