Purpose:Triple-negative breast cancer (TNBC) is a biologically and clinically heterogeneous disease, associated with poorer outcomes when compared with other subtypes of breast cancer. Neoadjuvant chemotherapy (NAC) is often given before surgery, and achieving a pathological complete response (pCR) has been associated with patient outcomes. There is thus strong clinical interest in the ability to accurately predict pCR status using baseline data.Materials and Methods:A cohort of 57 TNBC patients who underwent FDG-PET/CT before NAC was analyzed to develop a machine learning (ML) algorithm predictive of pCR. A total of 241 predictors were collected for each patient: 11 clinical features, 11 histopathological features, 13 genomic features, and 206 PET features, including 195 radiomic features. The optimization criterion was the area under the ROC curve (AUC). Event-free survival (EFS) was estimated using the Kaplan–Meier method.Results: The best ML algorithm reached an AUC of 0.82. The features with the highest weight in the algorithm were a mix of PET (including radiomics), histopathological, genomic, and clinical features, highlighting the importance of truly multimodal analysis. Patients with predicted pCR tended to have a longer EFS than patients with predicted non-pCR, even though this difference was not significant, probably due to the small sample size and few events observed (p= 0.09).Conclusions:This study suggests that ML applied to baseline multimodal data can help predict pCR status after NAC for TNBC patients and may identify correlations with long-term outcomes. Patients predicted as non-pCR may benefit from concomitant treatment with immunotherapy or dose intensification.
目的：三阴性乳腺癌是一种具有生物学和临床异质性的疾病，与其他乳腺癌亚型相比预后较差。新辅助化疗通常在手术前进行，而达到病理完全缓解与患者预后密切相关。因此，利用基线数据准确预测病理完全缓解状态的能力具有重要临床意义。 材料与方法：本研究纳入57例接受新辅助化疗前进行FDG-PET/CT检查的三阴性乳腺癌患者队列，开发预测病理完全缓解的机器学习算法。每例患者共收集241项预测因子：11项临床特征、11项组织病理学特征、13项基因组学特征及206项PET特征（含195项影像组学特征）。优化标准采用受试者工作特征曲线下面积。无事件生存期通过Kaplan-Meier法进行评估。 结果：最优机器学习算法的曲线下面积达0.82。算法中权重最高的特征涵盖PET（包括影像组学）、组织病理学、基因组学和临床特征，凸显了真正多模态分析的重要性。尽管差异未达统计学显著性（p=0.09），但预测为病理完全缓解的患者往往比预测为非病理完全缓解的患者具有更长的无事件生存期，这可能与样本量较小及观察到的事件数较少有关。 结论：本研究表明，基于基线多模态数据的机器学习有助于预测三阴性乳腺癌患者新辅助化疗后的病理完全缓解状态，并可能识别与长期预后的相关性。预测为非病理完全缓解的患者或可从免疫治疗联合疗法或剂量强化方案中获益。
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