Janse Markus H A, Janssen Liselore M, Wolters-van der Ben Elian J M, Moman Maaike R, Viergever Max A, van Diest Paul J, Gilhuijs Kenneth G A
Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Department of Radiology, St. Antonius Hospital, Nieuwegein, The Netherlands.
Eur Radiol. 2025 Aug 6. doi: 10.1007/s00330-025-11801-z.
This study aimed to evaluate the potential additional value of deep radiomics for assessing residual cancer burden (RCB) in locally advanced breast cancer, after neoadjuvant chemotherapy (NAC) but before surgery, compared to standard predictors: tumor volume and subtype.
This retrospective study used a 105-patient single-institution training set and a 41-patient external test set from three institutions in the LIMA trial. DCE-MRI was performed before and after NAC, and RCB was determined post-surgery. Three networks (nnU-Net, Attention U-net and vector-quantized encoder-decoder) were trained for tumor segmentation. For each network, deep features were extracted from the bottleneck layer and used to train random forest regression models to predict RCB score. Models were compared to (1) a model trained on tumor volume and (2) a model combining tumor volume and subtype. The potential complementary performance of combining deep radiomics with a clinical-radiological model was assessed. From the predicted RCB score, three metrics were calculated: area under the curve (AUC) for categories RCB-0/RCB-I versus RCB-II/III, pathological complete response (pCR) versus non-pCR, and Spearman's correlation.
Deep radiomics models had an AUC between 0.68-0.74 for pCR and 0.68-0.79 for RCB, while the volume-only model had an AUC of 0.74 and 0.70 for pCR and RCB, respectively. Spearman's correlation varied from 0.45-0.51 (deep radiomics) to 0.53 (combined model). No statistical difference between models was observed.
Segmentation network-derived deep radiomics contain similar information to tumor volume and subtype for inferring pCR and RCB after NAC, but do not complement standard clinical predictors in the LIMA trial.
Question It is unknown if and which deep radiomics approach is most suitable to extract relevant features to assess neoadjuvant chemotherapy response on breast MRI. Findings Radiomic features extracted from deep-learning networks yield similar results in predicting neoadjuvant chemotherapy response as tumor volume and subtype in the LIMA study. However, they do not provide complementary information. Clinical relevance For predicting response to neoadjuvant chemotherapy in breast cancer patients, tumor volume on MRI and subtype remain important predictors of treatment outcome; deep radiomics might be an alternative when determining tumor volume and/or subtype is not feasible.
本研究旨在评估与标准预测指标（肿瘤体积和亚型）相比，深度放射组学在评估局部晚期乳腺癌新辅助化疗（NAC）后但手术前的残留癌负担（RCB）方面的潜在附加价值。
这项回顾性研究使用了LIMA试验中来自三个机构的105例患者的单机构训练集和41例患者的外部测试集。在NAC前后进行了动态对比增强磁共振成像（DCE-MRI），并在手术后确定了RCB。训练了三个网络（nnU-Net、注意力U-Net和矢量量化编码器-解码器）用于肿瘤分割。对于每个网络，从瓶颈层提取深度特征，并用于训练随机森林回归模型以预测RCB评分。将模型与（1）基于肿瘤体积训练的模型和（2）结合肿瘤体积和亚型的模型进行比较。评估了将深度放射组学与临床放射学模型相结合的潜在互补性能。根据预测的RCB评分，计算了三个指标：RCB-0/RCB-I与RCB-II/III类别、病理完全缓解（pCR）与非pCR的曲线下面积（AUC）以及Spearman相关性。
深度放射组学模型对于pCR的AUC在0.68 - 0.74之间，对于RCB的AUC在0.68 - 0.79之间，而仅基于体积的模型对于pCR和RCB的AUC分别为0.74和0.70。Spearman相关性从0.45 - 0.51（深度放射组学）到0.53（联合模型）不等。未观察到模型之间的统计学差异。
分割网络衍生的深度放射组学在推断NAC后的pCR和RCB方面包含与肿瘤体积和亚型相似的信息，但在LIMA试验中不能补充标准临床预测指标。
问题尚不清楚哪种深度放射组学方法最适合提取相关特征以评估乳腺MRI上新辅助化疗的反应。发现从深度学习网络提取的放射组学特征在预测新辅助化疗反应方面产生的结果与LIMA研究中的肿瘤体积和亚型相似。然而，它们并未提供补充信息。临床相关性对于预测乳腺癌患者新辅助化疗的反应，MRI上的肿瘤体积和亚型仍然是治疗结果的重要预测指标；当确定肿瘤体积和/或亚型不可行时，深度放射组学可能是一种替代方法。
