Research Associate, King Hussein Cancer Center, Amman, Jordan.
Internship, Princess Basma Teaching Hospital, Irbid, Jordan.
Cancer Rep (Hoboken). 2024 Dec;7(12):e70071. doi: 10.1002/cnr2.70071.
Hürthle cell (HCC) and columnar cell variants (CCV) are rare subtypes of thyroid cancer.
This study used machine learning (ML) to evaluate treatment effectiveness and develop prognostic models.
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.
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.
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.
Hurthle 细胞（HCC）和柱状细胞变异型（CCV）是甲状腺癌的罕见亚型。
本研究使用机器学习（ML）评估治疗效果并开发预后模型。
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。
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。
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。
