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Prospective Multicenter Diagnostic Performance of Technologist-Performed Screening Breast Ultrasound After Tomosynthesis in Women With Dense Breasts (the DBTUST)

Berg, Wendie A; Zuley, Margarita L; Chang, Thomas S; Gizienski, Terri-Ann; Chough, Denise M; Böhm-Vélez, Marcela; Sharek, Danielle E; Straka, Michelle R; Hakim, Christiane M; Hartman, Jamie Y; Harnist, Kimberly S; Tyma, Cathy S; Kelly, Amy E; Waheed, Uzma; Houshmand, Golbahar; Nair, Bronwyn E; Shinde, Dilip D; Lu, Amy H; Bandos, Andriy I; Berg, Jeremy M; Lettiere, Nicole B; Ganott, Marie A
PURPOSE:To assess diagnostic performance of digital breast tomosynthesis (DBT) alone or combined with technologist-performed handheld screening ultrasound (US) in women with dense breasts. METHODS:In an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant multicenter protocol in western Pennsylvania, 6,179 women consented to three rounds of annual screening, interpreted by two radiologist observers, and had appropriate follow-up. Primary analysis was based on first observer results. RESULTS:< .001). CONCLUSION:Overall added cancer detection rate of US screening after DBT was modest at 19/17,552 (1.1/1,000; CI, 0.5- to 1.6) screens but potentially overcomes substantial increases in false-positive recalls and benign biopsies.
PMCID:10150890
PMID: 36626696
ISSN: 1527-7755
CID: 5502172

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

Impact of Original and Artificially Improved Artificial Intelligence-based Computer-Aided Diagnosis on Breast US Interpretation

Berg, Wendie A.; Gur, David; Bandos, Andriy I.; Nair, Bronwyn; Gizienski, Terri Ann; Tyma, Cathy S.; Abrams, Gordon; Davis, Katie M.; Mehta, Amar S.; Rathfon, Grace; Waheed, Uzma X.; Hakim, Christiane M.
Objective: For breast US interpretation, to assess impact of computer-Aided diagnosis (CADx) in original mode or with improved sensitivity or specificity. Methods: In this IRB approved protocol, orthogonal-paired US images of 319 lesions identified on screening, including 88 (27.6%) cancers (median 7 mm, range 1-34 mm), were reviewed by 9 breast imaging radiologists. Each observer provided BI-RADS assessments (2, 3, 4A, 4B, 4C, 5) before and after CADx in a mode-balanced design: mode 1, original CADx (outputs benign, probably benign, suspicious, or malignant); mode 2, artificially-high-sensitivity CADx (benign or malignant); and mode 3, artificially-high-specificity CADx (benign or malignant). Area under the receiver operating characteristic curve (AUC) was estimated under each modality and for standalone CADx outputs. Multi-reader analysis accounted for inter-reader variability and correlation between same-lesion assessments. Results: AUC of standalone CADx was 0.77 (95% CI: 0.72-0.83). For mode 1, average reader AUC was 0.82 (range 0.76-0.84) without CADx and not significantly changed with CADx. In high-sensitivity mode, all observers' AUCs increased: Average AUC 0.83 (range 0.78-0.86) before CADx increased to 0.88 (range 0.84-0.90), P < 0.001. In high-specificity mode, all observers' AUCs increased: Average AUC 0.82 (range 0.76-0.84) before CADx increased to 0.89 (range 0.87-0.92), P < 0.0001. Radiologists responded more frequently to malignant CADx cues in high-specificity mode (42.7% vs 23.2% mode 1, and 27.0% mode 2, P = 0.008). Conclusion: Original CADx did not substantially impact radiologists' interpretations. Radiologists showed improved performance and were more responsive when CADx produced fewer false-positive malignant cues.
SCOPUS:85107675157
ISSN: 2631-6110
CID: 4922602

Contrast venography: reassessment of its role

Naidich, J B; Feinberg, A W; Karp-Harman, H; Karmel, M I; Tyma, C G; Stein, H L
To compare contrast venography with noninvasive methods, 353 patients clinically suspected of having deep venous thrombosis were examined with venography and independently with combined Doppler flow sounds and plethysmography. Noninvasive examinations had a sensitivity of 96% and a specificity of 90%. Positive noninvasive tests had a 94% predictive value, and negative noninvasive tests had a 93% predictive value. The overall accuracy of the noninvasive tests was 94% (331 of 353) compared with venography. Since venography itself may be subject to misinterpretation, noninvasive examinations should be the preferred initial method for diagnosing deep venous thrombosis. Venography should be reserved for situations that require additional diagnostic confirmation.
PMID: 3289098
ISSN: 0033-8419
CID: 474772