Jin Yudi, Zhao Min, Su Tong, Fan Yanjia, Ouyang Zubin, Lv Fajin
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
J Med Internet Res. 2025 Apr 8;27:e69864. doi: 10.2196/69864.
Breast cancer is one of the most common malignancies among women worldwide. Patients who do not achieve a pathological complete response (pCR) or a clinical complete response (cCR) post-neoadjuvant chemotherapy (NAC) typically have a worse prognosis compared to those who do achieve these responses.
This study aimed to develop and validate a random survival forest (RSF) model to predict survival risk in patients with breast cancer who do not achieve a pCR or cCR post-NAC.
We analyzed patients with no pCR/cCR post-NAC treated at the First Affiliated Hospital of Chongqing Medical University from January 2019 to 2023, with external validation in Duke University and Surveillance, Epidemiology, and End Results (SEER) cohorts. RSF and Cox regression models were compared using the time-dependent area under the curve (AUC), the concordance index (C-index), and risk stratification.
The study cohort included 306 patients with breast cancer, with most aged 40-60 years (204/306, 66.7%). The majority had invasive ductal carcinoma (290/306, 94.8%), with estrogen receptor (ER)+ (182/306, 59.5%), progesterone receptor (PR)- (179/306, 58.5%), and human epidermal growth factor receptor 2 (HER2)+ (94/306, 30.7%) profiles. Most patients presented with T2 (185/306, 60.5%), N1 (142/306, 46.4%), and M0 (295/306, 96.4%) staging (TNM meaning "tumor, node, metastasis"), with 17.6% (54/306) experiencing disease progression during a median follow-up of 25.9 months (IQR 17.2-36.3). External validation using Duke (N=94) and SEER (N=2760) cohorts confirmed consistent patterns in age (40-60 years: 59/94, 63%, vs 1480/2760, 53.6%), HER2+ rates (26/94, 28%, vs 935/2760, 33.9%), and invasive ductal carcinoma prevalence (89/94, 95%, vs 2506/2760, 90.8%). In the internal cohort, the RSF achieved significantly higher time-dependent AUCs compared to Cox regression at 1-year (0.811 vs 0.763), 3-year (0.834 vs 0.783), and 5-year (0.810 vs 0.771) intervals (overall C-index: 0.803, 95% CI 0.747-0.859, vs 0.736, 95% CI 0.673-0.799). External validation confirmed robust generalizability: the Duke cohort showed 1-, 3-, and 5-year AUCs of 0.912, 0.803, and 0.776, respectively, while the SEER cohort maintained consistent performance with AUCs of 0.771, 0.729, and 0.702, respectively. Risk stratification using the RSF identified 25.8% (79/306) high-risk patients and a significantly reduced survival time (P<.001). Notably, the RSF maintained improved net benefits across decision thresholds in decision curve analysis (DCA); similar results were observed in external studies. The RSF model also showed promising performance across different molecular subtypes in all datasets. Based on the RSF predicted scores, patients were stratified into high- and low-risk groups, with notably poorer survival outcomes observed in the high-risk group compared to the low-risk group.
The RSF model, based solely on clinicopathological variables, provides a promising tool for identifying high-risk patients with breast cancer post-NAC. This approach may facilitate personalized treatment strategies and improve patient management in clinical practice.
乳腺癌是全球女性中最常见的恶性肿瘤之一。新辅助化疗（NAC）后未达到病理完全缓解（pCR）或临床完全缓解（cCR）的患者，其预后通常比达到这些缓解的患者更差。
本研究旨在开发并验证一种随机生存森林（RSF）模型，以预测NAC后未达到pCR或cCR的乳腺癌患者的生存风险。
我们分析了2019年1月至2023年在重庆医科大学附属第一医院接受治疗的NAC后未达到pCR/cCR的患者，并在杜克大学和监测、流行病学与最终结果（SEER）队列中进行了外部验证。使用时间依赖性曲线下面积（AUC）、一致性指数（C指数）和风险分层对RSF模型和Cox回归模型进行比较。
研究队列包括306例乳腺癌患者，大多数年龄在40 - 60岁（204/306，66.7%）。大多数为浸润性导管癌（290/306，94.8%），雌激素受体（ER）阳性（182/306，59.5%），孕激素受体（PR）阴性（179/306，58.5%），人表皮生长因子受体2（HER2）阳性（94/306，30.7%）。大多数患者表现为T2（185/306，60.5%）、N1（142/306，46.4%）和M0（295/306，96.4%）分期（TNM表示“肿瘤、淋巴结、转移”），在中位随访25.9个月（IQR 17.2 - 36.3）期间，17.6%（54/306）的患者出现疾病进展。使用杜克大学队列（N = 94）和SEER队列（N = 2760）进行的外部验证证实了年龄（40 - 60岁：59/94，63%，vs 1480/2760，53.6%）、HER2阳性率（26/94，28%，vs 935/2760，33.9%）和浸润性导管癌患病率（89/94，95%，vs 2506/2760，90.8%）的一致模式。在内部队列中，RSF在1年（0.811 vs 0.763）、3年（0.834 vs 0.783）和5年（0.810 vs 0.771）时的时间依赖性AUC显著高于Cox回归（总体C指数：0.803，95% CI 0.747 - 0.859，vs 0.736，95% CI 0.673 - 0.799）。外部验证证实了其强大的可推广性：杜克大学队列的1年、3年和5年AUC分别为0.912、0.803和0.776，而SEER队列的AUC分别为0.771、0.729和0.702，表现一致。使用RSF进行风险分层识别出25.8%（79/306）的高危患者，且生存时间显著缩短（P <.001）。值得注意的是，在决策曲线分析（DCA）中，RSF在不同决策阈值下均保持了更高的净效益；在外部研究中也观察到了类似结果。RSF模型在所有数据集中的不同分子亚型中也表现出良好的性能。根据RSF预测分数，患者被分为高危和低危组，与低危组相比，高危组的生存结果明显更差。
仅基于临床病理变量的RSF模型为识别NAC后乳腺癌高危患者提供了一种有前景的工具。这种方法可能有助于临床实践中的个性化治疗策略制定并改善患者管理。
