Accurate prediction of response to neoadjuvant chemotherapy (NAC) can help tailor treatment to individual patients’ needs. Little is known about the combination of liquid biopsies and computer extracted features from multiparametric magnetic resonance imaging (MRI) for the prediction of NAC response in breast cancer. Here, we report on a prospective study with the aim to explore the predictive potential of this combination in adjunct to standard clinical and pathological information before, during and after NAC. The study was performed in four Dutch hospitals. Patients without metastases treated with NAC underwent 3 T multiparametric MRI scans before, during and after NAC. Liquid biopsies were obtained before every chemotherapy cycle and before surgery. Prediction models were developed using penalized linear regression to forecast residual cancer burden after NAC and evaluated for pathologic complete response (pCR) using leave-one-out-cross-validation (LOOCV). Sixty-one patients were included. Twenty-three patients (38%) achieved pCR. Most prediction models yielded the highest estimated LOOCV area under the curve (AUC) at the post-treatment timepoint. A clinical-only model including tumor grade, nodal status and receptor subtype yielded an estimated LOOCV AUC for pCR of 0.76, which increased to 0.82 by incorporating post-treatment radiological MRI assessment (i.e., the “clinical-radiological” model). The estimated LOOCV AUC was 0.84 after incorporation of computer-extracted MRI features, and 0.85 when liquid biopsy information was added instead of the radiological MRI assessment. Adding liquid biopsy information to the clinical-radiological resulted in an estimated LOOCV AUC of 0.86. In conclusion, inclusion of liquid biopsy-derived markers in clinical-radiological prediction models may have potential to improve prediction of pCR after NAC in breast cancer.

准确预测新辅助化疗 (NAC) 的反应有助于根据个体患者的需求制定治疗方案。对于如何结合液体活检和计算机从多参数磁共振成像 (MRI) 提取的特征来预测乳腺癌中的 NAC 反应，人们知之甚少。在这里，我们报告了一项前瞻性研究，旨在探索这种组合在 NAC 之前、期间和之后辅助标准临床和病理信息的预测潜力。该研究在荷兰四家医院进行。接受 NAC 治疗的无转移患者在 NAC 之前、期间和之后接受 3 T 多参数 MRI 扫描。在每个化疗周期之前和手术之前进行液体活检。使用惩罚线性回归开发预测模型，以预测 NAC 后残留的癌症负担，并使用留一交叉验证 (LOOCV) 评估病理完全缓解 (pCR)。其中包括六十一名患者。23 名患者 (38%) 达到 pCR。大多数预测模型在治疗后时间点产生最高的估计 LOOCV 曲线下面积 (AUC)。包括肿瘤分级、淋巴结状态和受体亚型的纯临床模型产生的 pCR 的 LOOCV AUC 估计为 0.76，通过纳入治疗后放射学 MRI 评估（即“临床放射学”模型），该值增加到 0.82。合并计算机提取的 MRI 特征后，估计的 LOOCV AUC 为 0.84，当添加液体活检信息而不是放射学 MRI 评估时，估计的 LOOCV AUC 为 0.85。将液体活检信息添加到临床放射学结果中，估计的 LOOCV AUC 为 0.86。总之，将液体活检衍生的标记物纳入临床放射学预测模型可能有可能改善乳腺癌 NAC 后 pCR 的预测。