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机器学习算法在早期肝细胞癌术后复发预测中的应用价值

Application value of machine learning algorithms for predicting recurrence after resection of early-stage hepatocellular carcinoma

摘要:

目的:比较多种机器学习算法在早期肝细胞癌(HCC)术后复发预测中的效能。方法:回顾性分析2009年5月至2019年12月南京医科大学第一附属医院收治的882例接受根治性手术切除的早期HCC患者的临床资料,其中男性701例,女性181例,年龄(57.3±10.5)岁(范围:21~86岁)。将患者按2∶1随机分为训练集(588例)和测试集(294例)。构建的机器学习预测模型包括随机生存森林(RSF)、梯度提升机、弹性网络-Cox回归和Cox回归模型。采用一致性指数(C-index)衡量模型预测的准确性、综合Brier分数量化模型的预测误差、校准曲线反映模型的拟合情况。比较机器学习模型、竞争模型和HCC分期系统的预测效能。所有模型均在独立的测试集内进行验证。结果:训练集内患者中位无复发生存时间为61.7个月,测试集内患者中位无复发生存时间为61.9个月,两组患者无复发生存情况的差异无统计学意义( χ2=0.029, P=0.865)。RSF模型由5个常用临床病理学特征构成:白蛋白-胆红素分级、血清甲胎蛋白、肿瘤数目、肝切除方式和微血管侵犯。在训练集和测试集中,RSF模型的C-index值分别为0.758(95% CI:0.725~0.791)和0.749(95% CI:0.700~0.797),综合Brier分数分别为0.171和0.151。RSF模型对早期HCC复发预测的准确性优于其他3种机器学习模型、竞争模型(ERASL模型)及HCC分期系统(巴塞罗那分期、中国肝癌的分期方案、TNM分期),差异均有统计学意义( P值均<0.01)。校准曲线提示,RSF模型的预测概率与实际观察值具有较好的一致性。RSF模型可将早期HCC患者的复发风险分为低危、中危和高危组,在训练集和测试集内三组患者无复发生存情况的差异有统计学意义( P<0.01)。RSF模型对早期HCC术后复发风险的分层明显优于TNM分期。 结论:本研究构建的RSF模型集合了5个常用临床病理学特征,可较为准确地预测复发风险。

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abstracts:

Objective:To compare the performance of multiple machine learning algorithms in predicting recurrence after resection of early-stage hepatocellular carcinoma(HCC).Methods:Clinical data of 882 early-stage HCC patients who were admitted to the First Affiliated Hospital of Nanjing Medical University from May 2009 to December 2019 and treated with curative surgical resection were retrospectively collected. There were 701 males and 181 females,with an age of (57.3±10.5)years(range:21 to 86 years). All patients were randomly assigned in a 2∶1 ratio, the training dataset consisted of 588 patients and the test dataset consisted of 294 patients. The construction of machine learning-based prediction models included random survival forest(RSF),gradient boosting machine,elastic net regression and Cox regression model. The prediction accuracy of the model was measured by the concordance index(C-index). The prediction error of the model was measured by the integrated Brier score. Model fit was assessed by the calibration plot. The performance of machine learning models with that of rival model and HCC staging systems was compared. All models were validated in the independent test dataset.Results:Median recurrence-free survival was 61.7 months in the training dataset while median recurrence-free survival was 61.9 months in the validation dataset, there was no significant difference between two datasets in terms of recurrence-free survival( χ2=0.029, P=0.865). The RSF model consisted of 5 commonly used clinicopathological characteristics, including albumin-bilirubin grade,serum alpha fetoprotein,tumor number,type of hepatectomy and microvascular invasion. In both training and test datasets,the RSF model provided the best prediction accuracy,with respective C-index of 0.758(95% CI:0.725 to 0.791) and 0.749(95% CI:0.700 to 0.797),and the lowest prediction error,with respective integrated Brier score of 0.171 and 0.151. The prediction accuracy of RSF model for recurrence after resection of early-stage HCC was superior to that of other machine learning models,rival model(ERASL model) as well as HCC staging systems(BCLC,CNLC and TNM staging),with statistically significant difference( P<0.01). Calibration curves demonstrated good agreement between RSF model-predicted probabilities and observed outcomes.All patients could be stratified into low-risk,intermediate-risk or high-risk group based on RSF model;statistically significant differences among three risk groups were observed in both training and test datasets(all P<0.01). The risk stratification of RSF model was superior to that of TNM staging. Conclusion:The proposed RSF model assembled with 5 commonly used clinicopathological characteristics in this study can predict the recurrence risk with favorable accuracy that may facilitate clinical decision-support for patients with early-stage HCC.

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作者: 季顾惟 [1] 王科 [1] 夏永祥 [1] 李相成 [1] 王学浩 [1]
期刊: 《中华外科杂志》2021年59卷8期 679-685页 MEDLINEISTICPKUCSCD
栏目名称: 论著
DOI: 10.3760/cma.j.cn112139-20201026-00768
发布时间: 2024-03-19
基金项目:
国家自然科学基金项目 National Natural Science Foundation of China
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