论文标题
PD-DWI：通过生理分解的扩散加权MRI机器学习模型预测在侵入性乳腺癌中对新辅助化疗的反应
PD-DWI: Predicting response to neoadjuvant chemotherapy in invasive breast cancer with Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model
论文作者
论文摘要
新辅助化疗（NAC）对乳腺癌的病理完全反应（PCR）的早期预测在手术计划和优化治疗策略中起着至关重要的作用。最近，提出了从多参数MRI（MP-MRI）数据（包括动态对比增强的MRI和扩散加权MRI（DWI））的多参数MRI（MP-MRI）数据的早期PCR预测的基于机器和深度学习的方法。我们介绍了PD-DWI，这是一种生理分解的DWI机器学习模型，可预测DWI和临床数据的PCR。我们的模型首先将RAW DWI数据分解为影响DWI信号的各种生理提示，然后除了临床变量之外，使用分解数据，作为基于放射线学的XGBoost模型的输入特征。我们使用公开可用的乳房多参数MRI来预测NAC响应（BMMR2）挑战的常规机器学习方法，证明了我们的PD-DWI模型的附加价值。与当前排行榜上的最佳结果（0.8849 vs. 0.8397）相比，我们的模型大大改善了曲线下的面积（AUC）。 PD-DWI有可能改善NAC乳腺癌后PCR的预测，减少MP-MRI的总体采集时间，并消除对对比剂注射的需求。
Early prediction of pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) for breast cancer plays a critical role in surgical planning and optimizing treatment strategies. Recently, machine and deep-learning based methods were suggested for early pCR prediction from multi-parametric MRI (mp-MRI) data including dynamic contrast-enhanced MRI and diffusion-weighted MRI (DWI) with moderate success. We introduce PD-DWI, a physiologically decomposed DWI machine-learning model to predict pCR from DWI and clinical data. Our model first decomposes the raw DWI data into the various physiological cues that are influencing the DWI signal and then uses the decomposed data, in addition to clinical variables, as the input features of a radiomics-based XGBoost model. We demonstrated the added-value of our PD-DWI model over conventional machine-learning approaches for pCR prediction from mp-MRI data using the publicly available Breast Multi-parametric MRI for prediction of NAC Response (BMMR2) challenge. Our model substantially improves the area under the curve (AUC), compared to the current best result on the leaderboard (0.8849 vs. 0.8397) for the challenge test set. PD-DWI has the potential to improve prediction of pCR following NAC for breast cancer, reduce overall mp-MRI acquisition times and eliminate the need for contrast-agent injection.
