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 β 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 (β = 77.0 for mass, β = 73.1 for calcification only, β = 49.4 for architectural distortion, and β = 47.6 for asymmetry compared to negative; all Ps < 0.001), invasive tumor size (β = 4.3 per 1 cm, P < 0.001), and human epidermal growth receptor type 2 (HER2) positivity (β = 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.

Artificial Intelligence Improves Detection of Supplemental Screening Ultrasound-detected Breast Cancers in Mammography

Despite recent advances in artificial intelligence (AI) software with improved performance in mammography screening for breast cancer, insufficient data are available on its performance in detecting cancers that were initially missed on mammography. In this study, we aimed to determine whether AI software-aided mammography could provide additional value in identifying cancers detected through supplemental screening ultrasound. We searched our database from 2017 to 2018 and included 238 asymptomatic patients (median age, 50 years; interquartile range, 45-57 years) diagnosed with breast cancer using supplemental ultrasound. Two unblinded radiologists retrospectively reviewed the mammograms using commercially available AI software and identified the reasons for missed detection. Clinicopathological characteristics of AI-detected and AI-undetected cancers were compared using univariate and multivariate logistic regression analyses. A total of 253 cancers were detected in 238 patients using ultrasound. In an unblinded review, the AI software failed to detect 187 of the 253 (73.9%) mammography cases with negative findings in retrospective observations. The AI software detected 66 cancers (26.1%), of which 42 (63.6%) exhibited indiscernible findings obscured by overlapping dense breast tissues, even with the knowledge of magnetic resonance imaging and post-wire localization mammography. The remaining 24 cases (36.4%) were considered interpretive errors by the radiologists. Invasive tumor size was associated with AI detection after multivariable analysis (odds ratio, 2.2; 95% confidence intervals, 1.5-3.3; p < 0.001). In the control group of 160 women without cancer, the AI software identified 19 false positives (11.9%, 19/160). Although most ultrasound-detected cancers were not detected on mammography with the use of AI, the software proved valuable in identifying breast cancers with indiscernible abnormalities or those that clinicians may have overlooked.

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.

[Interpretation of Image-Guided Biopsy Results and Assessment]

The success of image-guided breast biopsy depends on the biopsy method, needle selection, and appropriate technique based on the accurate judgment by the radiologist at biopsy. However, insufficient or inappropriate sampling of specimens may result in false-negative results or pathologic underestimation. Therefore, image-pathology concordance assessments after biopsy are essential for appropriate patient management. Particularly, the assessment of image-pathology concordance can avoid false-negative reports of breast cancer as a benign pathology. Therefore, this study aimed to discuss factors that impact the accurate interpretation of image-guided breast biopsy along with the appropriate assessments.

Role of Doppler US and elastography prior to biopsy to identify candidates for avoidance of surgery following neoadjuvant chemotherapy for breast cancer

PURPOSE

This study aimed to evaluate the role of Doppler ultrasound (US) and elastography to identify residual breast cancer for patients showing near complete response following chemotherapy on magnetic resonance imaging (MRI).

METHODS

Between September 2016 and January 2018, 40 breast cancer patients who showed near complete response (either tumor size ≤0.5 cm or lesion-to-background parenchymal signal enhancement ratio ≤1.6) on MRI following neoadjuvant chemotherapy were prospectively enrolled. After excluding seven women who did not undergo Doppler US and elastography, 33 women (median age, 49 years; ranges, 32 to 67 years) were analyzed. On the day of surgery, women underwent Doppler US and elastography for tumor bed prior to US-guided core needle biopsy. Histopathologic results of biopsy and surgery were evaluated. Negative predictive value (NPV) and false negative rate (FNR) of biopsy and the combined Doppler US and elastography were analyzed, respectively.

RESULTS

After surgery, nine women had residual cancers and 24 women had pathologic complete response. The NPV and FNR of biopsy were 92% (24 of 26) and 22% (2 of 9), respectively. The NPV and FNR of combined Doppler US and elastography were 100% (14 of 14) and 0% (0 of 9), respectively. All of nine women with residual cancers had positive vascularity or elasticity. Two women with false-negative biopsy results, having 0.3 cm or 2.5 cm ductal carcinoma in situ at surgery, showed positive vascularity or elasticity.

CONCLUSION

Tumor bed showing positive vascularity or elasticity indicates residual breast cancer for patients showing near complete response on MRI following chemotherapy.

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.

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.

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.

US Evaluation of Axillary Lymphadenopathy Following COVID-19 Vaccination: A Prospective Longitudinal Study

Background Both temporal changes in imaging characteristics of lymphadenopathy on US scans after COVID-19 vaccination and expected duration of radiologically evident lymphadenopathy remain uncertain. Purpose To longitudinally evaluate COVID-19 vaccine-associated lymphadenopathy on axillary US scans at various time intervals in both messenger (mRNA) and vector vaccine recipients. Materials and Methods This prospective cohort study was conducted between March 2021 and January 2022. The participants were asymptomatic women without breast cancer who had received COVID-19 vaccination. Serial follow-up US was performed in women with lymphadenopathy. The following variables were assessed: cortical thickness, number of lymph nodes, morphologic characteristics, and Doppler signal. Temporal changes in cortical thickness and number of lymph nodes during follow-up were assessed using a linear mixed model. Results Ninety-one women with lymphadenopathy in the vaccinated arm had undergone a total of 215 serial US examinations (mean age, 44 years ± 13 [SD]). Fifty-one participants had received a vector vaccine (ChAdOx1 nCoV-19 vaccine) and 40 had received an mRNA vaccine (BNT162b2 vaccine [n = 37] and mRNA-1273 vaccine [n = 3]). Three of the 91 women were lost to follow-up; thus, 88 women underwent serial US. Complete resolution of axillary lymphadenopathy was observed at a median of 6 weeks after vaccination (range, 4-7 weeks) in 26% of women (23 of 88). Among 49 women with follow-up US at a median of 12 weeks after vaccination (range, 8-14 weeks), persistent lymphadenopathy was observed in 25 (51%). During the follow-up period, the cortical thickness gradually decreased (P < .001) over time regardless of vaccine type; however, values were higher in recipients of the mRNA vaccine than in recipients of the vector vaccine (P = .02). Conclusion COVID-19 vaccine-associated axillary lymphadenopathy frequently persisted for more than 6 weeks on US scans. Lymphadenopathy should be interpreted considering vaccine type and time elapsed since vaccination. Follow-up US examination at least 12 weeks after vaccination may be reasonable, particularly for recipients of the messenger RNA vaccine. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Moy and Kim in this issue.

Abbreviated Screening MRI for Women with a History of Breast Cancer: Comparison with Full-Protocol Breast MRI

Background Few studies have compared abbreviated breast MRI with full-protocol MRI in women with a personal history of breast cancer (PHBC), and they have not adjusted for confounding variables. Purpose To compare abbreviated breast MRI with full-protocol MRI in women with PHBC by using propensity score matching to adjust for confounding variables. Materials and Methods In this single-center retrospective study, women with PHBC who underwent full-protocol MRI (January 2008-August 2017) or abbreviated MRI (September 2017-April 2019) were identified. With use of a propensity score-matched cohort, screening performances were compared between the two MRI groups with the McNemar test or a propensity score-adjusted generalized estimating equation. The coprimary analyses were sensitivity and specificity. The secondary analyses were the cancer detection rate, interval cancer rate, positive predictive value for biopsies performed (PPV3), and Breast Imaging Reporting and Data System (BI-RADS) category 3 short-term follow-up rate. Results There were 726 women allocated to each MRI group (mean age ± SD, 50 years ± 8 for both groups). Abbreviated MRI and full-protocol MRI showed comparable sensitivity (15 of 15 cancers [100%; 95% CI: 78, 100] vs nine of 13 cancers [69%; 95% CI: 39, 91], respectively; P = .17). Abbreviated MRI showed higher specificity than full-protocol MRI (660 of 711 examinations [93%; 95% CI: 91, 95] vs 612 of 713 examinations [86%; 95% CI: 83, 88], respectively; P < .001). The cancer detection rate (21 vs 12 per 1000 examinations), interval cancer rate (0 vs five per 1000 examinations), and PPV3 (61% [14 of 23 examinations] vs 41% [nine of 22 examinations]) were comparable (all P < .05). The BI-RADS category 3 short-term follow-up rate of abbreviated MRI was less than half that of full-protocol MRI (5% [36 of 726 examinations] vs 12% [84 of 726 examinations], respectively; P < .001). Ninety-three percent (14 of 15) of cancers detected at abbreviated MRI were node-negative T1-invasive cancers (n = 6) or ductal carcinoma in situ (n = 8). Conclusion Abbreviated breast MRI showed comparable sensitivity and superior specificity to full-protocol MRI in breast cancer detection in women with a personal history of breast cancer. © RSNA, 2022 Online supplemental material is available for this article.

A randomized, prospective, multicenter trial of 3D printing, a patient-specific surgical guide for breast-conserving surgery after neoadjuvant chemotherapy: Comparative evaluation according to the presence or absence of surgical guide

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Background: To obtain a clear resection margin during breast-conserving surgery (BCS) after neoadjuvant chemotherapy (NCT), accurate targeting of the location and assessment extent of the tumor are essential. However, conventional targeting methods such as USG/MMG guided hook wire insertion have disadvantages in that they could only localize the tumor, not reflecting its extent. To assess this problem, we developed an MRI-based 3D printed breast surgical guide (3DP-BSG) and conducted a multicenter randomized clinical study to prove its clinical effectiveness. Methods: In this multicenter, randomized (1:1), controlled trial (KCT0004469), we assigned 566 patients who underwent NCT and planned to undergo BCS, to use customized 3DP-BSG for lumpectomy, or not to use 3DP-BSG for lumpectomy. We assumed that 3DP-BSG group achieve non-inferior outcomes compared with control group with conventional targeting method. The primary endpoint was the margin positivity of the first resected margin. Under the assumption that the proportion of margin positivity would be 23% in both groups, a total of 438 patients were calculated to have 80% power to establish non-inferiority with a margin of 10% at a one-sided significance level of 0.05. Efficacy was assessed in the intention-to-treat (ITT) population. Results: Between 2019 and 2021, 282 (49.8%) patients were assigned to 3DP-BSG group, and 284 (50.2%) patients were assigned to control group with conventional targeting method. The median age was 51 years. In the ITT analysis, first resected margin was positivity in 5 (2.1%) of 235 patients in 3DP-BSG group and 7 (2.9%) of 244 patients in control group (difference -1.15 [95% CI -3.58 to -1.28], pnon-inferiority <.0001). In the analysis of population excluding patients assessed as complete response (CR), first resected margin was positivity in 5 (3.40%) of 147 patients in 3DP-BSG group and 7 (4.8%) of 145 patients in control group (difference -1.43 [95% CI -5.25 to -2.40], pnon-inferiority <.0001). Most of the cases, marked areas with 3DP-BSG were partially different from those with conventional targeting method, and there were some cases where completely different areas were marked. In most of these cases, pathological CR made it difficult to compare the accuracy of the targeting methods, but in one case, a malignant tumor was diagnosed only in the 3DP-BSG targeting region. Even lesions that have been incorrectly targeted by conventional methods can be properly removed using 3DP-BSG, which is believed to be helpful in surgery. Conclusions: Lumpectomy using 3DP-BSG showed non-inferiority compared to the conventional targeting group, and the surgeon's satisfaction was higher by quantitatively presenting the extent of initial tumor. Clinical trial information: KCT0004469.

Microcalcifications and Peritumoral Edema Predict Survival Outcome in Luminal Breast Cancer Treated with Neoadjuvant Chemotherapy

Background Little is known regarding findings at imaging associated with survival in patients with luminal breast cancer treated with neoadjuvant chemotherapy (NAC). Purpose To determine the relationship between imaging (MRI, US, and mammography) and clinical-pathologic variables in predicting distant metastasis-free survival (DMFS) and overall survival (OS) in patients with luminal breast cancer treated with NAC. Materials and Methods In this retrospective study, consecutive women with luminal breast cancer who underwent NAC followed by surgery were identified from the breast cancer registries of two hospitals. Women from one hospital between January 2003 and July 2015 were classified into the development cohort, and women from the other hospital between January 2007 and July 2015 were classified into the validation cohort. MRI scans, US scans, and mammograms before and after NAC (hereafter, referred to as pre- and post-NAC, respectively) and clinical-pathologic data were reviewed. Peritumoral edema was defined as the water-like high signal intensity surrounding the tumor on T2-weighted MRI scans. The prediction model was developed in the development cohort by using Cox regression and then tested in the validation cohort. Results The development cohort consisted of 318 women (68 distant metastases, 54 deaths) and the validation cohort consisted of 165 women (37 distant metastases, 14 deaths) (median age, 46 years in both cohorts). Post-NAC MRI peritumoral edema, age younger than 40 years, clinical N2 or N3, and lymphovascular invasion were associated with worse DMFS (all, P < .05). Pre-NAC mammographic microcalcifications, post-NAC MRI peritumoral edema, age older than 60 years, and clinical T3 or T4 were associated with worse OS (all, P < .05). The prediction model showed good discrimination ability (C index, 0.67-0.75 for DMFS and 0.70-0.77 for OS) and stratified prognosis into low-risk and high-risk groups (10-year DMFS rates, 79% vs 21%, respectively; and 10-year OS rates, 95%-96% vs 63%-67%, respectively) in the validation cohort. Conclusion MRI features and clinical-pathologic variables were identified that were associated with prolonged survival of patients with luminal breast cancer treated with neoadjuvant chemotherapy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kataoka in this issue.

No axillary surgical treatment for lymph node-negative patients after ultra-sonography [NAUTILUS]: protocol of a prospective randomized clinical trial

Background

Following sentinel lymph node biopsy (SLNB), the axillary recurrence rate is very low although SLNB has a false-negative rate of 5–10%. In the ACOSOG Z0011 trial, non-sentinel positive-lymph nodes were found in more than 20% of the axillary dissection group; the SLNB only group did not have a higher axillary recurrence rate. These findings raised questions about the direct therapeutic effect of the SLNB. SLNB has post-surgical complications including lymphedema. Considering advances in imaging modalities and adjuvant therapies, the role of SLNB in early breast cancer needs to be re-evaluated.

Methods

The NAUTILUS trial is a prospective multicenter randomized controlled trial involving clinical stage T1–2 and N0 breast cancer patients receiving breast-conserving surgery. Axillary ultrasound is mandatory before surgery with predefined imaging criteria for inclusion. Ultrasound-guided core needle biopsy or needle aspiration of a suspicious node is allowed. Patients will be randomized (1:1) into the no-SLNB (test) and SLNB (control) groups. A total of 1734 patients are needed, considering a 5% non-inferiority margin, 5% significance level, 80% statistical power, and 10% dropout rate. All patients in the two groups will receive ipsilateral whole-breast radiation according to a predefined protocol. The primary endpoint of this trial is the 5-year invasive disease-free survival. The secondary endpoints are overall survival, distant metastasis-free survival, axillary recurrence rate, and quality of life of the patients.

Discussion

This trial will provide important evidence on the oncological safety of the omission of SLNB for early breast cancer patients undergoing breast-conserving surgery and receiving whole-breast radiation, especially when the axillary lymph node is not suspicious during preoperative axillary ultrasound.

Trial registration

ClinicalTrials.gov, NCT04303715. Registered on March 11, 2020.

Abstract OT1-04-02: The NAUTILUS trial (No Axillary sUrgical Treatment In clinically Lymph node negative patients after UltraSonography): A prospective multicenter randomized phase III trial (NCT04303715)

Background. Sentinel lymph node biopsy (SLNB) is currently the standard axillary staging procedure for clinically node-negative breast cancer. According to the ACOSOG Z0011 trial, axillary lymph node dissection (ALND) is now often omitted for clinically node-negative cT1-2 breast cancer patients undergoing breast-conserving surgery even if 1 or 2 metastatic lymph nodes (LN) are identified. It was shown that radiotherapy contributes to regional control of the axilla non-inferior to ALND even in the presence of metastatic LNs. Furthermore, LN metastasis has a limited role in planning adjuvant systemic therapy for hormone receptor-negative breast cancers, and also for hormone receptor-positive, HER2-negative breast cancer with increased use of genomic assays. SLNB is not without complications and omitting axillary staging operation could improve quality of life. Trial Design. In this multicenter, phase III randomized controlled trial including 9 study sites in South Korea, we plan to 1:1 randomize 1734 patients to either omit SLNB (Arm 1) or receive SLNB (Arm 2). Additional ALND can be performed according to the discretion of the surgeon according to the SLNB results. All patients must receive whole-breast irradiation ± tumor bed boost. The recommended upper margin of the radiation field is within 2cm of the humeral head. ClinicalTrials.gov identifier: NCT04303715. Eligibility. Inclusion criteria: women ≥19 years; cT1-2N0M0 unilateral invasive breast carcinoma; all molecular subtypes; clinically and radiologically tumor size ≤ 5cm; clinically- and ultrasonogram-negative axillary lymph nodes, or no tumor on core needle biopsy or fine needle aspiration cytology in case of suspicious lymph nodes; candidate for breast-conserving surgery with no restriction to radiotherapy and adequate systemic therapy. Exclusion criteria: history of any malignancy within 5 years (exception: thyroid cancer and well-treated skin cancer except melanoma); bilateral breast cancer; neoadjuvant chemotherapy; candidate for total mastectomy; male breast cancer. Specific Aims. Primary objective is to test the hypothesis that omitting SLNB for breast cancer is non-inferior to axillary staging operation in terms of 5-year disease-free survival. Secondary objectives are to compare overall survival, distant metastasis-free survival, locoregional recurrence, quality of life assessment, and DFS and axillary recurrence according to molecular subtypes. Statistical Methods. With an expected 5-year DFS of 86% for Arm 2, Arm 1(SLNB omission) will be assessed with a non-inferiority limit of 5% and hazard ratio of 1.4, power 80%, and significance level of 5%, where 224 events are required. The calculated sample size is 780 per study arm, resulting in a total of 1,734 patients assuming a 10% drop-out rate. Present Accrual and Target Accrual. The first patient was randomized on September 15, 2020. As of July 9, 2021, 480 patients have been randomized. Target accrual of 1734 patients is expected to be complete by April 2023, with the primary endpoint analysis expected in 2028.

Citation Format: Han-Byoel Lee, Ji Gwang Jung, Jung Min Chang, Ji Hyun Chang, Woo Kyung Moon, Kyung Hwan Shin, Il Yong Chung, Seok Jin Nam, Eun-Kyu Kim, Seeyoun Lee, Seho Park, Woo Sung Lim, Yongsik Jung, Wonshik Han. The NAUTILUS trial (No Axillary sUrgical Treatment In clinically Lymph node negative patients after UltraSonography): A prospective multicenter randomized phase III trial (NCT04303715) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT1-04-02.

Diagnostic performance improvement with combined use of proteomics biomarker assay and breast ultrasound

PURPOSE

To investigate the combined use of blood-based 3-protein signature and breast ultrasound (US) for validating US-detected lesions.

METHODS

From July 2011 to April 2020, women who underwent whole-breast US within at least 6 months from sampling period were retrospectively included. Blood-based 3-protein signature (Mastocheck®) value and US findings were evaluated. Following outcome measures were compared between US alone and the combination of Mastocheck® value with US: sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the receiver operating characteristic curve (AUC), and biopsy rate.

RESULTS

Among the 237 women included, 59 (24.9%) were healthy individuals and 178 (75.1%) cancer patients. Mean size of cancers was 1.2 ± 0.8 cm. Median value of Mastocheck® was significantly different between nonmalignant (- 0.24, interquartile range [IQR] - 0.48, - 0.03) and malignant lesions (0.55, IQR - 0.03, 1.42) (P < .001). Utilizing Mastocheck® value with US increased the AUC from 0.67 (95% confidence interval [CI] 0.61, 0.73) to 0.81 (95% CI 0.75, 0.88; P < .001), and specificity from 35.6 (95% CI 23.4, 47.8) to 64.4% (95% CI 52.2, 76.6; P < .001) without loss in sensitivity. PPV was increased from 82.2 (95% CI 77.1, 87.3) to 89.3% (95% CI 85.0, 93.6; P < .001), and biopsy rate was significantly decreased from 79.3 (188/237) to 72.1% (171/237) (P < .001). Consistent improvements in specificity, PPV, and AUC were observed in asymptomatic women, in women with dense breast, and in those with normal/benign mammographic findings.

CONCLUSION

Mastocheck® is an effective tool that can be used with US to improve diagnostic specificity and reduce false-positive findings and unnecessary biopsies.

Ultrasonographic morphological characteristics determined using a deep learning-based computer-aided diagnostic system of breast cancer

To investigate the correlations between ultrasonographic morphological characteristics quantitatively assessed using a deep learning-based computer-aided diagnostic system (DL-CAD) and histopathologic features of breast cancer.This retrospective study included 282 women with invasive breast cancer (<5 cm; mean age, 54.4 [range, 29-85] years) who underwent surgery between February 2016 and April 2017. The morphological characteristics of breast cancer on B-mode ultrasonography were analyzed using DL-CAD, and quantitative scores (0-1) were obtained. Associations between quantitative scores and tumor histologic type, grade, size, subtype, and lymph node status were compared.Two-hundred and thirty-six (83.7%) tumors were invasive ductal carcinoma, 18 (6.4%) invasive lobular carcinoma, and 28 (9.9%) micropapillary, apocrine, and mucinous. The mean size was 1.8 ± 1.0 (standard deviation) cm, and 108 (38.3%) cases were node positive. Irregular shape score was associated with tumor size (P < .001), lymph nodes status (P = .001), and estrogen receptor status (P = .016). Not-circumscribed margin (P < .001) and hypoechogenicity (P = .003) scores correlated with tumor size, and non-parallel orientation score correlated with histologic grade (P = .024). Luminal A tumors exhibited more irregular features (P = .048) with no parallel orientation (P = .002), whereas triple-negative breast cancer showed a rounder/more oval and parallel orientation.Quantitative morphological characteristics of breast cancers determined using DL-CAD correlated with histopathologic features and could provide useful information about breast cancer phenotypes.

Imaging Protocol and Criteria for Evaluation of Axillary Lymph Nodes in the NAUTILUS Trial

Axillary ultrasonography (US) is the most commonly used imaging modality for nodal evaluation in patients with breast cancer. No Axillary Surgical Treatment in Clinically Lymph Node-Negative Patients after Ultrasonography (NAUTILUS) is a prospective, multicenter, randomized controlled trial investigating whether sentinel lymph node biopsy (SLNB) can be safely omitted in patients with clinically and sonographically node-negative T1–2 breast cancer treated with breast-conserving therapy. In this trial, a standardized imaging protocol and criteria were established for the evaluation of axillary lymph nodes. Women lacking palpable lymph nodes underwent axillary US to dismiss suspicious nodal involvement. Patients with a round hypoechoic node with effaced hilum or indistinct margins were excluded. Patients with T1 tumors and a single node with a cortical thickness ≥ 3 mm underwent US-guided biopsy. Finally, patients with negative axillary US findings were included. The NAUTILUS axillary US nodal assessment criteria facilitate the proper selection of candidates who can omit SLNB.

Precautions for breast ultrasound examination following COVID-19 vaccination

Background: Coronavirus disease 2019 (COVID-19) vaccine-induced lymphadenopathy is a critical side effect that should be a concern to clinicians, patients, radiologists, and oncologists. Vaccine-induced lymphadenopathy causes a diagnostic dilemma, especially for breast radiologists who examine both axillary regions during breast ultrasound examinations. Appropriate imaging guidelines are needed to manage vaccine-induced lymphadenopathy for patients undergoing screening examinations or symptomatic patients, including cancer patients.Current Concepts: For patients with axillary lymphadenopathy in the setting of recent ipsilateral vaccination, clinical follow-up is recommended. In other scenarios, short-term follow-up axillary ultrasound examinations are recommended if the clinical concerns persist for more than 6 weeks after vaccination. To mitigate the diagnostic dilemma of vaccine-induced lymphadenopathy, patients should schedule screening imaging examinations before the first vaccination or at least six weeks following the second vaccination. For clinicians and radiologists, documenting the patients’ vaccination status is critical to decreasing unnecessary follow-up imaging, biopsies, and patient’s anxiety.Discussion and Conclusion: Our proposal can help reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged lymph nodes in the setting of recent COVID-19 vaccination. Further, it can avoid delays in vaccination and breast cancer screening during the COVID-19 pandemic.

Added value of deep learning-based computer-aided diagnosis and shear wave elastography to b-mode ultrasound for evaluation of breast masses detected by screening ultrasound

Low specificity and operator dependency are the main problems of breast ultrasound (US) screening. We investigated the added value of deep learning-based computer-aided diagnosis (S-Detect) and shear wave elastography (SWE) to B-mode US for evaluation of breast masses detected by screening US.Between February 2018 and June 2019, B-mode US, S-Detect, and SWE were prospectively obtained for 156 screening US-detected breast masses in 146 women before undergoing US-guided biopsy. S-Detect was applied for the representative B-mode US image, and quantitative elasticity was measured for SWE. Breast Imaging Reporting and Data System final assessment category was assigned for the datasets of B-mode US alone, B-mode US plus S-Detect, and B-mode US plus SWE by 3 radiologists with varied experience in breast imaging. Area under the receiver operator characteristics curve (AUC), sensitivity, and specificity for the 3 datasets were compared using Delong's method and McNemar test.Of 156 masses, 10 (6%) were malignant and 146 (94%) were benign. Compared to B-mode US alone, the addition of S-Detect increased the specificity from 8%-9% to 31%-71% and the AUC from 0.541-0.545 to 0.658-0.803 in all radiologists (All P < .001). The addition of SWE to B-mode US also increased the specificity from 8%-9% to 41%-75% and the AUC from 0.541-0.545 to 0.709-0.823 in all radiologists (All P < .001). There was no significant loss in sensitivity when either S-Detect or SWE were added to B-mode US.Adding S-Detect or SWE to B-mode US improved the specificity and AUC without loss of sensitivity.

Glandular Tissue Component and Breast Cancer Risk in Mammographically Dense Breasts at Screening Breast US

Background Breast density at mammography is an established risk factor for breast cancer, but it cannot be used to distinguish between glandular and fibrous tissue. Purpose To evaluate the association between the glandular tissue component (GTC) at screening breast US and the risk of future breast cancer in women with dense breasts and the association between the GTC and lobular involution. Materials and Methods Screening breast US examinations performed in women with no prior history of breast cancer and with dense breasts with negative findings from mammography from January 2012 to December 2015 were retrospectively identified. The GTC was reported as being minimal, mild, moderate, or marked at the time of the US examination. In women who had benign breast biopsy results, the degree of lobular involution in normal background tissue was categorized as not present, mild, moderate, or complete. The GTC-related breast cancer risk in women with a cancer diagnosis or follow-up after 6 months was estimated by using Cox proportional hazards regression. Cumulative logistic regression was used to evaluate the association between the GTC and lobular involution. Results Among 8483 women (mean age, 49 years ± 8 [standard deviation]), 137 developed breast cancer over a median follow-up time of 5.3 years. Compared with a minimal or mild GTC, a moderate or marked GTC was associated with an increased cancer risk (hazard ratio, 1.5; 95% CI: 1.05, 2.1; P = .03) after adjusting for age and breast density. The GTC had an inverse association with lobular involution; women with no, mild, or moderate involution had greater odds (odds ratios of 4.9 [95% CI: 1.5, 16.6], 2.6 [95% CI: 0.95, 7.2], and 1.8 [95% CI: 0.7, 4.6], respectively) of a moderate or marked GTC than those with complete involution (P = .004). Conclusion The glandular tissue component was independently associated with the future breast cancer risk in women with dense breasts and reflects the lobular involution. It should be considered for risk stratification during screening breast US. © RSNA, 2021 Online supplemental material is available for this article.

Accuracy of Digital Breast Tomosynthesis for Detecting Breast Cancer in the Diagnostic Setting: A Systematic Review and Meta-Analysis

Objective

To compare the accuracy for detecting breast cancer in the diagnostic setting between the use of digital breast tomosynthesis (DBT), defined as DBT alone or combined DBT and digital mammography (DM), and the use of DM alone through a systematic review and meta-analysis.

Materials and Methods

Ovid-MEDLINE, Ovid-Embase, Cochrane Library and five Korean local databases were searched for articles published until March 25, 2020. We selected studies that reported diagnostic accuracy in women who were recalled after screening or symptomatic. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to estimate pooled sensitivity and specificity. We compared the diagnostic accuracy between DBT and DM alone using meta-regression and subgroup analyses by modality of intervention, country, existence of calcifications, breast density, Breast Imaging Reporting and Data System category threshold, study design, protocol for participant sampling, sample size, reason for diagnostic examination, and number of readers who interpreted the studies.

Results

Twenty studies (n = 44513) that compared DBT and DM alone were included. The pooled sensitivity and specificity were 0.90 (95% confidence interval [CI] 0.86–0.93) and 0.90 (95% CI 0.84–0.94), respectively, for DBT, which were higher than 0.76 (95% CI 0.68–0.83) and 0.83 (95% CI 0.73–0.89), respectively, for DM alone (p < 0.001). The area under the summary receiver operating characteristics curve was 0.95 (95% CI 0.93–0.97) for DBT and 0.86 (95% CI 0.82–0.88) for DM alone. The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses.

Conclusion

Use of DBT was more accurate than DM alone for the diagnosis of breast cancer. Women with clinical symptoms or abnormal screening findings could be more effectively evaluated for breast cancer using DBT, which has a superior diagnostic performance compared to DM alone.

Noncontrast-Enhanced MR-Based Conductivity Imaging for Breast Cancer Detection and Lesion Differentiation

BACKGROUND

There is increasing interest in noncontrast-enhanced MRI due to safety concerns for gadolinium contrast agents.

PURPOSE

To investigate the clinical feasibility of MR-based conductivity imaging for breast cancer detection and lesion differentiation.

STUDY TYPE

Prospective.

SUBJECTS

One hundred and ten women, with 112 known cancers and 17 benign lesions (biopsy-proven), scheduled for preoperative MRI.

FIELD STRENGTH/SEQUENCE

Non-fat-suppressed T2-weighted turbo spin-echo sequence (T2WI), dynamic contrast-enhanced MRI and diffusion-weighted imaging (DWI) at 3T.

ASSESSMENT

Cancer detectability on each imaging modality was qualitatively evaluated on a per-breast basis: the conductivity maps derived from T2WI were independently reviewed by three radiologists (R1-R3). T2WI, DWI, and pre-operative digital mammography were independently reviewed by three other radiologists (R4-R6). Conductivity and apparent diffusion coefficient (ADC) measurements (mean, minimum, and maximum) were performed for 112 cancers and 17 benign lesions independently by two radiologists (R1 and R2). Tumor size was measured from surgical specimens.

STATISTICAL TESTS

Cancer detection rates were compared using generalized estimating equations. Multivariable logistic regression analysis was performed to identify factors associated with cancer detectability. Discriminating ability of conductivity and ADC was evaluated by using the areas under the receiver operating characteristic curve (AUC).

RESULTS

Conductivity imaging showed lower cancer detection rates (20%-32%) compared to T2WI (62%-71%), DWI (85%-90%), and mammography (79%-88%) (all P < 0.05). Fatty breast on MRI (odds ratio = 11.8, P < 0.05) and invasive tumor size (odds ratio = 1.7, P < 0.05) were associated with cancer detectability of conductivity imaging. The maximum conductivity showed comparable ability to the mean ADC in discriminating between cancers and benign lesions (AUC = 0.67 [95% CI: 0.59, 0.75] vs. 0.84 [0.76, 0.90], P = 0.06 (R1); 0.65 [0.56, 0.73] vs. 0.82 [0.74, 0.88], P = 0.07 (R2)).

DATA CONCLUSION

Although conductivity imaging showed suboptimal performance in breast cancer detection, the quantitative measurement of conductivity showed the potential for lesion differentiation.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Detection of Contralateral Breast Cancer Using Diffusion-Weighted Magnetic Resonance Imaging in Women with Newly Diagnosed Breast Cancer: Comparison with Combined Mammography and Whole-Breast Ultrasound

Objective

To compare the screening performance of diffusion-weighted (DW) MRI and combined mammography and ultrasound (US) in detecting clinically occult contralateral breast cancer in women with newly diagnosed breast cancer.

Materials and Methods

Between January 2017 and July 2018, 1148 women (mean age ± standard deviation, 53.2 ± 10.8 years) with unilateral breast cancer and no clinical abnormalities in the contralateral breast underwent 3T MRI, digital mammography, and radiologist-performed whole-breast US. In this retrospective study, three radiologists independently and blindly reviewed all DW MR images (b = 1000 s/mm² and apparent diffusion coefficient map) of the contralateral breast and assigned a Breast Imaging Reporting and Data System category. For combined mammography and US evaluation, prospectively assessed results were used. Using histopathology or 1-year follow-up as the reference standard, cancer detection rate and the patient percentage with cancers detected among all women recommended for tissue diagnosis (positive predictive value; PPV₂) were compared.

Results

Of the 30 cases of clinically occult contralateral cancers (13 invasive and 17 ductal carcinoma in situ [DCIS]), DW MRI detected 23 (76.7%) cases (11 invasive and 12 DCIS), whereas combined mammography and US detected 12 (40.0%, five invasive and seven DCIS) cases. All cancers detected by combined mammography and US, except two DCIS cases, were detected by DW MRI. The cancer detection rate of DW MRI (2.0%; 95% confidence interval [CI]: 1.3%, 3.0%) was higher than that of combined mammography and US (1.0%; 95% CI: 0.5%, 1.8%; p = 0.009). DW MRI showed higher PPV₂ (42.1%; 95% CI: 26.3%, 59.2%) than combined mammography and US (18.5%; 95% CI: 9.9%, 30.0%; p = 0.001).

Conclusion

In women with newly diagnosed breast cancer, DW MRI detected significantly more contralateral breast cancers with fewer biopsy recommendations than combined mammography and US.

Automated breast US as the primary screening test for breast cancer among East Asian women aged 40-49 years: a multicenter prospective study

OBJECTIVES

To prospectively evaluate the diagnostic performance of screening ABUS as the primary screening test for breast cancer among Korean women aged 40-49 years.

METHODS

This prospective, multicenter study included asymptomatic Korean women aged 40-49 years from three academic centers between February 2017 and October 2019. Each participant underwent ABUS without mammography, and the ABUS images were interpreted at each hospital with double-reading by two breast radiologists. Biopsy and at least 1 year of follow-up was considered the reference standard. Diagnostic performance of ABUS screening and subgroup analyses according to patient and tumor characteristics were evaluated.

RESULTS

Reference standard data were available for 959 women. The recall rate was 9.8% (95% confidence interval [CI]: 7.9%, 11.7%; 94 of 959 women) and the cancer detection yield was 5.2 per 1000 women (95% CI: -0.6, 11.1; 5 of 959 women). There was only one interval cancer. The sensitivity was 83.3% (95% CI: 53.5%, 100%; 5 of 6 cancers) and the specificity was 90.7% (95% CI: 88.8%, 92.5%; 864 of 95. women). The positive predictive values of biopsies performed (PPV₃) was 20.0% (95% CI: 4.3%, 35.7%; 5 of 25 women). Women with heterogeneous background echotexture had a higher recall rate (p = .009) and lower specificity (p = .036). Women with body mass index values < 25 kg/m² had a higher mean recall rate (p = .046).

CONCLUSION

In East Asia, screening automated breast US may be an alternative to screening mammography for detecting breast cancers in women aged 40-49 years.

KEY POINTS

• Automated breast US screening for breast cancer in asymptomatic women aged 40-49 is effective with 5.2 per 1000 cancer detection yield. • Women with heterogeneous background echotexture had a higher recall rate and lower specificity. • Women with body mass index < 25 kg/m² had a higher recall rate.

Factors Affecting Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: Development and Validation of a Predictive Nomogram

Background There is an increasing need to develop a more accurate prediction model for pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Purpose To develop a nomogram based on MRI and clinical-pathologic variables to predict pCR. Materials and Methods In this single-center retrospective study, consecutive women with stage II-III breast cancer who underwent NAC followed by surgery between January 2011 and December 2017 were considered for inclusion. The women were divided into a development cohort between January 2011 and September 2015 and a validation cohort between October 2015 and December 2017. Clinical-pathologic data were collected, and mammograms and MRI scans obtained before and after NAC were analyzed. Logistic regression analyses were performed to identify independent variables associated with pCR in the development cohort from which the nomogram was created. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results A total of 359 women (mean age, 49 years ± 10 [standard deviation]) were in the development cohort and 351 (49 years ± 10) in the validation cohort. Hormone receptor negativity (odds ratio [OR], 3.1; 95% CI: 1.4, 7.1; P = .006), high Ki-67 index (OR, 1.05; 95% CI: 1.03, 1.07; P < .001), and post-NAC MRI variables, including small tumor size (OR, 0.6; 95% CI: 0.4, 0.9; P = .03), low lesion-to-background parenchymal signal enhancement ratio (OR, 0.2; 95% CI: 0.1, 0.6; P = .004), and absence of enhancement in the tumor bed (OR, 3.8; 95% CI: 1.4, 10.5; P = .009) were independently associated with pCR. The nomogram incorporating these variables showed good discrimination (AUC, 0.90; 95% CI: 0.86, 0.94) and calibration abilities (calibration slope, 0.91; 95% CI: 0.69, 1.13) in the independent validation cohort. Conclusion A nomogram incorporating hormone receptor status, Ki-67 index, and MRI variables showed good discrimination and calibration abilities in predicting pathologic complete response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco and Ponsiglione in this issue.

Usefulness of staging chest-CT in patients with operable breast cancer

Objective

The aim of this study was to investigate the usefulness of staging chest-CT in terms of diagnostic yield and false-referral rate in patients with operable breast cancer.

Materials and methods

This study was approved by the institutional review border. In this retrospective study, we reviewed patients who underwent staging chest-CT between January 2014 and June 2016. Reference standard was defined as a combination of pathology and radiologic tumor changes in accordance with primary tumor or metastatic lesions and stability during the 12-month follow-up period. We calculated diagnostic yield and false-referral rates stratified by pathologic stage. The important ancillary findings of staging chest-CT were also recorded.

Results

A total of 1,342 patients were included in this study. Of these, four patients (0.3%; 4/1342) had true pulmonary metastasis. Diagnostic yields of stage I, II, III disease were 0.0% (0/521), 0.3% (2/693), and 1.6% (2/128), respectively. The overall false-referral rate was 4.6% (62/1342); false-referral rates of stage I, II, and III disease were 5.0% (26/521), 3.8% (26/693), and 7.8% (10/128), respectively. No occult thoracic metastasis occurred within 12 months of staging chest-CT. Nineteen patients showed significant ancillary findings besides lung metastasis, including primary lung cancer (n = 9). The overall diagnostic yield of ancillary findings was 1.7% (23 of 1342).

Conclusions

The incidence of pulmonary metastasis was near zero for pathologic stages I/II and slightly higher (although still low; 1.6%). for stage III. Considering its low diagnostic yield and substantial false-referral rates, staging chest-CT might not be useful in patients with operable breast cancer.

Addition of Screening Breast US to Digital Mammography and Digital Breast Tomosynthesis for Breast Cancer Screening in Women at Average Risk

Background Digital breast tomosynthesis (DBT) with or without digital mammography (DM) is the primary method of breast cancer screening. However, the sufficiency of DBT screening for women at average risk and the need for supplemental whole-breast US needs further investigation. Purpose To evaluate the added value of supplemental US screening following combined DM/DBT. Materials and Methods A retrospective database search identified consecutive asymptomatic women who underwent DM/DBT and radiologist-performed screening breast US simultaneously between March 2016 and December 2018. The cancer detection rate (CDR) per 1000 screening examinations, sensitivity, specificity, and abnormal interpretation rate of DM/DBT and DM/DBT combined with US were compared. Results A total of 1003 women (mean age, 56 years ± 8.6 [standard deviation]) were included. Among them, 12 cancers (mean invasive tumor size, 14 mm; range, 6-33 mm) were diagnosed. With DM/DBT and DM/DBT combined with US, the CDRs were 9.0 per 1000 screening examinations (nine of 1003 women; 95% CI: 4.1, 17) and 12 per 1000 screening examinations (12 of 1003 women; 95% CI: 6.2, 21), respectively, and the abnormal interpretation rates were 7.8% (78 of 1003 women; 95% CI: 6.2, 9.6) and 24% (243 of 1003 women; 95% CI: 22, 27). In women with negative findings at DM/DBT, supplementary US yielded a CDR of 3.2 per 1000 examinations (three of 925 women; 95% CI: 0.7, 9.4), sensitivity of 100% (three of three women; 95% CI: 29, 100), specificity of 82% (760 of 922 women; 95% CI: 80, 85), and abnormal interpretation rate of 18% (165 of 925 women; 95% CI: 15, 21). The three additional US-detected cancers were identified in women with dense breasts; no benefit was observed in women with nondense breasts. Conclusion The addition of breast US to digital mammography and digital breast tomosynthesis yielded an additional 0.7-9.4 cancers per 1000 women at average risk, with a substantial increase in the abnormal interpretation rate. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rahbar in this issue.

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P = .018) and lower parallel orientation score (P = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists' Breast Imaging Reporting and Data Systems (BI-RADS) final assessment (P = .014), and patient age (P < .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists' BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P < .001 in the development cohort; 97% vs. 45% P < .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.

Comparison of Abbreviated MRI and Full Diagnostic MRI in Distinguishing between Benign and Malignant Lesions Detected by Breast MRI: A Multireader Study

Objective

To compare the performance of simulated abbreviated breast MRI (AB-MRI) and full diagnostic (FD)-MRI in distinguishing between benign and malignant lesions detected by MRI and investigate the features of discrepant lesions of the two protocols.

Materials and Methods

An AB-MRI set with single first postcontrast images was retrospectively obtained from an FD-MRI cohort of 111 lesions (34 malignant, 77 benign) detected by contralateral breast MRI in 111 women (mean age, 49.8. ± 9.8; range, 28–75 years) with recently diagnosed breast cancer. Five blinded readers independently classified the likelihood of malignancy using Breast Imaging Reporting and Data System assessments. McNemar tests and area under the receiver operating characteristic curve (AUC) analyses were performed. The imaging and pathologic features of the discrepant lesions of the two protocols were analyzed.

Results

The sensitivity of AB-MRI for lesion characterization tended to be lower than that of FD-MRI for all readers (58.8–82.4% vs. 79.4–100%), although the findings of only two readers were significantly different (p < 0.05). The specificity of AB-MRI for lesion characterization was higher than that of FD-MRI for 80% of readers (39.0–74.0% vs. 19.5–45.5%, p ≤ 0.001). The AUC of AB-MRI was comparable to that of FD-MRI for all readers (p > 0.05). Fifteen percent (5/34) of the cancers were false-negatives on AB-MRI. More suspicious margins or internal enhancement on the delayed phase images were related to the discrepancies.

Conclusion

The overall performance of AB-MRI was similar to that of FD-MRI in distinguishing between benign and malignant lesions. AB-MRI showed lower sensitivity and higher specificity than FD-MRI, as 15% of the cancers were misclassified compared to FD-MRI.

Ultrafast Dynamic Contrast-Enhanced Breast MRI: Lesion Conspicuity and Size Assessment according to Background Parenchymal Enhancement

Objective

To evaluate the clinical utility of ultrafast dynamic contrast-enhanced (DCE)-MRI compared to conventional DCE-MRI by studying lesion conspicuity and size according to the level of background parenchymal enhancement (BPE).

Materials and Methods

This study included 360 women (median age, 54 years; range, 26–82 years) with 361 who had undergone breast MRI, including both ultrafast and conventional DCE-MRI before surgery, between January and December 2017. Conspicuity was evaluated using a five-point score. Size was measured as the single maximal diameter. The Wilcoxon signed-rank test was used to compare median conspicuity score. To identify factors associated with conspicuity, multivariable logistic regression was performed. Absolute agreement between size at MRI and histopathologic examination was assessed using the intraclass correlation coefficient (ICC).

Results

The median conspicuity scores were 5 at both scans, but the interquartile ranges were significantly different (5-5 at ultrafast vs. 4-5 at conventional, p < 0.001). Premenopausal status (odds ratio [OR] = 2.2, p = 0.048), non-mass enhancement (OR = 4.1, p = 0.001), moderate to marked BPE (OR = 7.5, p < 0.001), and shorter time to enhancement (OR = 0.9, p = 0.043) were independently associated with better conspicuity at ultrafast scans. Tumor size agreement between MRI and histopathologic examination was similar for both scans (ICC = 0.66 for ultrafast vs. 0.63 for conventional).

Conclusion

Ultrafast DCE-MRI could improve lesion conspicuity compared to conventional DCE-MRI, especially in women with premenopausal status, non-mass enhancement, moderate to marked BPE or short time to enhancement.

Automated Breast Ultrasound System for Breast Cancer Evaluation: Diagnostic Performance of the Two-View Scan Technique in Women with Small Breasts

OBJECTIVE

To comparatively evaluate the scan coverage and diagnostic performance of the two-view scan technique (2-VST) of the automated breast ultrasound system (ABUS) versus the conventional three-view scan technique (3-VST) in women with small breasts.

MATERIALS AND METHODS

Between March 2016 and May 2017, 136 asymptomatic women with small breasts (bra cup size A) suitable for 2-VST were enrolled. Subsequently, 272 breasts were subjected to bilateral whole-breast ultrasound examinations using ABUS and the hand-held ultrasound system (HHUS). During ABUS image acquisition, one breast was scanned with 2-VST, while the other breast was scanned with 3-VST. In each breast, the breast coverage and visibility of the HHUS detected lesions on ABUS were assessed. The sensitivity and specificity of ABUS were compared between 2-VST and 3-VST.

RESULTS

Among 136 breasts, eight cases of breast cancer were detected by 2-VST, and 10 cases of breast cancer were detected by 3-VST. The breast coverage was satisfactory in 94.1% and 91.9% of cases under 2-VST and 3-VST, respectively (p = 0.318). All HHUS-detected lesions were visible on the ABUS images regardless of the scan technique. The sensitivities and specificities were similar between 2-VST and 3-VST (100% [8/8] vs. 100% [10/10], and 97.7% [125/128] vs. 95.2% [120/126], respectively), with no significant difference (p > 0.05).

CONCLUSION

2-VST of ABUS achieved comparable scan coverage and diagnostic performance to that of conventional 3-VST in women with small breasts.

Utility and Diagnostic Performance of Automated Breast Ultrasound System in Evaluating Pure Non-Mass Enhancement on Breast Magnetic Resonance Imaging

Objective

To compare the utility and diagnostic performance of automated breast ultrasound system (ABUS) with that of hand-held ultrasound (HHUS) in evaluating pure non-mass enhancement (NME) lesions on breast magnetic resonance imaging (MRI).

Materials and Methods

One hundred twenty-six consecutive MRI-visible pure NME lesions of 122 patients with breast cancer were assessed from April 2016 to March 2017. Two radiologists reviewed the preoperative breast MRI, ABUS, and HHUS images along with mammography (MG) findings. The NME correlation rate and diagnostic performance of ABUS were compared with that of HHUS, and the imaging features associated with ABUS visibility were analyzed.

Results

Among 126 pure NME lesions, 100 (79.4%) were malignant and 26 (20.6%) were benign. The overall correlation rate was 87.3% (110/126) in ABUS and 92.9% (117/126) in HHUS. The sensitivity and specificity were 87% and 50% for ABUS and 92% and 42.3% for HHUS, respectively, with no significant differences (p = 0.180 and 0.727, respectively). Malignant NME was more frequently visualized than benign NME lesions on ABUS (93% vs. 65.4%, p = 0.001). Significant factors associated with the visibility of ABUS were the size of NME lesions on MRI (p < 0.001), their distribution pattern (p < 0.001), and microcalcifications on MG (p = 0.027).

Conclusion

ABUS evaluation of pure NME lesions on MRI in patients with breast cancer is a useful technique with high visibility, especially in malignant lesions. The diagnostic performance of ABUS was comparable with that of conventional HHUS in evaluating NME lesions.

Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network

BACKGROUND AND OBJECTIVES

Automated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this study, an effective and fast computer-aided detection (CADe) system based on a 3-D convolutional neural network (CNN) is proposed as the second reader for the physician in order to decrease the reviewing time and misdetection rate.

METHODS

Our CADe system uses the sliding window method, a CNN-based determining model, and a candidate aggregation algorithm. First, the sliding window method is performed to split the ABUS volume into volumes of interest (VOIs). Afterward, VOIs are selected as tumor candidates by our determining model. To achieve higher performance, focal loss and ensemble learning are used to solve data imbalance and reduce false positive (FP) and false negative (FN) rates. Because several selected candidates may be part of the same tumor and they may overlap each other, a candidate aggregation method is applied to merge the overlapping candidates into the final detection result.

RESULTS

In the experiments, 165 and 81 cases are utilized for training the system and evaluating system performance, respectively. On evaluation with the 81 cases, our system achieves sensitivities of 100% (81/81), 95.3% (77/81), and 90.9% (74/81) with FPs per pass (per case) of 21.6 (126.2), 6.0 (34.8), and 4.6 (27.1) respectively. According to the results, the number of FPs per pass (per case) can be diminished by 56.8% (57.1%) at a sensitivity of 95.3% based on our tumor detection model.

CONCLUSIONS

In conclusion, our CADe system using 3-D CNN with the focal loss and ensemble learning may have the capability of being a tumor detection system in ABUS image.

Diffusion-weighted MRI at 3.0 T for detection of occult disease in the contralateral breast in women with newly diagnosed breast cancer

PURPOSE

Diffusion-weighted magnetic resonance imaging (DW-MRI) offers unenhanced method to detect breast cancer without cost and safety concerns associated with dynamic contrast-enhanced (DCE) MRI. Our purpose was to evaluate the performance of DW-MRI at 3.0T in detection of clinically and mammographically occult contralateral breast cancer in patients with unilateral breast cancer.

METHODS

Between 2017 and 2018, 1130 patients (mean age 53.3 years; range 26-84 years) with newly diagnosed unilateral breast cancer who underwent breast MRI and had no abnormalities on clinical and mammographic examinations of contralateral breast were included. Three experienced radiologists independently reviewed DW-MRI (b = 0 and 1000 s/mm²) and DCE-MRI and assigned a BI-RADS category. Using histopathology or 1-year clinical follow-up, performance measures of DW-MRI were compared with DCE-MRI.

RESULTS

A total of 21 (1.9%, 21/1130) cancers were identified (12 ductal carcinoma in situ and 9 invasive ductal carcinoma; mean invasive tumor size, 8.0 mm) in the contralateral breast. Cancer detection rate of DW-MRI was 13-15 with mean of 14 per 1000 examinations (95% confidence interval [CI] 9-23 per 1000 examinations), which was lower than that of DCE-MRI (18-19 with mean of 18 per 1000 examinations, P = 0.01). A lower abnormal interpretation rate (14.0% versus 17.0%, respectively, P < 0.001) with higher specificity (87.3% versus 84.6%, respectively, P < 0.001) but lower sensitivity (77.8% versus 96.8%, respectively, P < 0.001) was noted for DW-MRI compared to DCE-MRI.

CONCLUSIONS

DW-MRI at 3.0T has the potential as a cost-effective tool for evaluation of contralateral breast in women with newly diagnosed breast cancer.

Axillary Nodal Evaluation in Breast Cancer: State of the Art

Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.