Early Detection of Breast Cancer in MRI Using AI

Abstract

Rationale and objectives: To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women.

Materials and methods: A convolutional neural network (CNN) AI model, pre-trained on breast MRI data, was fine-tuned using a retrospective dataset of 3029 MRI scans from 910 patients. These contained 115 cancers that were diagnosed within one year of a negative MRI. The model aimed to identify these cancers, with the goal of predicting cancer development up to one year in advance. The network was fine-tuned and tested with 10-fold cross-validation. Mean age of patients was 52 years (range, 18-88 years), with average follow-up of 4.3 years (range 1-12 years).

Results: The AI detected cancers one year earlier with an area under the ROC curve of 0.72 (0.67-0.76). Retrospective analysis by a radiologist of the top 10% highest risk MRIs as ranked by the AI could have increased early detection by up to 30%. (35/115, CI:22.2-39.7%, 30% sensitivity). A radiologist identified a visual correlate to biopsy-proven cancers in 83 of prior-year MRIs (83/115, CI: 62.1-79.4%). The AI algorithm identified the anatomic region where cancer would be detected in 66 cases (66/115, CI:47.8-66.5%); with both agreeing in 54 cases (54/115, CI:%37.5-56.4%).

Conclusion: This novel AI-aided re-evaluation of "benign" breasts shows promise for improving early breast cancer detection with MRI. As datasets grow and image quality improves, this approach is expected to become even more impactful.

Keywords: Breast cancer; Deep learning; Early detection; Magnetic resonance imaging.

Figure 1.

Patient sample. Partitions for training and testing of the AI algorithm were done per patient. Results were evaluated per breast and per exam.

Figure 2.

AI-estimated probability of developing breast cancer one year in advance from the current cancer-free breast MRI in a clinical screening population, and cost-benefit analysis. (a) Cross-validation ROC curve for 12-month cancer prediction (cross-validation performance). The suggested operating point for sensitivity is selected at 30% (circle), resulting in a specificity of 90%. (b) Distribution of future screening outcomes for all breasts based on AI-derived probability from the current cancer-free MRI. The histogram is in logarithmic scale to better visualize the low prevalence of screen-detected cancers (n = 115). (c) Trade-off between the false discovery rate (FDR) and sensitivity (effectively, a “precision-recall” curve). One can select the operating point based on the desired benefit (sensitivity, vertical arrow) or alternatively, the acceptable cost (FDR, horizontal arrow). (d) Relation between FDR and AI probability to determine the decision threshold for re-evaluations. The operating point in panel A (circle) corresponds to a decision threshold of 0.64 in panel B (dotted line).

Figure 3.

Localization and predicted probability of future cancers. Each of the four panels shows the healthy breast in the current MRI (left) and the cancer in the subsequent MRI (right), with the cancer highlighted in yellow. In the current MRI, the slice is selected by AI, while in the subsequent MRI, it is selected by the radiologist. The numeric value in the top-left corner indicates the predicted one-year cancer risk for this breast. N indicates the number of screen-detected cancers in each category, totaling 115. Panels on top show true positive predictions with matching (a) or non-matching localizations (b), and panels on the left show matching localization for successful (a) or missed (c) early detections.

Figure 4.

Summary of early detection and localization results. Each circle represents the total number of breasts examined for screening. Areas are scaled to the fraction of cases. Left: of all benign exams most will remain benign in the next screening exam (green area) and a small fraction will have a cancer diagnosis (2% in orange). The AI-tool suggests to re-evaluate 10% of breasts (blue circle). Center: From all cancers that will be detected in the subsequent screening exam, the AI-tool recommends re-evaluating 30% (blue overlap: “AI: Correct detection upon re-evaluation”). The AI-tool also correctly flagged the location where cancers would be found next year in 57% of all cancers (red circle: “AI: Correct location”). Right: of the correct detections recommended for re-evaluation (blue circle), a large portion were also correctly localized (71% overlap between red circle and blue circle: “AI: Correct location and detection upon re-evaluation”).