Abstract B038: A preliminary study on predicting pathological complete response to neoadjuvant chemotherapy in triple-negative breast cancer

Abstract

Abstract Purpose In this preliminary study, we are aiming to develop a deep learning model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for triple-negative breast cancer (TNBC) patient based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Background Neoadjuvant chemotherapy (NAC) has become a grown treatment method for patients with triple-negative breast cancer (TNBC). However, not every patient can achieve pathological complete response (pCR) to NAC. NAC also has side effects such as nausea, hair loss, appetite loss, mouth sores, and nerve damage. Additionally, delaying surgery to remove the tumor can allow it to spread, making treatment more difficult. Therefore, it is necessary to predict pCR to NAC reliably in a non-invasive way. Deep learning methods, especially convolutional neural network (CNN), have been shown great success in image-based treatment response prediction. Methods In this preliminary study, totally 78 TNBC patients on DCE-MRI collected from public dataset (Duke University) were used, among which are 23 patients with pCR and 55 patients without pCR. For each patient, the median slice from the tumor area of the fat-saturated gradient echo T1-weighted pre-contrast sequence MRI was extracted. A convolutional neural network model was built to predict whether the patients receive pCR or not after NAC. In the constructed prediction model, three convolutional layers were used to capture the features from DCE-MRI. Each convolutional layer was followed by a max-pooling layer to reduce the dimensions of the feature maps. Two fully connected layers were added into the model so that the model has a binary outcome which provides the prediction of pCR. Additionally, “ReLU” was used as the activation function in all the layers except for the output layer which used “softmax” instead. By using augmentation technique, we solved the problems of small sample size and unbalance data. Results In this study, 75% dataset were used for training and the remaining 25% were used for testing. The model we developed has a 75% accuracy, with a sensitivity of 0.67 and a specificity of 0.79. These results indicate that the model performs well in predicting patients who do not achieve pathological complete response (pCR). Additionally, the area under the receiver operating characteristic (AUC) curve is calculated as 0.74, indicating the outstanding performance. Citation Format: Xi Chen, Zhiguo Zhou. A preliminary study on predicting pathological complete response to neoadjuvant chemotherapy in triple-negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B038.