Ruan Ye, Liu Xingyuan, Jin Yantong, Zhao Mingming, Zhang Xingda, Cheng Xiaoying, Wang Yang, Cao Siwei, Yan Menglu, Cai Jianing, Li Mengru, Gao Bo
Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
Front Med (Lausanne). 2025 Apr 17;12:1582560. doi: 10.3389/fmed.2025.1582560. eCollection 2025.
This study aimed to develop a comprehensive nomogram model by integrating clinical pathological and full-field digital mammography (FFDM) radiomic features to predict the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer patients, thereby providing personalized treatment recommendations.
A retrospective analysis was conducted on the clinical and imaging data of 227 breast cancer patients from 2016 to 2024 at the Second Affiliated Hospital of Harbin Medical University. The patients were divided into a training set ( = 159) and a test set ( = 68) with a 7:3 ratio. The region of interest (ROI) was manually segmented on FFDM images, and features were extracted and gradually selected. The rad-score was calculated for each patient. Five machine learning classifiers were used to build radiomics models, and the optimal model was selected. Univariate and multivariate regression analyses were performed to identify independent risk factors for predicting the efficacy of NAC in breast cancer patients. A nomogram prediction model was further developed by combining the independent risk factors and rad-score, and probability-based stratification was applied. An independent cohort was collected from an external hospital to evaluate the performance of the model.
The radiomics model based on support vector machine (SVM) demonstrated the best predictive performance. FFDM tumor density and HER-2 status were identified as independent risk factors for achieving pathologic complete response (PCR) after NAC ( < 0.05). The nomogram prediction model, developed by combining the independent risk factors and rad-score, outperformed other models, with areas under the curve (AUC) of 0.91 and 0.85 for the training and test sets, respectively. Based on the optimal cutoff points of 103.42 from the nomogram model, patients were classified into high-probability and low-probability groups. When the nomogram model was applied to an independent cohort of 47 patients, only four patients had incorrect diagnoses. The nomogram model demonstrated stable and accurate predictive performance.
The nomogram prediction model, developed by integrating clinical pathological and radiomic features, demonstrated significant performance in predicting the efficacy of NAC in breast cancer, providing valuable reference for clinical personalized prediction planning.
本研究旨在通过整合临床病理特征和全视野数字化乳腺摄影（FFDM）影像组学特征，建立一个综合列线图模型，以预测乳腺癌患者新辅助化疗（NAC）的疗效，从而提供个性化治疗建议。
对哈尔滨医科大学附属第二医院2016年至2024年227例乳腺癌患者的临床和影像数据进行回顾性分析。患者按7:3的比例分为训练集（n = 159）和测试集（n = 68）。 在FFDM图像上手动分割感兴趣区域（ROI），提取并逐步选择特征。为每位患者计算rad分数。 使用五个机器学习分类器建立影像组学模型，并选择最佳模型。进行单因素和多因素回归分析，以确定预测乳腺癌患者NAC疗效的独立危险因素。 通过结合独立危险因素和rad分数，进一步开发列线图预测模型，并应用基于概率的分层方法。从外部医院收集独立队列，以评估模型的性能。
基于支持向量机（SVM）的影像组学模型表现出最佳的预测性能。FFDM肿瘤密度和HER-2状态被确定为NAC后实现病理完全缓解（PCR）的独立危险因素（P < 0.05）。 通过结合独立危险因素和rad分数开发的列线图预测模型优于其他模型，训练集和测试集的曲线下面积（AUC）分别为0.91和0.85。 根据列线图模型的最佳截断点103.42，将患者分为高概率组和低概率组。当将列线图模型应用于47例患者的独立队列时，只有4例患者诊断错误。列线图模型表现出稳定且准确的预测性能。
通过整合临床病理特征和影像组学特征开发的列线图预测模型，在预测乳腺癌NAC疗效方面表现出显著性能，为临床个性化预测规划提供了有价值的参考。
