Comparison of Mammography and Mammography with Supplemental Whole-Breast US Tomography for Cancer Detection in Patients with Dense Breasts.

BACKGROUND: Women with dense breasts benefit from supplemental cancer screening with US, but US has low specificity. PURPOSE: To evaluate the performance of breast US tomography (UST) combined with full-field digital mammography (FFDM) compared with FFDM alone for breast cancer screening in women with dense breasts. MATERIALS AND METHODS: This retrospective multireader multicase study included women with dense breasts who underwent FFDM and UST at 10 centers between August 2017 and October 2019 as part of a prospective case collection registry. All patients in the registry with cancer were included; patients with benign biopsy or negative follow-up imaging findings were randomly selected for inclusion. Thirty-two Mammography Quality Standards Act-qualified radiologists independently evaluated FFDM followed immediately by FFDM plus UST for suspicious findings and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. The superiority of FFDM plus UST versus FFDM alone for cancer detection (assessed with area under the receiver operating characteristic curve [AUC]), BI-RADS 4 sensitivity, and BI-RADS 3 sensitivity and specificity were evaluated using the two-sided significance level of α = .05. Noninferiority of BI-RADS 4 specificity was evaluated at the one-sided significance level of α = .025 with a -10% margin. RESULTS: Among 140 women (mean age, 56 years ±10 [SD]; 36 with cancer, 104 without), FFDM plus UST achieved superior performance compared with FFDM alone (AUC, 0.60 [95% CI: 0.51, 0.69] vs 0.54 [95% CI: 0.45, 0.64]; P = .03). For FFDM plus UST versus FFDM alone, BI-RADS 4 mean sensitivity was superior (37% [428 of 1152] vs 30% [343 of 1152]; P = .03) and BI-RADS 4 mean specificity was noninferior (82% [2741 of 3328] vs 88% [2916 of 3328]; P = .004). For FFDM plus UST versus FFDM, no difference in BI-RADS 3 mean sensitivity was observed (40% [461 of 1152] vs 33% [385 of 1152]; P = .08), but BI-RADS 3 mean specificity was superior (75% [2491 of 3328] vs 69% [2299 of 3328]; P = .04). CONCLUSION: In women with dense breasts, FFDM plus UST improved cancer detection by radiologists versus FFDM alone. Clinical trial registration nos. NCT03257839 and NCT04260620 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Mann in this issue.

Factors associated with MRI detection of occult lesions in newly diagnosed breast cancers.

BACKGROUND: The use of preoperative magnetic resonance imaging (MRI) for newly diagnosed breast cancer remains controversial. We examined factors associated with detection of occult multicentric, multifocal, and contralateral malignant lesions only seen by MRI. METHODS: We performed a retrospective analysis of consecutive patients undergoing preoperative MRI for breast cancer. Clinicopathologic data were assessed regarding the findings of multifocality, multicentricity, and the presence of contralateral lesions. We analyzed the association of factors with these findings on MRI. RESULTS: Of 857 patients undergoing MRI, 770 patients met inclusion criteria. Mean age was 54.7 years. Biopsy-proven detection rates by MRI for multifocal, multicentric, and contralateral cancers were 6.2% (48 of 770), 1.9% (15 of 770) and 3.1% (24 of 770), respectively. African American race and heterogeneously or extremely dense mammographic density were associated with multifocal cancers on MRI. Larger lesion size and mammographic density were associated with multicentric cancers. Invasive lobular carcinoma (ILC) and progesterone receptor (PR)-positivity were associated with contralateral cancers. CONCLUSIONS: African American race, heterogeneously or extremely dense mammographic density, ILC, and PR-positivity were associated with additional biopsy-proven cancers based on MRI. These factors should be considered when assessing the clinical utility of preoperative breast MRI.

Radiologist's role in breast cancer staging: providing key information for clinicians.

Breast cancer is the second leading cause of cancer death in women, exceeded only by lung cancer, and the 5-year survival rate is largely dependent on disease stage. The American Joint Committee on Cancer (AJCC) staging system for breast cancer (7th edition) provides a tumor-node-metastasis (TNM) classification scheme for breast cancer that is important for determining prognosis and treatment. Ascertaining the correct stage of breast cancer can be challenging, and the importance of the radiologist's role has increased over the years. The radiologist should understand how breast cancer stage is assigned and should be familiar with the AJCC's TNM classification scheme. The authors review the AJCC's TNM staging system for breast cancer with emphasis on clinical and preoperative staging, the different imaging modalities used in staging, and the key information that should be conveyed to clinicians. Radiologic information that may alter stage, prognosis, or treatment includes tumor size; number of tumor lesions; total span of disease; regional nodal status (axillary levels I-III, internal mammary, supraclavicular); locoregional invasion (involvement of the pectoralis muscle, skin, nipple, or chest wall); and distant metastases to bone, lung, brain, and liver, among other anatomic structures.

Empowering breast cancer diagnosis and radiology practice: advances in artificial intelligence for contrast-enhanced mammography.

Artificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.

High-frequency ultrasound imaging for breast cancer biopsy guidance.

Image-guided core needle biopsy is the current gold standard for breast cancer diagnosis. Microcalcifications, an important radiographic finding on mammography suggestive of early breast cancer such as ductal carcinoma in situ, are usually biopsied under stereotactic guidance. This procedure, however, is uncomfortable for patients and requires the use of ionizing radiation. It would be preferable to biopsy microcalcifications under ultrasound guidance since it is a faster procedure, more comfortable for the patient, and requires no radiation. However, microcalcifications cannot reliably be detected with the current standard ultrasound imaging systems. This study is motivated by the clinical need for real-time high-resolution ultrasound imaging of microcalcifications, so that biopsies can be accurately performed under ultrasound guidance. We have investigated how high-frequency ultrasound imaging can enable visualization of microstructures in ex vivo breast tissue biopsy samples. We generated B-mode images of breast tissue and applied the Nakagami filtering technique to help refine image output so that microcalcifications could be better assessed during ultrasound-guided core biopsies. We describe the preliminary clinical results of high-frequency ultrasound imaging of ex vivo breast biopsy tissue with microcalcifications and without Nakagami filtering and the correlation of these images with the pathology examination by hematoxylin and eosin stain and whole slide digital scanning.