Prediction of Second Primary Cancer Among Lung Cancer Patients with Competing Risk Survival Analysis

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题名:	Prediction of Second Primary Cancer Among Lung Cancer Patients with Competing Risk Survival Analysis
作者:	張珮慈;Chang, Pei-Tzu
贡献者:	資訊管理學系
关键词:	生存分析;競爭風險生存分析;機器學習;深度學習;第二原發性癌症;肺癌;survival analysis;competing risk survival analysis;machine learning;deep learning;second primary cancer;lung cancer
日期:	2024-07-26
上传时间:	2024-10-09 17:03:31 (UTC+8)
出版者:	國立中央大學
摘要:	肺癌為全世界癌症種類中，具有高發生率與高死亡率的癌症。隨著癌症治療的進步，第二原發性癌症已經成為癌症倖存者面臨的嚴重問題。競爭風險生存分析已被應用於研究此類患者的疾病預後狀況，機器學習和深度學習的興起也被用於改良競爭風險生存分析方法。本研究的目標是運用統計學、基於機器學習和基於深度學習的生存分析模型，來預測肺癌的患者在考慮或不考慮死亡作為競爭風險事件的情況下，罹患第二原發性癌症的風險。所納入的模型包括 Cox 比例風險模型、隨機生存森林、梯度提升生存分析、生存支持向量機、DeepSurv、特定原因 Cox 比例風險模型、Fine-Gray 模型、競爭風險的隨機生存森林以及競爭風險的部分邏輯人工神經網絡模型 (PLANNCR)。模型性能通過 180 天、360 天、540 天、720 天、900 天、1080 天的時間依賴性特徵曲線下面積(time-dependent AUC)和整合 Brier 得分進行比較。結果顯示，競爭風險的隨機生存森林方法在六個時間點上訓練 30 次的平均 time-dependent AUC 為最高，分別為 0.755、0.745、0.741、0.745、0.741 和 0.739。此外，競爭風險的隨機生存森林方法在各模型中整合 Brier 得分最低，為 0.0275。總結而言，我們的研究表明，在不同時間點預測目標事件時，競爭風險生存分析方法的區分能力和校準能力更加穩定。;Lung cancer has not only a high incidence rate, but also a high mortality rate in the world. With the improvement in cancer treatment, second primary cancer has become a serious issue for cancer survivors. Competing risk survival analysis has been applied to study disease prognosis of such patients. With the rise of machine learning and deep learning methods, a variety of competing risk survival analysis methods have been proposed. In this study, our objective was to employ statistical, machine learning-based, and deep learning-based survival analysis models to predict second primary cancer in patients diagnosed with lung cancer with and without death as its competing risk event. The models included were Cox Proportional Hazards model (CPH), Random Survival Forests (RSF), Gradient Boosting Survival Analysis (GBSA), Survival Support Vector Machine (SSVM), DeepSurv, Cause-Specific Cox proportional hazard regression (CSC), Fine-Gray regression (FGR), Random Survival Forests for competing risks (RSFCR), and the partial logistic artificial neural network model for competing risks (PLANNCR). The performances were compared with time-dependent area under the receiver operating characteristic curve (AUC) at 180, 360, 540, 720, 900, 1080 days and Integrated Brier Score throughout this period. The result showed that RSFCR had the highest average time-dependent AUC over 30 training times of 0.755, 0.745, 0.741, 0.745, 0.741 and 0.739 at six time points, respectively. RSFCR also had the lowest Integrated Brier Score of 0.0275 between the models. In summary, our study suggests that the discriminative and calibration abilities of competing risk survival analysis methods are more stable when predicting the event of interest at a set of different time points.
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