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29


PPV of the Molecular Breast Imaging Lexicon

Hunt, Katie N; Conners, Amy Lynn; Samreen, Naziya; Rhodes, Deborah; Johnson, Matthew P; Hruska, Carrie B
PMID: 35856455
ISSN: 1546-3141
CID: 5279072

Improving breast cancer diagnostics with deep learning for MRI

Witowski, Jan; Heacock, Laura; Reig, Beatriu; Kang, Stella K; Lewin, Alana; Pysarenko, Kristine; Patel, Shalin; Samreen, Naziya; Rudnicki, Wojciech; Łuczyńska, Elżbieta; Popiela, Tadeusz; Moy, Linda; Geras, Krzysztof J
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set (n = 3936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92 to 0.93). In a retrospective reader study, there was no statistically significant difference (P = 0.19) between five board-certified breast radiologists and the DL system (mean ΔAUROC, +0.04 in favor of the DL system). Radiologists' performance improved when their predictions were averaged with DL's predictions [mean ΔAUPRC (area under the precision-recall curve), +0.07]. We demonstrated the generalizability of the DL system using multiple datasets from Poland and the United States. An additional reader study on a Polish dataset showed that the DL system was as robust to distribution shift as radiologists. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using decision curve analysis, we showed that the DL system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding biopsies yielding benign results in up to 20% of all patients with BI-RADS category 4 lesions. Last, we performed an error analysis, investigating situations where DL predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of DL-based models for breast MRI.
PMID: 36170446
ISSN: 1946-6242
CID: 5334352

Axillary Adenopathy after COVID-19 Vaccine: No Reason to Delay Screening Mammogram

Wolfson, Stacey; Kim, Eric; Plaunova, Anastasia; Bukhman, Rita; Sarmiento, Ruth D; Samreen, Naziya; Awal, Divya; Sheth, Monica M; Toth, Hildegard B; Moy, Linda; Reig, Beatriu
PMID: 35994402
ISSN: 1527-1315
CID: 5639432

Differences between human and machine perception in medical diagnosis

Makino, Taro; Jastrzębski, Stanisław; Oleszkiewicz, Witold; Chacko, Celin; Ehrenpreis, Robin; Samreen, Naziya; Chhor, Chloe; Kim, Eric; Lee, Jiyon; Pysarenko, Kristine; Reig, Beatriu; Toth, Hildegard; Awal, Divya; Du, Linda; Kim, Alice; Park, James; Sodickson, Daniel K; Heacock, Laura; Moy, Linda; Cho, Kyunghyun; Geras, Krzysztof J
Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they use features that are grounded on medical science. It is therefore important to know whether DNNs use different features than humans. Towards this end, we propose a framework for comparing human and machine perception in medical diagnosis. We frame the comparison in terms of perturbation robustness, and mitigate Simpson's paradox by performing a subgroup analysis. The framework is demonstrated with a case study in breast cancer screening, where we separately analyze microcalcifications and soft tissue lesions. While it is inconclusive whether humans and DNNs use different features to detect microcalcifications, we find that for soft tissue lesions, DNNs rely on high frequency components ignored by radiologists. Moreover, these features are located outside of the region of the images found most suspicious by radiologists. This difference between humans and machines was only visible through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into the comparison.
PMCID:9046399
PMID: 35477730
ISSN: 2045-2322
CID: 5205672

Axillary Adenopathy after COVID-19 Vaccine: No Reason to Delay Screening Mammogram

Wolfson, Stacey; Kim, Eric; Plaunova, Anastasia; Bukhman, Rita; Sarmiento, Ruth D; Samreen, Naziya; Awal, Divya; Sheth, Monica M; Toth, Hildegard B; Moy, Linda; Reig, Beatriu
PMCID:8855316
PMID: 35133198
ISSN: 1527-1315
CID: 5156732

Non-BRCA Early-Onset Breast Cancer in Young Women

Gao, Yiming; Samreen, Naziya; Heller, Samantha L
The incidence of breast cancer in younger women is rising. Although early-onset breast cancer is highly associated with biologically aggressive tumors such as triple-negative and human epidermal growth factor 2 (HER2)-positive cancers, the more recent increase is disproportionately driven by an increase in the incidence of luminal cancer. In particular, the increase in de novo stage IV disease and the inherent age-based poorer survival rate among younger women with even early-stage luminal cancers suggest underlying distinct biologic characteristics that are not well understood. Further contributing to the higher number of early-onset breast cancers is pregnancy-associated breast cancer (PABC), which is attributed to persistent increases in maternal age over time. Although guidelines for screening of patients who carry a BRCA1 or BRCA2 gene mutation are well established, this population comprises only a fraction of those with early-onset breast cancer. A lack of screening in most young patients precludes timely diagnosis, underscoring the importance of early education and awareness. The disproportionate disease burden in young women of certain racial and ethnic groups, which is further exacerbated by socioeconomic disparity in health care, results in worse outcomes. An invited commentary by Monticciolo is available online. ©RSNA, 2022.
PMID: 34990317
ISSN: 1527-1323
CID: 5107282

Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

Shen, Yiqiu; Shamout, Farah E; Oliver, Jamie R; Witowski, Jan; Kannan, Kawshik; Park, Jungkyu; Wu, Nan; Huddleston, Connor; Wolfson, Stacey; Millet, Alexandra; Ehrenpreis, Robin; Awal, Divya; Tyma, Cathy; Samreen, Naziya; Gao, Yiming; Chhor, Chloe; Gandhi, Stacey; Lee, Cindy; Kumari-Subaiya, Sheila; Leonard, Cindy; Mohammed, Reyhan; Moczulski, Christopher; Altabet, Jaime; Babb, James; Lewin, Alana; Reig, Beatriu; Moy, Linda; Heacock, Laura; Geras, Krzysztof J
Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.
PMCID:8463596
PMID: 34561440
ISSN: 2041-1723
CID: 5039442

Screening Breast MRI Primer: Indications, Current Protocols, and Emerging Techniques

Samreen, Naziya; Mercado, Cecilia; Heacock, Laura; Chacko, Celin; Partridge, Savannah C.; Chhor, Chloe
Breast dynamic contrast-enhanced MRI (DCE-MRI) is the most sensitive imaging modality for the detection of breast cancer. Screening MRI is currently performed predominantly in patients at high risk for breast cancer, but it could be of benefit in patients at intermediate risk for breast cancer and patients with dense breasts. Decreasing scan time and image interpretation time could increase cost-effectiveness, making screening MRI accessible to a larger group of patients. Abbreviated breast MRI (Ab-MRI) reduces scan time by decreasing the number of sequences obtained, but as multiple delayed contrast enhanced sequences are not obtained, no kinetic information is available. Ultrafast techniques rapidly acquire multiple sequences during the first minute of gadolinium contrast injection and provide information about both lesion morphology and vascular kinetics. Diffusion-weighted imaging is a noncontrast MRI technique with the potential to detect mammographically occult cancers. This review article aims to discuss the current indications of breast MRI as a screening tool, examine the standard breast DCE-MRI technique, and explore alternate screening MRI protocols, including Ab-MRI, ultrafast MRI, and noncontrast diffusion-weighted MRI, which can decrease scan time and interpretation time.
SCOPUS:85107675031
ISSN: 2631-6110
CID: 4922592

Magnetic resonance imaging in the evaluation of pathologic nipple discharge: indications and imaging findings

Samreen, Naziya; Madsen, Laura B; Chacko, Celin; Heller, Samantha L
Pathologic nipple discharge (PND) is typically unilateral, spontaneous, involves a single duct, and is serous or bloody in appearance. In patients with PND, breast MRI can be helpful as an additional diagnostic tool when conventional imaging with mammogram and ultrasound are negative. MRI is able to detect the etiology of nipple discharge in 56-61% of cases when initial imaging with mammogram and ultrasound are negative. Advantages to using MRI in evaluation of PND include good visualization of the retroareolar breast and better evaluation of posterior lesions which may not be well evaluated on mammograms and galactograms. It is also less invasive compared to central duct excision. Papillomas and nipple adenomas are benign breast masses that can cause PND and are well visualized on MRI. Ductal ectasia, and infectious etiologies such as mastitis, abscess, and fistulas are additional benign causes of PND that are well evaluated with MRI. MRI is also excellent for evaluation of malignant causes of PND including Paget's disease, ductal carcinoma in-situ and invasive carcinoma. MRI's high negative predictive value of 87-98.2% is helpful in excluding malignant etiologies of PND.
PMID: 33544650
ISSN: 1748-880x
CID: 4776732

MR Elastography of the Breast: Evolution of Technique, Case Examples, and Future Directions

Patel, Bhavika K; Samreen, Naziya; Zhou, Yuxiang; Chen, Jun; Brandt, Kathy; Ehman, Richard; Pepin, Kay
Recognizing that breast cancers present as firm, stiff lesions, the foundation of breast magnetic resonance elastography (MRE) is to combine tissue stiffness parameters with sensitive breast MR contrast-enhanced imaging. Breast MRE is a non-ionizing, cross-sectional MR imaging technique that provides for quantitative viscoelastic properties, including tissue stiffness, elasticity, and viscosity, of breast tissues. Currently, the technique continues to evolve as research surrounding the use of MRE in breast tissue is still developing. In the setting of a newly diagnosed cancer, associated desmoplasia, stiffening of the surrounding stroma, and necrosis are known to be prognostic factors that can add diagnostic information to patient treatment algorithms. In fact, mechanical properties of the tissue might also influence breast cancer risk. For these reasons, exploration of breast MRE has great clinical value. In this review, we will: (1) address the evolution of the various MRE techniques; (2) provide a brief overview of the current clinical studies in breast MRE with interspersed case examples; and (3) suggest directions for future research.
PMID: 32900617
ISSN: 1938-0666
CID: 4589022