Deep Learning for Early Breast Cancer Detection on Contrast-Enhanced Breast MRI: A Multicenter Study

Background/Objectives: Accurate visual categorization of small enhancing lesions on contrast-enhanced breast MRI remains challenging. The purpose of this study was to develop a deep learning (DL) model for detecting small (≤2 cm) invasive breast cancers using multi-institutional breast MRI data. Methods: This retrospective study included a total of 1721 women (mean age: 54 years; range, 21-87 years) with T1-stage invasive breast cancer across five hospitals. All patients underwent dynamic contrast-enhanced breast MRI before surgery between 2010 and 2020. Enhancing breast masses on early postcontrast T1-weighted images (14,917 labeled cancer images and 1443 labeled benign images) were used as the ground truth. In this multicenter cancer-enriched cohort, a DL model was developed to distinguish cancerous lesions from noncancerous lesions. MR images were independently reviewed by three breast imaging radiologists to detect subcentimeter (≤1 cm) cancers, and the readers were informed that each examination contained a single cancer. The performance of the model was evaluated using the area under the precision-recall curve (AUPRC), sensitivity, precision, and F1 score. Results: The DL model achieved an AUPRC of 0.42 in detecting small (≤2 cm) invasive breast cancers presenting as enhancing masses on MRI (sensitivity, 83.2%; precision, 33.2%; F1 score, 0.47). For subcentimeter (≤1 cm) cancers, the detection performance of the model was lower (sensitivity, 75.3%; precision, 20.8%; F1 score, 0.33) than that of the radiologists (mean sensitivity, 89.5%; mean precision, 72.9%; mean F1 score, 0.80). When the radiologists used the DL model, their mean precision in detecting subcentimeter invasive breast cancers improved from 72.9% to 83.2%, with no significant change in sensitivity (89.5% vs. 86.8%, p > 0.05). Conclusions: The DL model demonstrated potential as an assistive tool by improving radiologists' precision for detecting small invasive breast cancers on contrast-enhanced breast MRI in this enriched reader-study setting, although it did not significantly improve their sensitivity.