Jiang Zirui, Low Joshua, Huang Colin, Yue Yong, Njeh Christopher, Oderinde Oluwaseyi
Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Lab, School of Health Sciences, College of Health and Human Sciences, Purdue University, West Lafayette, IN, 47907, USA.
Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Med Oncol. 2025 Aug 11;42(9):425. doi: 10.1007/s12032-025-02982-0.
Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer. In this study, we developed deep radiomic models to enhance the prediction of chemotherapy response after the first treatment cycle. 18F-Fludeoxyglucose PET/CT imaging data and clinical record from 60 breast cancer patients were retrospectively obtained from the Cancer Imaging Archive. PET/CT scans were conducted at three distinct stages of treatment; prior to the initiation of chemotherapy (T1), following the first cycle of chemotherapy (T2), and after the full chemotherapy regimen (T3). The patient's primary gross tumor volume (GTV) was delineated on PET images using a 40% threshold of the maximum standardized uptake value (SUVmax). Radiomic features were extracted from the GTV based on the PET/CT images. In addition, a squeeze-and-excitation network (SENet) deep learning model was employed to generate additional features from the PET/CT images for combined analysis. A XGBoost machine learning model was developed and compared with the conventional machine learning algorithm [random forest (RF), logistic regression (LR) and support vector machine (SVM)]. The performance of each model was assessed using receiver operating characteristics area under the curve (ROC AUC) analysis, and prediction accuracy in a validation cohort. Model performance was evaluated through fivefold cross-validation on the entire cohort, with data splits stratified by treatment response categories to ensure balanced representation. The AUC values for the machine learning models using only radiomic features were 0.85(XGBoost), 0.76 (RF), 0.80 (LR), and 0.59 (SVM), with XGBoost showing the best performance. After incorporating additional deep learning-derived features from SENet, the AUC values increased to 0.92, 0.88, 0.90, and 0.61, respectively, demonstrating significant improvements in predictive accuracy. Predictions were based on pre-treatment (T1) and post-first-cycle (T2) imaging data, enabling early assessment of chemotherapy response after the initial treatment cycle. Integrating deep learning-derived features significantly enhanced the performance of predictive models for chemotherapy response in breast cancer patients. This study demonstrated the superior predictive capability of the XGBoost model, emphasizing its potential to optimize personalized therapeutic strategies by accurately identifying patients unlikely to respond to chemotherapy after the first treatment cycle.
提高肿瘤反应预测的准确性有助于为乳腺癌患者制定个性化的治疗策略。在本研究中，我们开发了深度放射组学模型，以提高对首个治疗周期后化疗反应的预测。从癌症影像存档库中回顾性获取了60例乳腺癌患者的18F-氟脱氧葡萄糖PET/CT影像数据和临床记录。在治疗的三个不同阶段进行PET/CT扫描：化疗开始前（T1）、首个化疗周期后（T2）以及完整化疗方案后（T3）。使用最大标准化摄取值（SUVmax）的40%阈值在PET图像上勾勒出患者的原发大体肿瘤体积（GTV）。基于PET/CT图像从GTV中提取放射组学特征。此外，采用挤压激励网络（SENet）深度学习模型从PET/CT图像中生成额外特征进行联合分析。开发了一种XGBoost机器学习模型，并与传统机器学习算法[随机森林（RF）、逻辑回归（LR）和支持向量机（SVM）]进行比较。使用曲线下面积（ROC AUC）分析和验证队列中的预测准确性评估每个模型的性能。通过对整个队列进行五折交叉验证来评估模型性能，数据分割按治疗反应类别分层以确保均衡代表性。仅使用放射组学特征的机器学习模型的AUC值分别为0.85（XGBoost）、0.76（RF）、0.80（LR）和0.59（SVM），其中XGBoost表现最佳。纳入来自SENet的额外深度学习衍生特征后，AUC值分别增至0.92、0.88、0.90 和 0.61，表明预测准确性有显著提高。预测基于治疗前（T1）和首个周期后（T2）的影像数据，能够在首个治疗周期后早期评估化疗反应。整合深度学习衍生特征显著提高了乳腺癌患者化疗反应预测模型的性能。本研究证明了XGBoost模型卓越的预测能力，并强调其通过准确识别首个治疗周期后不太可能对化疗产生反应的患者来优化个性化治疗策略的潜力。
