High-resolution diffusion-weighted MRI plus mammography for detecting clinically occult breast cancers in women with dense breasts.

PURPOSE: To compare the performance of mammography, high-resolution DW-MRI, DCE-MRI, and their combinations in detecting clinically occult breast cancer in women with dense breasts. METHOD: 544 breasts from 281 consecutive asymptomatic women with dense breasts were retrospectively identified. They underwent breast MRI for preoperative evaluation of breast cancers (n = 214) or as supplemental screening (n = 67) including DCE-MRI and DW-MRI (b values, 0 and 1000 sec/mm2; in-plane resolution, 1.1 × 1.1 mm2 and 1.3 × 1.3 mm2; section thickness, 3 mm), in addition to mammography. Three readers independently reviewed each examination on a per-breast basis. Histopathology and at least two year of imaging follow-up served as the gold standard. The sensitivities and specificities of different imaging modalities were compared using McNemar test. RESULTS: 230 of 544 breasts (42 %) had malignant lesions. The sensitivity of DW-MRI was higher than that of mammography (77.0 % vs 57.9 %; adjusted p < 0.001), but lower than that of DCE-MRI (84.8 %; adjusted p = 0.014). The specificity of DW-MRI was comparable to those of mammography (98.1 % vs 99.1 %; adjusted p > 0.999) and DCE-MRI (97.1 %; adjusted p > 0.999). DW-MRI plus mammography had a comparable sensitivity and specificity to those of DCE-MRI plus mammography (88.6 % vs 90.9 % and 97.1 % vs 96.2 %; adjusted p > 0.999 for both). CONCLUSIONS: High-resolution DW-MRI had a sensitivity higher than mammography and lower than DCE-MRI. Nevertheless, DW-MRI plus mammography showed a comparable sensitivity and specificity to DCE-MRI plus mammography for detecting clinically occult cancers in women with dense breasts.

Mammographic density changes after neoadjuvant chemotherapy in triple-negative breast cancer: Association with treatment and survival outcome.

PURPOSE: To investigate the association of mammographic breast density with treatment and survival outcomes in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC). METHODS: This retrospective study evaluated 306 women with TNBC who underwent NAC followed by surgery between 2010 and 2019. The baseline density and the density changes after NAC were evaluated. Qualitative breast density (a-d) was evaluated using the Breast Imaging Reporting and Data System. Quantitative breast density (%) was evaluated using fully automated software (the Laboratory for Individualized Breast Radiodensity Assessment) in the contralateral breast. Multivariable logistic regression analysis was used to evaluate the association between breast density and pathologic complete response (pCR), stratified by menopausal status. Cox proportional hazard regression analysis was used to evaluate the association among breast density, the development of contralateral breast cancer, and the development of locoregional recurrence and/or distant metastasis. RESULTS: Contralateral density reduction ≥10 % was independently associated with pCR in premenopausal women (odds ratio [OR], 2.5; p = 0.022) but not in postmenopausal women (OR, 0.9; p = 0.823). During a mean follow-up of 65 months, 10 (3 %) women developed contralateral breast cancer, and 68 (22 %) women developed locoregional recurrences and/or distant metastases. Contralateral density reduction ≥10 % showed no association with the occurrence of contralateral breast cancer (hazard ratio [HR], 3.1; p = 0.308) or with locoregional recurrence and/or distant metastasis (HR, 1.1; p = 0.794). CONCLUSION: In premenopausal women, a contralateral breast density reduction of ≥10 % after NAC was independently associated with pCR, although it did not translate into improved outcomes.

Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection.

RATIONALE AND OBJECTIVES: Little is known about the factors affecting the Artificial Intelligence (AI) software performance on mammography for breast cancer detection. This study was to identify factors associated with abnormality scores assigned by the AI software. MATERIALS AND METHODS: A retrospective database search was conducted to identify consecutive asymptomatic women who underwent breast cancer surgery between April 2016 and December 2019. A commercially available AI software (Lunit INSIGHT, MMG, Ver. 1.1.4.0) was used for preoperative mammography to assign individual abnormality scores to the lesions and score of 10 or higher was considered as positive detection by AI software. Radiologists without knowledge of the AI results retrospectively assessed the mammographic density and classified mammographic findings into positive and negative finding. General linear model (GLM) analysis was used to identify the clinical, pathological, and mammographic findings related to the abnormality scores, obtaining coefficient ß values that represent the mean difference per unit or comparison with the reference value. Additionally, the reasons for non-detection by the AI software were investigated. RESULTS: Among the 1001 index cancers (830 invasive cancers and 171 ductal carcinoma in situs) in 1001 patients, 717 (72%) were correctly detected by AI, while the remaining 284 (28%) were not detected. Multivariable GLM analysis showed that abnormal mammography findings (ß = 77.0 for mass, ß = 73.1 for calcification only, ß = 49.4 for architectural distortion, and ß = 47.6 for asymmetry compared to negative; all Ps < 0.001), invasive tumor size (ß = 4.3 per 1 cm, P < 0.001), and human epidermal growth receptor type 2 (HER2) positivity (ß = 9.2 compared to hormone receptor positive, HER2 negative, P = 0.004) were associated with higher mean abnormality score. AI failed to detect small asymmetries in extremely dense breasts, subcentimeter-sized or isodense lesions, and faint amorphous calcifications. CONCLUSION: Cancers with positive abnormal mammographic findings on retrospective review, large invasive size, HER2 positivity had high AI abnormality scores. Understanding the patterns of AI software performance is crucial for effectively integrating AI into clinical practice.

Unenhanced Breast MRI With Diffusion-Weighted Imaging for Breast Cancer Detection: Effects of Training on Performance and Agreement of Subspecialty Radiologists.

OBJECTIVE: To investigate whether reader training improves the performance and agreement of radiologists in interpreting unenhanced breast magnetic resonance imaging (MRI) scans using diffusion-weighted imaging (DWI). MATERIALS AND METHODS: A study of 96 breasts (35 cancers, 24 benign, and 37 negative) in 48 asymptomatic women was performed between June 2019 and October 2020. High-resolution DWI with b-values of 0, 800, and 1200 sec/mm² was performed using a 3.0-T system. Sixteen breast radiologists independently reviewed the DWI, apparent diffusion coefficient maps, and T1-weighted MRI scans and recorded the Breast Imaging Reporting and Data System (BI-RADS) category for each breast. After a 2-h training session and a 5-month washout period, they re-evaluated the BI-RADS categories. A BI-RADS category of 4 (lesions with at least two suspicious criteria) or 5 (more than two suspicious criteria) was considered positive. The per-breast diagnostic performance of each reader was compared between the first and second reviews. Inter-reader agreement was evaluated using a multi-rater κ analysis and intraclass correlation coefficient (ICC). RESULTS: Before training, the mean sensitivity, specificity, and accuracy of the 16 readers were 70.7% (95% confidence interval [CI]: 59.4-79.9), 90.8% (95% CI: 85.6-94.2), and 83.5% (95% CI: 78.6-87.4), respectively. After training, significant improvements in specificity (95.2%; 95% CI: 90.8-97.5; P = 0.001) and accuracy (85.9%; 95% CI: 80.9-89.8; P = 0.01) were observed, but no difference in sensitivity (69.8%; 95% CI: 58.1-79.4; P = 0.58) was observed. Regarding inter-reader agreement, the κ values were 0.57 (95% CI: 0.52-0.63) before training and 0.68 (95% CI: 0.62-0.74) after training, with a difference of 0.11 (95% CI: 0.02-0.18; P = 0.01). The ICC was 0.73 (95% CI: 0.69-0.74) before training and 0.79 (95% CI: 0.76-0.80) after training (P = 0.002). CONCLUSION: Brief reader training improved the performance and agreement of interpretations by breast radiologists using unenhanced MRI with DWI.

Nonmass Lesions on Breast US: An International Perspective on Clinical Use and Outcomes.

Nonmass lesions (NMLs) on breast US are defined as discrete areas of altered echotexture compared to surrounding breast tissue and lack the 3-dimensionality of a mass. They are not a component of American College of Radiology BI-RADS, but they are a finding type included in the Japan Association of Breast and Thyroid Sonology lexicon. Use of the NML finding is routine in many Asian practices, including the Samsung Medical Center and Seoul National University Hospital, and their features and outcomes have been investigated in multiple studies. Nonmass lesions are most often observed when US is used to evaluate mammographic asymmetries, suspicious calcifications, and nonmass enhancement on MRI and contrast-enhanced mammography. Nonmass lesions can be described by their echogenicity, distribution, presence or absence of associated calcifications, abnormal duct changes, architectural distortion, posterior shadowing, small cysts, and hypervascularity. Malignant lesions, especially ductal carcinoma in situ, can manifest as NMLs on US. There is considerable overlap between the US features of benign and malignant NMLs, and they also must be distinguished from normal variants. The literature indicates that NMLs with linear or segmental distribution, associated calcifications, abnormal duct changes, posterior shadowing, and hypervascularity are suggestive of malignancy, whereas NMLs with only interspersed small cysts are usually benign fibrocystic changes. In this article, we introduce the concepts of NMLs, illustrate US features suggestive of benign and malignant etiologies, and discuss our institutional approach for evaluating NMLs and an algorithm that we use to guide interpretation in clinical practice.

Beyond BI-RADS: Nonmass Abnormalities on Breast Ultrasound.

Abnormalities on breast ultrasound (US) images which do not meet the criteria for masses are referred to as nonmass lesions. These features and outcomes have been investigated in several studies conducted by Asian researchers. However, the term "nonmass" is not included in the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) 5th edition for US. According to the Japan Association of Breast and Thyroid Sonology guidelines, breast lesions are divided into mass and nonmass. US findings of nonmass abnormalities are classified into five subtypes: abnormalities of the ducts, hypoechoic areas in the mammary glands, architectural distortion, multiple small cysts, and echogenic foci without a hypoechoic area. These findings can be benign or malignant; however, focal or segmental distributions and presence of calcifications suggest malignancy. Intraductal, invasive ductal, and lobular carcinomas can present as nonmass abnormalities. For the nonmass concept to be included in the next BI-RADS and be widely accepted in clinical practice, standardized terminologies, an interpretation algorithm, and outcome-based evidence are required for both screening and diagnostic US.

Hemorrhagic Complications Following Ultrasound-Guided Breast Biopsy: A Prospective Patient-Centered Study.

OBJECTIVE: We aimed to evaluate the clinical and imaging factors associated with hemorrhagic complications and patient discomfort following ultrasound (US)-guided breast biopsy. MATERIALS AND METHODS: We prospectively enrolled 94 patients who were referred to our hospital between June 2022 and December 2022 for US-guided breast biopsy. After obtaining informed consent, two breast radiologists independently performed US-guided breast biopsy and evaluated the imaging findings. A hemorrhagic complication was defined as the presence of bleeding or hematoma on US. The patients rated symptoms of pain, febrile sensation, swelling at the biopsy site, and dyspnea immediately, 20 minutes, and 2 weeks after the procedure on a visual analog scale, with 0 for none and 10 for the most severe symptoms. Additional details recorded included those of nausea, vomiting, bleeding, bruising, and overall satisfaction score. We compared the clinical symptoms, imaging characteristics, and procedural features between patients with and those without hemorrhagic complications. RESULTS: Of 94 patients, 7 (7%) developed hemorrhagic complications, while 87 (93%) did not. The complication resolved with 20 minutes of manual compression, and no further intervention was required. Vascularity on Doppler examination (P = 0.008), needle type (P = 0.043), and lesion location (P < 0.001) were significantly different between the groups. Patients with hemorrhagic complications reported more frequent nausea or vomiting than those without hemorrhagic complications (29% [2/7] vs. 2% [2/87], respectively; P = 0.027). The overall satisfaction scores did not differ between the two groups (P = 0.396). After 2 weeks, all symptoms subsided, except bruising (50% 2/4 in the complication group and 25% [16/65] in the no-complication group). CONCLUSION: US-guided breast biopsy is a safe procedure with a low complication rate. Radiologists should be aware of hemorrhagic complications, patient discomfort, and overall satisfaction related to this procedure.

Overcoming the limitations of screening mammography in Japan and Korea: a paradigm shift to personalized breast cancer screening based on ultrasonography.

Screening mammography programs have been implemented in numerous Western countries with the aim of reducing breast cancer mortality. However, despite over 20 years of population-based screening mammography, the mortality rates in Japan and Korea continue to rise. This may be due to the fact that screening mammography is not as effective for Japanese and Korean women, who often have dense breasts. This density decreases the sensitivity of mammography due to a masking effect. Therefore, the early detection of small invasive cancers requires more than just mammography, particularly for women in their 40s. This review discusses the limitations and challenges of screening mammography, as well as the keys to successful population-based breast cancer screening in Japan and Korea. This includes a focus on breast ultrasonography techniques, which are based on histopathologic anatomical knowledge, and personalized screening strategies that are based on risk assessments measured by glandular tissue components.

Corrigendum: Diffusion-Weighted Magnetic Resonance Imaging for Preoperative Evaluation of Patients With Breast Cancer: Protocol of a Prospective, Multicenter, Observational Cohort Study.

This corrects the article on p. 292 in vol. 26, PMID: 37272245.

Diffusion-Weighted Magnetic Resonance Imaging for Preoperative Evaluation of Patients With Breast Cancer: Protocol of a Prospective, Multicenter, Observational Cohort Study.

PURPOSE: Detection of multifocal, multicentric, and contralateral breast cancers in patients affects surgical management. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can identify additional foci that were initially undetected by conventional imaging. However, its use is limited owing to low specificity and high false-positive rate. Multiparametric MRI (DCE-MRI + diffusion-weighted [DW] MRI) can increase the specificity. We aimed to describe the protocols of our prospective, multicenter, observational cohort studies designed to compare the diagnostic performance of DCE-MRI and multiparametric MRI for the diagnosis of multifocal, multicentric cancer and contralateral breast cancer in patients with newly diagnosed breast cancer. METHODS: Two studies comparing the performance of DCE-MRI and multiparametric MRI for the diagnosis of multifocal, multicentric cancer (NCT04656639) and contralateral breast cancer (NCT05307757) will be conducted. For trial NCT04656639, 580 females with invasive breast cancer candidates for breast conservation surgery whose DCE-MRI showed additional suspicious lesions (breast imaging reporting and data system [BI-RADS] category ≥ 4) on DCE-MRI in the ipsilateral breast will be enrolled. For trial NCT05307757, 1098 females with invasive breast cancer whose DCE-MRI showed contralateral lesions (BI-RADS category ≥ 3 or higher on DCE-MRI) will be enrolled. Participants will undergo 3.0-T DCE-MRI and DW-MRI. The diagnostic performance of DCE-MRI and multiparametric MRI will be compared. The receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and characteristics of the detected cancers will be analyzed. The primary outcome is the difference in the receiver operating characteristic curve between DCE-MRI and multiparametric MRI interpretation. Enrollment completion is expected in 2024, and study results are expected to be presented in 2026. DISCUSSION: This prospective, multicenter study will compare the performance of DCE-MRI versus multiparametric MRI for the preoperative evaluation of multifocal, multicentric, and contralateral breast cancer and is currently in the patient enrollment phase. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04656639, NCT05307757. Registered on April 1 2022.

Semiannual Breast US or MRI Screening in Patients with a Personal History of Breast Cancer.

Background The wide variability of screening imaging use in patients with a personal history of breast cancer (PHBC) warrants investigation of its comparative clinical effectiveness. While more intensive screening with US or MRI at an interval of less than 1 year could increase early-stage breast cancer detection, its benefit has not been established. Purpose To investigate the outcomes of semiannual multimodality screening in patients with PHBC. Materials and Methods An academic medical center database was retrospectively searched for patients diagnosed with breast cancer between January 2015 and June 2018 who had undergone annual mammography with either semiannual incidence US or MRI screening from July 2019 to December 2019 and three subsequent semiannual screenings over a 2-year period. The primary outcome was second breast cancers diagnosed during follow-up. Examination-level cancer detection and interval cancer rates were calculated. Screening performances were compared with χ2 or Fisher exact tests or a logistic model with generalized estimating equations. Results Our final cohort included 2758 asymptomatic women (median age, 53 years; range, 20-84 years). Among 5615 US and 1807 MRI examinations, 18 breast cancers were detected after negative findings on a prior semiannual incidence US screening examination; 44% (eight of 18) were stage 0 (three detected with MRI; five, with US), and 39% (seven of 18) were stage I (three detected with MRI; four, with US). MRI had a cancer detection rate up to 17.1 per 1000 examinations (eight of 467; 95% CI: 8.7, 33.4), and the overall cancer detection rates of US and MRI were 1.8 (10 of 5615; 95% CI: 1.0, 3.3) and 4.4 (eight of 1807; 95% CI: 2.2, 8.8) per 1000 examinations, respectively (P = .11). Conclusion Supplemental semiannual US or MRI screening depicted second breast cancers after negative findings at prior semiannual incidence US examination in patients with PHBC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Berg in this issue.

Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients.

PURPOSE: To develop a prediction model incorporating clinicopathological information, US, and MRI to diagnose axillary lymph node (LN) metastasis with acceptable false negative rate (FNR) in patients with early stage, clinically node-negative breast cancers. METHODS: In this single center retrospective study, the inclusion criteria comprised women with clinical T1 or T2 and N0 breast cancers who underwent preoperative US and MRI between January 2017 and July 2018. Patients were temporally divided into the development and validation cohorts. Clinicopathological information, US, and MRI findings were collected. Two prediction models (US model and combined US and MRI model) were created using logistic regression analysis from the development cohort. FNRs of the two models were compared using the McNemar test. RESULTS: A total of 964 women comprised the development (603 women, 54 ± 11 years) and validation (361 women, 53 ± 10 years) cohorts with 107 (18%) and 77 (21%) axillary LN metastases in each cohort, respectively. The US model consisted of tumor size and morphology of LN on US. The combined US and MRI model consisted of asymmetry of LN number, long diameter of LN, tumor type, and multiplicity of breast cancers on MRI, in addition to tumor size and morphology of LN on US. The combined model showed significantly lower FNR than the US model in both development (5% vs. 32%, P < .001) and validation (9% vs. 35%, P < .001) cohorts. CONCLUSION: Our prediction model combining US and MRI characteristics of index cancer and LN lowered FNR compared to using US alone, and could potentially lead to avoid unnecessary SLNB in early stage, clinically node-negative breast cancers.

Digital Breast Tomosynthesis Plus Ultrasound Versus Digital Mammography Plus Ultrasound for Screening Breast Cancer in Women With Dense Breasts.

OBJECTIVE: To compare the outcomes of digital breast tomosynthesis (DBT) screening combined with ultrasound (US) with those of digital mammography (DM) combined with US in women with dense breasts. MATERIALS AND METHODS: A retrospective database search identified consecutive asymptomatic women with dense breasts who underwent breast cancer screening with DBT or DM and whole-breast US simultaneously between June 2016 and July 2019. Women who underwent DBT + US (DBT cohort) and DM + US (DM cohort) were matched using 1:2 ratio according to mammographic density, age, menopausal status, hormone replacement therapy, and a family history of breast cancer. The cancer detection rate (CDR) per 1000 screening examinations, abnormal interpretation rate (AIR), sensitivity, and specificity were compared. RESULTS: A total of 863 women in the DBT cohort were matched with 1726 women in the DM cohort (median age, 53 years; interquartile range, 40-78 years) and 26 breast cancers (9 in the DBT cohort and 17 in the DM cohort) were identified. The DBT and DM cohorts showed comparable CDR (10.4 [9 of 863; 95% confidence interval {CI}: 4.8-19.7] vs. 9.8 [17 of 1726; 95% CI: 5.7-15.7] per 1000 examinations, respectively; P = 0.889). DBT cohort showed a higher AIR than the DM cohort (31.6% [273 of 863; 95% CI: 28.5%-34.9%] vs. 22.4% [387 of 1726; 95% CI: 20.5%-24.5%]; P < 0.001). The sensitivity for both cohorts was 100%. In women with negative findings on DBT or DM, supplemental US yielded similar CDRs in both DBT and DM cohorts (4.0 vs. 3.3 per 1000 examinations, respectively; P = 0.803) and higher AIR in the DBT cohort (24.8% [188 of 758; 95% CI: 21.8%-28.0%] vs. 16.9% [257 of 1516; 95% CI: 15.1%-18.9%; P < 0.001). CONCLUSION: DBT screening combined with US showed comparable CDR but lower specificity than DM screening combined with US in women with dense breasts.

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities.

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

Background Parenchymal Enhancement at Postoperative Surveillance Breast MRI: Association with Future Second Breast Cancer Risk.

Background Background parenchymal enhancement (BPE) is a known risk factor for breast cancer. However, studies on the association between BPE and second breast cancer risk are still lacking. Purpose To investigate whether BPE at surveillance breast MRI is associated with subsequent second breast cancer risk in women with a personal history of breast cancer. Materials and Methods A retrospective search of the imaging database of an academic medical center identified consecutive surveillance breast MRI examinations performed between January 2008 and December 2017 in women who underwent surgery for primary breast cancer and had no prior diagnosis of second breast cancer. BPE at surveillance breast MRI was qualitatively assessed using a four-category classification of minimal, mild, moderate, or marked. Future second breast cancer was defined as ipsilateral breast tumor recurrence or contralateral breast cancer diagnosed at least 1 year after each surveillance breast MRI examination. Factors associated with future second breast cancer risk were evaluated using the multivariable Fine-Gray subdistribution hazard model. Results Among the 2668 women (mean age at baseline surveillance breast MRI, 49 years ± 8 [SD]), 109 developed a second breast cancer (49 ipsilateral, 58 contralateral, and two ipsilateral and contralateral) at a median follow-up of 5.8 years. Mild, moderate, or marked BPE at surveillance breast MRI (hazard ratio [HR], 2.1 [95% CI: 1.4, 3.1]; P < .001), young age (<45 years) at initial breast cancer diagnosis (HR, 3.4 [95% CI: 1.7, 6.4]; P < .001), positive results from a BRCA1/2 genetic test (HR, 6.5 [95% CI: 3.5, 12.0]; P < .001), and negative hormone receptor expression in the initial breast cancer (HR, 1.6 [95% CI: 1.1, 2.6]; P = .02) were independently associated with an increased risk of future second breast cancer. Conclusion Background parenchymal enhancement at surveillance breast MRI was associated with future second breast cancer risk in women with a personal history of breast cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Niell in this issue.

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.

OBJECTIVE: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. MATERIALS AND METHODS: A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. RESULTS: Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001). CONCLUSION: AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.

The Value of Adding Ductography to Ultrasonography for the Evaluation of Pathologic Nipple Discharge in Women with Negative Mammography.

OBJECTIVE: The optimal imaging approach for evaluating pathological nipple discharge remains unclear. We investigated the value of adding ductography to ultrasound (US) for evaluating pathologic nipple discharge in patients with negative mammography findings. MATERIALS AND METHODS: From July 2003 to December 2018, 101 women (mean age, 46.3 ± 12.2 years; range, 23-75 years) with pathologic nipple discharge were evaluated using pre-ductography (initial) US, ductography, and post-ductography US. The imaging findings were reviewed retrospectively. The standard reference was surgery (70 patients) or > 2 years of follow-up with US (31 patients). The diagnostic performances of initial US, ductography, and post-ductography US for detecting malignancy were compared using the McNemar's test or a generalized estimating equation. RESULTS: In total, 47 papillomas, 30 other benign lesions, seven high-risk lesions, and 17 malignant lesions were identified as underlying causes of pathologic nipple discharge. Only eight of the 17 malignancies were detected on the initial US, while the remaining nine malignancies were detected by ductography. Among the nine malignancies detected by ductography, eight were detected on post-ductography US and could be localized for US-guided intervention. The sensitivities of ductography (94.1% [16/17]) and post-ductography US (94.1% [16/17]) were significantly higher than those of initial US (47.1% [8/17]; p = 0.027 and 0.013, respectively). The negative predictive value of post-ductography US (96.9% [31/32]) was significantly higher than that of the initial US (83.3% [45/54]; p = 0.006). Specificity was significantly higher for initial US than for ductography and post-ductography US (p = 0.001 for all). CONCLUSION: The combined use of ductography and US has a high sensitivity for detecting malignancy in patients with pathologic nipple discharge and negative mammography. Ductography findings enable lesion localization on second-look post-ductography US, thus facilitating the selection of optimal treatment plans.

Diffusion-weighted Breast MRI in Prediction of Upstaging in Women with Biopsy-proven Ductal Carcinoma in Situ.

Background Accurate preoperative prediction of upstaging in women with biopsy-proven ductal carcinoma in situ (DCIS) is important for surgical planning, but published models using predictive MRI features remain lacking. Purpose To develop and validate a predictive model based on preoperative breast MRI to predict upstaging in women with biopsy-proven DCIS and to select high-risk women who may benefit from sentinel lymph node biopsy at initial surgery. Materials and methods Consecutive women with biopsy-proven DCIS who underwent preoperative 3.0-T breast MRI including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) and who underwent surgery between June 2019 and March 2020 were retrospectively identified (development set) from an academic medical center. The apparent diffusion coefficients of lesions from DWI, lesion size and morphologic features on DCE MRI scans, mammographic findings, age, symptoms, biopsy method, and DCIS grade at biopsy were collected. The presence of invasive cancer and axillary metastases was determined with surgical pathology. A predictive model for upstaging was developed by using multivariable logistic regression and validated in a subsequent prospective internal validation set recruited between July 2020 and April 2021. Results Fifty-seven (41%) of 140 women (mean age, 53 years ± 11 [SD]) in the development set and 43 (41%) of 105 women (mean age, 53 years ± 10) in the validation set were upstaged after surgery. The predictive model combining DWI and clinical-pathologic factors showed the areas under the receiver operating characteristic curve at 0.87 (95% CI: 0.80, 0.92) in the development set and 0.76 (95% CI: 0.67, 0.84) in the validation set. The predicted probability of invasive cancer showed good interobserver agreement (intraclass correlation coefficient, 0.79); the positive predictive value was 85% (28 of 33), and the negative predictive value was 92% (22 of 24). Conclusion A predictive model based on diffusion-weighted breast MRI identified women at high risk of upstaging. © RSNA, 2022 Online supplemental material is available for this article See also the editorial by Baltzer in this issue. An earlier incorrect version appeared online. This article was corrected on July 7, 2022.

Transcriptome analysis of SerpinB2-deficient breast tumors provides insight into deciphering SerpinB2-mediated roles in breast cancer progression.

BACKGROUND: SerpinB2 is highly expressed in immune and tumor cells and is involved in multiple biological functions, including cell survival and remodeling for disease progression. This study prepared SerpinB2-deficient mice and analyzed the differentially expressed genes (DEGs) to determine if loss of this protein delays mammary tumor progression. RESULTS: A total of 305 DEGs (75 upregulated and 230 downregulated; > 1.5-fold difference, P < 0.05) were identified in SB2-/-;PyMT tumors compared with PyMT tumors. The DEGs were mainly involved in immune and inflammatory responses related to T cell differentiation, IFN-γ production, and lymphocyte chemotaxis based on 61 enriched GO terms, hierarchical clustering, KEGG pathways, and a functionally grouped annotation network. The significantly changed DEGs (Anxa3, Ccl17, Cxcl13, Cxcr3, IFN-γ, Nr4a1, and Sema3a) annotated with at least two GO categories in SB2-/-;PyMT tumors was validated by qRT-PCR. CONCLUSIONS: SerpinB2 deficiency alters the expression of multiple genes in mammary tumors, which might cause a delay in PyMT-induced mammary tumor progression.