基于机器学习算法的甲状腺乳头状癌变异型预后模型与治疗结局。
Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants.
机构信息
Research Associate, King Hussein Cancer Center, Amman, Jordan.
Internship, Princess Basma Teaching Hospital, Irbid, Jordan.
出版信息
Cancer Rep (Hoboken). 2024 Dec;7(12):e70071. doi: 10.1002/cnr2.70071.
BACKGROUND
Hürthle cell (HCC) and columnar cell variants (CCV) are rare subtypes of thyroid cancer.
AIMS
This study used machine learning (ML) to evaluate treatment effectiveness and develop prognostic models.
METHODS
Chi-square tests, Kaplan-Meier curves, log-rank tests, and Cox regression were used. Five ML algorithms constructed prognostic models predicting 5-year survival, validated using the AUC of the ROC curve.
RESULTS
Among 3690 patients, 3180 had CCV and 510 had HCC. ML models showed metastasis, surgery + RT, and age were significant factors for HCC, while the N component of TNM, metastasis, and tumor size were significant for CCV.
CONCLUSION
This study offers a comprehensive approach for treating and assessing prognosis in PTC variants. The ML models developed offer practical tools for personalized clinical decision-making.
背景
Hurthle 细胞（HCC）和柱状细胞变异型（CCV）是甲状腺癌的罕见亚型。
目的
本研究使用机器学习（ML）评估治疗效果并开发预后模型。
方法
采用卡方检验、Kaplan-Meier 曲线、对数秩检验和 Cox 回归。构建了 5 种 ML 算法的预后模型，预测 5 年生存率，通过 ROC 曲线的 AUC 进行验证。
结果
在 3690 名患者中，3180 名患有 CCV，510 名患有 HCC。ML 模型显示转移、手术+RT 和年龄是 HCC 的显著因素，而 TNM 的 N 成分、转移和肿瘤大小是 CCV 的显著因素。
结论
本研究为 PTC 变异体的治疗和预后评估提供了一种综合方法。所开发的 ML 模型为个性化临床决策提供了实用工具。
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