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

IF 2.8 4区医学 Q2 ONCOLOGY

Current oncology Pub Date : 2024-10-19 DOI:10.3390/curroncol31100474

Rachel Gravell, Russell Frood, Anna Littlejohns, Nathalie Casanova, Rebecca Goody, Christine Podesta, Raneem Albazaz, Andrew Scarsbrook

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引用次数: 0

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.

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患者特征和治疗前磁共振成像特征能否预测肝细胞癌（HCC）立体定向消融放疗（SABR）治疗后的生存期？初步评估。

研究背景研究目的是为接受立体定向消融放疗（SABR）的肝细胞癌（HCC）患者建立一个基于机器学习（ML）的无事件生存期（EFS）预测模型：研究纳入了2017年至2020年间在一家机构接受SABR治疗的HCC患者。他们被分为训练组和测试组（85%:15%）。关注事件为 HCC 复发或死亡。对三个 ML 模型进行了训练、特征选择和超参数调整。使用 Harrell's C 指数衡量模型的性能，并在未见过的队列中测试性能最好的模型：共纳入 41 例患者（训练 = 34 例，测试 = 7 例），分析了 64 个病灶（训练 = 50 例，测试 = 14 例），结果在训练集中发生了 30 起事件（发生率为 60%）（死亡 = 6 例，复发 = 24 例），在测试集中发生了 8 起事件（发生率为 57%）（死亡 = 5 例，复发 = 3 例）。使用治疗时的年龄、白蛋白和通过核磁共振成像确定的皮下脂肪作为变量的 Cox 回归模型性能最佳，平均训练得分为 0.78（标准差 (SD) 0.02），平均验证得分为 0.78（标准差 0.18），测试得分为 0.94：使用新型模型预测SABR术后HCC患者的预后是可行的，值得进一步评估。

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

Current oncology ONCOLOGY-

CiteScore

3.30

自引率

7.70%

发文量

664

审稿时长

1 months

期刊介绍： Current Oncology is a peer-reviewed, Canadian-based and internationally respected journal. Current Oncology represents a multidisciplinary medium encompassing health care workers in the field of cancer therapy in Canada to report upon and to review progress in the management of this disease. We encourage submissions from all fields of cancer medicine, including radiation oncology, surgical oncology, medical oncology, pediatric oncology, pathology, and cancer rehabilitation and survivorship. Articles published in the journal typically contain information that is relevant directly to clinical oncology practice, and have clear potential for application to the current or future practice of cancer medicine.