Jia Yiwei, Li Chaofan, Feng Cong, Sun Shiyu, Cai Yifan, Yao Peizhuo, Wei Xinyu, Feng Zeyao, Liu Yanbin, Lv Wei, Wu Huizi, Wu Fei, Zhang Lu, Zhang Shuqun, Ma Xingcong
The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China.
Department of Tumor and Immunology in Precision Medical Institute, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Transl Oncol. 2025 Feb;52:102246. doi: 10.1016/j.tranon.2024.102246. Epub 2024 Dec 15.
Inflammatory breast cancer (IBC) is an aggressive and rare phenotype of breast cancer, which has a poor prognosis. Thus, it is necessary to establish a novel predictive model of high accuracy for the prognosis of IBC patients.
Clinical information of 1,230 IBC patients from 2010 to 2020 was extracted from the Surveillance, Epidemiology and End Results (SEER) database. Cox analysis was applied to identify clinicopathological characteristics associated with the overall survival (OS) of IBC patients. Random survival forest (RSF) algorithm was adopted to construct an accurate prognostic prediction model for IBC patients. Kaplan-Meier analysis was performed for survival analyses.
Race, N stage, M stage, molecular subtype, history of chemotherapy and surgery, and response to neoadjuvant therapy were identified as independent predictive factors for the OS of IBC patients. The top five significant variables included surgery, response to neoadjuvant therapy, chemotherapy, breast cancer molecular subtypes, and M stage. The C-index of RSF model was 0.7704 and the area under curve (AUC) values for 1, 3, 5 years in training and validation datasets were 0.879-0.955, suggesting the excellent predictive performance of RSF model. IBC patients were divided into high-risk group and low-risk group according the risk score of RSF model, and the OS of patients in the low-risk group was significantly longer than those in the high-risk group.
In this study, we constructed a prognosis prediction model for IBC patients through RSF algorithm, which may potentially serve as a useful tool during clinical decision-making.
炎性乳腺癌（IBC）是一种侵袭性强且罕见的乳腺癌表型，预后较差。因此，有必要建立一种针对IBC患者预后的新型高精度预测模型。
从监测、流行病学和最终结果（SEER）数据库中提取了2010年至2020年1230例IBC患者的临床信息。应用Cox分析来确定与IBC患者总生存期（OS）相关的临床病理特征。采用随机生存森林（RSF）算法构建IBC患者准确的预后预测模型。进行Kaplan-Meier分析以进行生存分析。
种族、N分期、M分期、分子亚型、化疗和手术史以及对新辅助治疗的反应被确定为IBC患者OS的独立预测因素。前五个显著变量包括手术、对新辅助治疗的反应、化疗、乳腺癌分子亚型和M分期。RSF模型的C指数为0.7704，训练集和验证集中1年、3年、5年的曲线下面积（AUC）值为0.879 - 0.955，表明RSF模型具有出色的预测性能。根据RSF模型的风险评分将IBC患者分为高风险组和低风险组，低风险组患者的OS明显长于高风险组。
在本研究中，我们通过RSF算法构建了IBC患者的预后预测模型，该模型可能在临床决策过程中作为一种有用的工具。
