Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1-weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max-pooling layers and ended with three dense layers. The pre-NAC model and post-NAC model inputted six phases of pre-NAC and post-NAC images, respectively. The combined model used 12 channels from six phases of pre-NAC and six phases of post-NAC images. All models above included three indexes of molecular type as one additional input channel. The training set contained 137 non-pCR and 107 pCR participants. The validation set contained 33 non-pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre-NAC, 0.968 for post-NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre-NAC data alone and combined data (P < 0.001). The positive predictive value of the combined model was greater than that of the post-NAC model (100% vs. 82.8%, P = 0.033). This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data. The model performed better than using pre-NAC data only, and also performed better than using post-NAC data only. Significant findings of the study. It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre-NAC data only. What this study adds This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data.

Qu YH ，Zhu HT ，Cao K ，Li XT ，Ye M ，Sun YS ... -  《-》

A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.

Sutton EJ ，Onishi N ，Fehr DA ，Dashevsky BZ ，Sadinski M ，Pinker K ，Martinez DF ，Brogi E ，Braunstein L ，Razavi P ，El-Tamer M ，Sacchini V ，Deasy JO ，Morris EA ，Veeraraghavan H ... -  《-》

Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm.

Ha R ，Chang P ，Karcich J ，Mutasa S ，Van Sant EP ，Connolly E ，Chin C ，Taback B ，Liu MZ ，Jambawalikar S ... -  《-》

MRI staging after neoadjuvant chemotherapy for breast cancer: does tumor biology affect accuracy?

McGuire KP ，Toro-Burguete J ，Dang H ，Young J ，Soran A ，Zuley M ，Bhargava R ，Bonaventura M ，Johnson R ，Ahrendt G ... -  《-》

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

El Adoui M ，Drisis S ，Benjelloun M 《-》