Machireddy Archana, Thibault Guillaume, Huang Wei, Song Xubo
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:682-685. doi: 10.1109/EMBC.2018.8512301.
Positive response to neoadjuvant chemotherapy (NACT) has been correlated to better long-term outcomes in breast cancer treatment. Early prediction of response to NACT can help modify the regimen for non-responding patients, sparing them of potential toxicities of ineffective therapies. It has been observed that tumor functions such as vascularization and vascular permeability change even before noticeable changes occur in the tumor size in response to the treatment. Therefore, it is essential to have reliable imaging based features to measure these changes. Texture analysis on parametric maps from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown to be a good predictor of breast cancer response to NACT at an early stage. But hand crafted texture features might not be able to capture the rich spatio-temporal information in the parametric maps. In this work, we studied the ability of convolutional neural networks in predicting the response to NACT at an early stage.
新辅助化疗（NACT）的阳性反应与乳腺癌治疗中更好的长期预后相关。对NACT反应的早期预测有助于为无反应患者调整治疗方案，避免他们遭受无效治疗的潜在毒性。据观察，在肿瘤大小因治疗而出现明显变化之前，诸如血管生成和血管通透性等肿瘤功能就已发生改变。因此，拥有可靠的基于成像的特征来测量这些变化至关重要。动态对比增强磁共振成像（DCE-MRI）参数图的纹理分析已被证明是乳腺癌对NACT早期反应的良好预测指标。但是手工制作的纹理特征可能无法捕捉参数图中丰富的时空信息。在这项工作中，我们研究了卷积神经网络在早期预测对NACT反应的能力。
