Jiang Linhan, Su Ke, Wang Jing, Lin Yitong, Zhao Xianya, Zhang Hengxiang, Liu Yu
Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Immunol. 2024 Nov 12;15:1495329. doi: 10.3389/fimmu.2024.1495329. eCollection 2024.
Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five machine learning models to evaluate the efficacy of postoperative IFN-α1b in patients with advanced melanoma.
We retrospectively analyzed 113 patients with the American Joint Committee on Cancer (AJCC) stage III-IV melanoma who received postoperative IFN-α1b therapy between July 2009 and February 2024. Recurrence-free survival (RFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Five machine learning models (Decision Tree, Cox Proportional Hazards, Random Forest, Support Vector Machine, and LASSO regression) were developed and compared for their capacity to predict the outcomes of patients. Model performance was evaluated using concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, and decision curve analysis.
The 1-year, 2-year, and 3-year RFS rates were 71.10%, 43.10%, and 31.10%, respectively. For OS, the 1-year, 2-year, and 3-year OS rates were 99.10%, 82.30%, and 75.00%, respectively. The Decision Tree (DT) model demonstrated superior predictive performance with the highest C-index of 0.792. Time-dependent ROC analysis for predicting 1-, 2-, and 3-year RFS based on the DT model is 0.77, 0.79 and 0.76, respectively. Serum albumin emerged as the important predictor of RFS.
Our study demonstrates the considerable efficacy DT model for predicting the efficacy of adjuvant IFN-α1b in patients with advanced melanoma. Serum albumin was identified as a key predictive factor of the treatment efficacy.
α1b干扰素（IFN-α1b）作为黑色素瘤的辅助治疗已显示出显著的治疗潜力。本研究旨在开发五种机器学习模型，以评估术后IFN-α1b对晚期黑色素瘤患者的疗效。
我们回顾性分析了2009年7月至2024年2月期间接受术后IFN-α1b治疗的113例美国癌症联合委员会（AJCC）III-IV期黑色素瘤患者。采用Kaplan-Meier分析评估无复发生存期（RFS）和总生存期（OS）。开发了五种机器学习模型（决策树、Cox比例风险模型、随机森林、支持向量机和LASSO回归），并比较它们预测患者预后的能力。使用一致性指数（C指数）、时间依赖性受试者工作特征（ROC）曲线和决策曲线分析评估模型性能。
1年、2年和3年的RFS率分别为71.10%、43.10%和31.10%。对于OS，1年、2年和3年的OS率分别为99.10%、82.30%和75.00%。决策树（DT）模型表现出卓越的预测性能，最高C指数为0.792。基于DT模型预测1年、2年和3年RFS的时间依赖性ROC分析分别为0.77、0.79和0.76。血清白蛋白是RFS的重要预测指标。
我们的研究证明了DT模型在预测晚期黑色素瘤患者辅助IFN-α1b疗效方面具有显著效果。血清白蛋白被确定为治疗效果的关键预测因素。
