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

Abstract

Background 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. Methods 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. Results 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). Conclusion 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. Key points 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.

Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response

“…They showed that imaging and clinical parameters boosted the performance of Bayesian logistic regression. Qu et al [ 12 ] predicted PCR to NAC in breast cancer with two combined time points, using a multipath deep convolutional neural network and obtained a similar AUC as our current study. However, they did not include molecular subtypes.…”

“…Pre-, early, and mid-treatment MRI data alone yielded moderate PCR prediction accuracy, consistent with a previous study that used tumor volumes at different time points to predict PCR [ 7 ] in which they found an AUC at pre- and post-treatment time points to be 0.7 and 0.73, respectively. Our study differed from the study by Hylton et al [ 7 ] and most previous studies [ 12 – 17 ] in that we used machine-learning classification and we incorporated additional input parameters (such as molecular subtypes, data of different time points, and peri-tumoral features among others) into our prediction model.…”

Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations

“…52 Monitoring and prognostication MRI is routinely used in the monitoring of response to neoadjuvant chemotherapy, with patients imaged before, during, and after treatment. Deep learning algorithms have been implemented to evaluate pathological complete response to chemotherapy using post-treatment MRI with an AUC of 0.98, 53 which could affect the extent of post-treatment surgery, or potentially reduce the need for surgical excision at all. A number of studies have used deep learning to identify features from pre-treatment MRI that are predictive of response in an unsupervised fashion.…”

Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept

“…Tahmassebi et al conducted a study using machine learning based on both pre-and during-NACT MRI (N = 38) with residual cancer burden as an outcome measure (with class zero being defined as pCR), which yielded an AUC of 0.86 [26]. A study by Qu et al presented results of a deep learning-based method applied to MRI (N/training = 244, N/validation = 58) using pCR as an outcome measure and showed an AUC of 0.55 using pre-NACT data in comparison to an AUC of 0.97 when using post-NACT data or the combination of both pre-and post-NACT MRI [27]. Sutton et al applied machine learning to pre-and post-NACT MRI (N/training = 222, N/validation = 56) and showed an AUC between 0.78 and 0.83.…”