Rationale and Objectives
Diagnosis of breast cancer on MRI requires, first, the identification of suspicious
lesions; second, the characterization to give a diagnostic impression. We implemented
Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed
by ResNet50 to estimate the malignancy probability.
Materials and Methods
Two datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The
second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast
image and the subtraction images of left and right breasts were used as inputs, so
the symmetry could be considered. The detected suspicious area was characterized by
ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based
analyses were combined to give a lesion-based diagnosis.
Results
In the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious,
and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity
of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious.
Following classification by ResNet50, only 16 benign and 16 normal areas remained
as malignant. The second dataset was used for independent testing. The sensitivity
was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31
benign lesions and 22 normal tissues were classified as malignant.
Conclusion
ResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN.
Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided
diagnostic system for breast cancer on MRI.
Keywords
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Article info
Publication history
Published online: January 10, 2023
Accepted:
December 23,
2022
Received in revised form:
December 10,
2022
Received:
October 3,
2022
Publication stage
In Press Corrected ProofFootnotes
Funding: This work was supported in part by NIH/NCI R01 CA127927, R21 CA208938, P30 CA062203, and the UC Irvine Comprehensive Cancer Center using UCI Anti-Cancer Challenge funds; and also supported by the Research Incubation Project of First Affiliated Hospital of Wenzhou Medical University (No. FHY2019085), Zhejiang Provincial Natural Science Foundation of China (LY21F020030), Medical Health Science and Technology Project of Zhejiang Province Health Commission (No. 2019326177).
Identification
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© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
