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Radiomics and deep learning methods in expanding the use of screening breast MRI [Editorial]

Reig, Beatriu
KEY POINTS/CONCLUSIONS:• The use of screening breast MRI is expanding beyond high-risk women to include intermediate- and average-risk women.• The study by Pötsch et al uses a radiomics-based method to decrease the number of benign biopsies while maintaining high sensitivity.• Future studies will likely increasingly focus on deep learning methods and abbreviated MRI data.
PMID: 34014381
ISSN: 1432-1084
CID: 4877452

Breast MRI for Evaluation of Response to Neoadjuvant Therapy

Reig, Beatriu; Lewin, Alana A; Du, Linda; Heacock, Laura; Toth, Hildegard K; Heller, Samantha L; Gao, Yiming; Moy, Linda
Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and human epidermal growth factor 2-overexpressing breast cancers, as well as locally advanced and inflammatory breast cancers. The rationales for neoadjuvant therapy are to shrink tumor size and potentially decrease the extent of surgery, to serve as an in vivo test of response to therapy, and to reveal prognostic information for the patient. MRI is the most accurate modality to demonstrate response to therapy and to help ensure accurate presurgical planning. Changes in lesion diameter, volume, and enhancement are used to predict complete response, partial response, or nonresponse to therapy. However, residual disease may be overestimated or underestimated at MRI. Fibrosis, necrotic tumors, and residual benign masses may be causes of overestimation of residual disease. Nonmass lesions, invasive lobular carcinoma, hormone receptor-positive tumors, nonconcentric shrinkage patterns, the use of antiangiogenic therapy, and late-enhancing foci may be causes of underestimation of residual disease. In patients with known axillary lymph node metastasis, neoadjuvant therapy may be followed by targeted axillary dissection to avoid the potential morbidity associated with an axillary lymph node dissection. Diffusion-weighted imaging, radiomics, machine learning, and deep learning methods are under investigation to improve MRI accuracy in predicting treatment response.©RSNA, 2021.
PMID: 33939542
ISSN: 1527-1323
CID: 4858892

Magnetic Resonance Imaging in Screening of Breast Cancer

Gao, Yiming; Reig, Beatriu; Heacock, Laura; Bennett, Debbie L; Heller, Samantha L; Moy, Linda
Magnetic Resonance (MR) imaging is the most sensitive modality for breast cancer detection but is currently limited to screening women at high risk due to limited specificity and test accessibility. However, specificity of MR imaging improves with successive rounds of screening, and abbreviated approaches have the potential to increase access and decrease cost. There is growing evidence to support supplemental MR imaging in moderate-risk women, and current guidelines continue to evolve. Functional imaging has the potential to maximize survival benefit of screening. Leveraging MR imaging as a possible primary screening tool is therefore also being investigated in average-risk women.
PMID: 33223002
ISSN: 1557-8275
CID: 4676352

Abbreviated MR Imaging for Breast Cancer

Heacock, Laura; Lewin, Alana A; Toth, Hildegard K; Moy, Linda; Reig, Beatriu
Breast MR imaging is the most sensitive imaging method for the detection of breast cancer and detects more aggressive malignancies than mammography and ultrasound examination. Despite these advantages, breast MR imaging has low use rates for breast cancer screening. Abbreviated breast MR imaging, in which a limited number of breast imaging sequences are obtained, has been proposed as a way to solve cost and patient tolerance issues while preserving the high cancer detection rate of breast MR imaging. This review discusses abbreviated breast MR imaging, including protocols, multicenter clinical trial results, clinical workflow implementation challenges, and future directions.
PMID: 33223003
ISSN: 1557-8275
CID: 4680132

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

Reig, Beatriu; Heacock, Laura; Lewin, Alana; Cho, Nariya; Moy, Linda
The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting response on pretreatment imaging. This article comprehensively reviews response assessment on breast MRI and highlights areas of ongoing research. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.
PMID: 32227407
ISSN: 1522-2586
CID: 4370022

Machine learning in breast MRI

Reig, Beatriu; Heacock, Laura; Geras, Krzysztof J; Moy, Linda
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019.
PMID: 31276247
ISSN: 1522-2586
CID: 3968372

Abbreviated breast MRI: Road to clinical implementation

Heacock, Laura; Reig, Beatriu; Lewin, Alana A.; Toth, Hildegard K.; Moy, Linda; Lee, Cindy S.
Breast MRI offers high sensitivity for breast cancer detection, with preferential detection of high-grade invasive cancers when compared to mammography and ultrasound. Despite the clear benefits of breast MRI in cancer screening, its cost, patient tolerance, and low utilization remain key issues. Abbreviated breast MRI, in which only a select number of sequences and postcontrast imaging are acquired, exploits the high sensitivity of breast MRI while reducing table time and reading time to maximize availability, patient tolerance, and accessibility. Worldwide studies of varying patient populations have demonstrated that the comparable diagnostic accuracy of abbreviated breast MRI is comparable to a full diagnostic protocol, highlighting the emerging role of abbreviated MRI screening in patients with an intermediate and high lifetime risk of breast cancer. The purpose of this review is to summarize the background and current literature relating to abbreviated MRI, highlight various protocols utilized in current multicenter clinical trials, describe workflow and clinical implementation issues, and discuss the future of abbreviated protocols, including advanced MRI techniques.
ISSN: 2631-6110
CID: 4612692

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Wu, Nan; Phang, Jason; Park, Jungkyu; Shen, Yiqiu; Huang, Zhe; Zorin, Masha; Jastrzebski, Stanislaw; Fevry, Thibault; Katsnelson, Joe; Kim, Eric; Wolfson, Stacey; Parikh, Ujas; Gaddam, Sushma; Lin, Leng Leng Young; Ho, Kara; Weinstein, Joshua D; Reig, Beatriu; Gao, Yiming; Pysarenko, Hildegard Toth Kristine; Lewin, Alana; Lee, Jiyon; Airola, Krystal; Mema, Eralda; Chung, Stephanie; Hwang, Esther; Samreen, Naziya; Kim, S Gene; Heacock, Laura; Moy, Linda; Cho, Kyunghyun; Geras, Krzysztof J
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. (i) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. (ii) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. (iii) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. (iv) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at
PMID: 31603772
ISSN: 1558-254x
CID: 4130202

Editorial on "Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue" [Editorial]

Reig, Beatriu; Ha, Richard
PMID: 31846141
ISSN: 1522-2586
CID: 4242422

Core Biopsy of Vascular Neoplasms of the Breast: Pathologic Features, Imaging, and Clinical Findings

Mantilla, Jose G; Koenigsberg, Tova; Reig, Beatriu; Shapiro, Nella; Villanueva-Siles, Esperanza; Fineberg, Susan
Vascular lesions (VLs) of the breast present a diagnostic challenge on breast core biopsy (BCBx). We report on 27 VLs presenting on BCBx. The mean patient age was 60 years, and mean size was 7.5 mm (range, 1.6 to 16 mm). Presentation included palpable mass in 6 (22%), incidental in 6 (22%), and an imaging abnormality in 15 (56%) cases. Imaging impression included hematoma (24%), lymph node (10%), fat necrosis (10%), tortuous vessel (5%), and not provided in 52%. The lesions were classified on the basis of BCBx or BCBx and excision (available in 16 pts) as follows: 1 low-grade angiosarcoma, 8 angiolipomas, 6 capillary hemangiomas, 4 cavernous hemangiomas, 2 hemangiomas (not otherwise specified), 1 papillary endothelial hyperplasia, and 5 perilobular hemangiomas. The angiosarcoma was 9 mm, detected incidentally by magnetic resonance imaging, and showed dissection of stromal collagen, infiltration of glands, high cellularity, moderate cytologic atypia, scant mitotic activity, and Ki-67 reactivity of 10%. Among the 26 benign VLs, worrisome histologic features were noted in 14 on BCBx, including anastomosing vascular channels in 9, moderate cytologic atypia in 4, high cellularity in 2, Ki-67>10% in 2, mitotic activity in 1, and infiltration of glands in 1. Of the 12 VLs without worrisome features, the lesion extended to edge of core in 8, precluding complete evaluation. BCBx of VLs presents diagnostic challenges due to overlapping clinicopathologic and radiologic features with low-grade angiosarcoma. If completeness of removal is documented on BCBx, and cytoarchitectural changes are not worrisome, follow-up could be considered rather than excision. However, only 4 of these cases fulfilled those criteria.
PMID: 27340752
ISSN: 1532-0979
CID: 3184022