Screening in Patients With Dense Breasts: Comparison of Mammography, Artificial Intelligence, and Supplementary Ultrasound.

BACKGROUND. Screening mammography has decreased performance in patients with dense breasts. Supplementary screening ultrasound is a recommended option in such patients, although it has yielded mixed results in prior investigations. OBJECTIVE. The purpose of this article is to compare the performance characteristics of screening mammography alone, standalone artificial intelligence (AI), ultrasound alone, and mammography in combination with AI and/or ultrasound in patients with dense breasts. METHODS. This retrospective study included 1325 women (mean age, 53 years) with dense breasts who underwent both screening mammography and supplementary breast ultrasound within a 1-month interval from January 2017 to December 2017; prior mammography and prior ultrasound examinations were available for comparison in 91.2% and 91.8%, respectively. Mammography and ultrasound examinations were interpreted by one of 15 radiologists (five staff; 10 fellows); clinical reports were used for the present analysis. A commercial AI tool was used to retrospectively evaluate mammographic examinations for presence of cancer. Screening performances were compared among mammography, AI, ultrasound, and test combinations, using generalized estimating equations. Benign diagnoses required 24 months or longer of imaging stability. RESULTS. Twelve cancers (six invasive ductal carcinoma; six ductal carcinoma in situ) were diagnosed. Mammography, standalone AI, and ultrasound showed cancer detection rates (per 1000 patients) of 6.0, 6.8, and 6.0 (all p > .05); recall rates of 4.4%, 11.9%, and 9.2% (all p < .05); sensitivity of 66.7%, 75.0%, and 66.7% (all p > .05); specificity of 96.2%, 88.7%, and 91.3% (all p < .05); and accuracy of 95.9%, 88.5%, and 91.1% (all p < .05). Mammography with AI, mammography with ultrasound, and mammography with both ultrasound and AI showed cancer detection rates of 7.5, 9.1, and 9.1 (all p > .05); recall rates of 14.9, 11.7, and 21.4 (all p < .05); sensitivity of 83.3%, 100.0%, and 100.0% (all p > .05); specificity of 85.8%, 89.1%, and 79.4% (all p < .05); and accuracy of 85.7%, 89.2%, and 79.5% (all p < .05). CONCLUSION. Mammography with supplementary ultrasound showed higher accuracy, higher specificity, and lower recall rate in comparison with mammography with AI and in comparison with mammography with both ultrasound and AI. CLINICAL IMPACT. The findings fail to show benefit of AI with respect to screening mammography performed with supplementary breast ultrasound in patients with dense breasts.

International Expert Consensus on US Lexicon for Thyroid Nodules.

Multiple US-based systems for risk stratification of thyroid nodules are in use worldwide. Unfortunately, the malignancy probability assigned to a nodule varies, and terms and definitions are not consistent, leading to confusion and making it challenging to compare study results and craft revisions. Consistent application of these systems is further hampered by interobserver variability in identifying the sonographic features on which they are founded. In 2018, an international multidisciplinary group of 19 physicians with expertise in thyroid sonography (termed the International Thyroid Nodule Ultrasound Working Group) was convened with the goal of developing an international system, tentatively called the International Thyroid Imaging Reporting and Data System, or I-TIRADS, in two phases: (phase I) creation of a lexicon and atlas of US descriptors of thyroid nodules and (phase II) development of a system that estimates the malignancy risk of a thyroid nodule. This article presents the methods and results of phase I. The purpose herein is to show what has been accomplished thus far, as well as generate interest in and support for this effort in the global thyroid community.

Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, P = .002), but not for historic cohort studies (0.89 vs 0.96, P = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, P < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Scaranelo in this issue.

Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography.

BACKGROUND: Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views. PURPOSE: To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view. MATERIAL AND METHODS: From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. RESULTS: Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance (P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P < 0.001) significantly improved with AI assistance. CONCLUSION: AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.

US, Mammography, and Histopathologic Evaluation to Identify Low Nuclear Grade Ductal Carcinoma in Situ.

Background Low nuclear grade ductal carcinoma in situ (DCIS) identified at biopsy can be upgraded to intermediate to high nuclear grade DCIS at surgery. Methods that confirm low nuclear grade are needed to consider nonsurgical approaches for these patients. Purpose To develop a preoperative model to identify low nuclear grade DCIS and to evaluate factors associated with low nuclear grade DCIS at biopsy that was not upgraded to intermediate to high nuclear grade DCIS at surgery. Materials and Methods In this retrospective study, 470 women (median age, 50 years; interquartile range, 44-58 years) with 477 pure DCIS lesions at surgical histopathologic evaluation were included (January 2010 to December 2015). Patients were divided into the training set (n = 330) or validation set (n = 147) to develop a preoperative model to identify low nuclear grade DCIS. Features at US (mass, nonmass) and at mammography (morphologic characteristics, distribution of microcalcification) were reviewed. The upgrade rate of low nuclear grade DCIS was calculated, and multivariable regression was used to evaluate factors for associations with low nuclear grade DCIS that was not upgraded later. Results A preoperative model that included lesions manifesting as a mass at US without microcalcification and no comedonecrosis at biopsy was used to identify low nuclear grade DCIS, with a high area under the receiver operating characteristic curve of 0.97 (95% CI: 0.94, 1.00) in the validation set. The upgrade rate of low nuclear grade DCIS at biopsy was 38.8% (50 of 129). Ki-67 positivity (odds ratio, 0.04; 95% CI: 0.0003, 0.43; P = .005) was inversely associated with constant low nuclear grade DCIS. Conclusion The upgrade rate of low nuclear grade ductal carcinoma in situ (DCIS) at biopsy to intermediate to high nuclear grade DCIS at surgery occurred in more than a third of patients; low nuclear grade DCIS at final histopathologic evaluation could be identified if the mass was viewed at US without microcalcifications and had no comedonecrosis at histopathologic evaluation of biopsy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Rahbar in this issue. An earlier incorrect version appeared online. This article was corrected on April 14, 2022.

Ultrafast dynamic contrast-enhanced breast MRI: association with pathologic complete response in neoadjuvant treatment of breast cancer.

OBJECTIVES: The purpose of this study was to investigate whether pretreatment kinetic features from ultrafast DCE-MRI are associated with pathological complete response (pCR) in patients with invasive breast cancer and according to immunohistochemistry (IHC) subtype. METHODS: Between August 2018 and June 2019, 256 consecutive breast cancer patients (mean age, 50.2 years; range, 25-86 years) who underwent both ultrafast and conventional DCE-MRI and surgery following neoadjuvant chemotherapy were included. DCE-MRI kinetic features were obtained from pretreatment MRI data. Time-to-enhancement, maximal slope (MS), and volumes at U1 and U2 (U1, time point at which the lesion starts to enhance; U2, subsequent time point after U1) were derived from ultrafast MRI. Logistic regression analysis was performed to identify factors associated with pCR. RESULTS: Overall, 41.4% of all patients achieved pCR. None of the kinetic features was associated with pCR when including all cancers. Among ultrafast DCE-MRI kinetic features, a lower MS (OR, 0.982; p = 0.040) was associated with pCR at univariable analysis in hormone receptor (HR)-positive cancers. In triple-negative cancers, a higher volume ratio U1/U2 was associated with pCR at univariable (OR, 11.787; p = 0.006) and multivariable analysis (OR, 14.811; p = 0.005). Among conventional DCE-MRI kinetic features, a lower peak enhancement (OR, 0.993; p = 0.031) and a lower percentage of washout (OR, 0.904; p = 0.039) was associated with pCR only in HR-positive cancers at univariable analysis. CONCLUSIONS: A higher volume ratio of U1/U2 derived from ultrafast DCE-MRI was independently associated with pCR in triple-negative invasive breast cancer. KEY POINTS: • The ratio of tumor volumes obtained at the first (U1) and second time points (U2) of enhancement was independently associated with pCR in triple-negative invasive breast cancers. • Ultrafast MRI has the potential to improve accuracy in predicting treatment response and personalizing therapy.

AI-CAD for differentiating lesions presenting as calcifications only on mammography: outcome analysis incorporating the ACR BI-RADS descriptors for calcifications.

OBJECTIVES: To evaluate how AI-CAD triages calcifications and to compare its performance to an experienced breast radiologist. METHODS: Among routine mammography performed between June 2016 and May 2018, 535 lesions detected as calcifications only on mammography in 500 women (mean age, 48.8 years) that were additionally interpreted with additional magnification views were included in this study. One dedicated breast radiologist retrospectively reviewed the magnification mammograms to assess morphology, distribution, and final assessment category according to ACR BI-RADS. AI-CAD analyzed routine mammograms providing AI-CAD marks and corresponding AI-CAD scores (ranging from 0 to 100%), for which values ≥ 10% were considered positive. Ground truth in terms of malignancy or benignity was confirmed with a histopathologic diagnosis or at least 1 year of imaging follow - up. RESULTS: Of the 535 calcifications, 215 (40.2%) were malignant. Calcifications with positive AI-CAD scores showed significantly higher PPVs compared to calcifications with negative scores for all morphology (all p < 0.05). PPVs were significantly higher in calcifications with positive AI-CAD scores compared to those with negative scores for BI-RADS 3, 4a, or 4b assessments (all p < 0.05). AI-CAD and the experienced radiologist did not show significant difference in diagnostic performance; sensitivity 92.1% vs 95.4% (p = 0.125), specificity 71.9% vs 72.5% (p = 0.842), and accuracy 80.0% vs 81.7% (p = 0.413). CONCLUSION: Among calcifications with same morphology or BI-RADS assessment, those with positive AI-CAD scores had significantly higher PPVs. AI-CAD showed similar diagnostic performances to the experienced radiologist for calcifications detected on mammography. KEY POINTS: • Among calcifications with same morphology or BI-RADS assessment, those with positive AI-CAD scores had significantly higher PPVs. • AI-CAD showed similar diagnostic performance to an experienced radiologist in assessing lesions detected as calcifications only on mammography. • Among malignant calcifications, calcifications with positive AI-CAD scores showed higher rates of invasive cancers than calcifications with negative scores (all p > 0.05).

Cancer yield and imaging features of probably benign calcifications at digital magnification view.

OBJECTIVES: To investigate the malignancy rate of probably benign calcifications assessed by digital magnification view and imaging and clinical features associated with malignancy. METHODS: This retrospective study included consecutive women with digital magnification views assessed as probably benign for calcifications without other associated mammographic findings from March 2009 to January 2014. Initial studies rendering a probably benign assessment were analyzed, with biopsy or 4-year imaging follow-up. Fisher's exact test and univariable logistic regression were performed. Cancer yields were calculated. RESULTS: A total of 458 lesions in 422 patients were finally included. The overall cancer yield was 2.2% (10 of 458, invasive cancer [n = 4] and DCIS [n = 6]). Calcification distribution (OR = 23.80, p = .041), calcification morphology (OR = 10.84, p = .005), increased calcifications (OR = 29.40, p = .001), and having a concurrent newly diagnosed breast cancer or high-risk lesion (OR = 10.24, p = .001) were associated with malignancy. Cancer yields did not significantly differ between grouped punctate calcifications vs. calcifications with other features (1.2% [2 of 162] vs. 2.7% [8 of 296], p = .506). The cancer yield was 1.6% (7 of 437) in women without newly diagnosed breast cancer or high-risk lesions. CONCLUSION: The cancer yield of probably benign calcifications assessed by digital magnification view was below the 2% threshold for grouped punctate calcifications and for women without newly diagnosed breast cancer or high-risk lesions. Calcification distribution, morphology, increase in calcifications, and the presence of newly diagnosed breast cancer/high-risk lesion were associated with malignancy. KEY POINTS: • Among 458 probably benign calcifications assessed by digital magnification view, the overall cancer yield was 2.2% (10 of 458). • The cancer yield was below the 2% threshold for grouped punctate calcifications (1.2%, 2 of 162) and in women without newly diagnosed breast cancer or high-risk lesions (1.6%, 7 of 437). • Calcification distribution, morphology, increase in calcifications, and the presence of newly diagnosed breast cancer/high-risk lesion were associated with malignancy (all p < .05).

Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics.

OBJECTIVE: To evaluate how breast cancers are depicted by artificial intelligence-based computer-assisted diagnosis (AI-CAD) according to clinical, radiological, and pathological factors. MATERIALS AND METHODS: From January 2017 to December 2017, 896 patients diagnosed with 930 breast cancers were enrolled in this retrospective study. Commercial AI-CAD was applied to digital mammograms and abnormality scores were obtained. We evaluated the abnormality score according to clinical, radiological, and pathological characteristics. False-negative results were defined by abnormality scores less than 10. RESULTS: The median abnormality score of 930 breasts was 87.4 (range 0-99). The false-negative rate of AI-CAD was 19.4% (180/930). Cancers with an abnormality score of more than 90 showed a high proportion of palpable lesions, BI-RADS 4c and 5 lesions, cancers presenting as mass with or without microcalcifications and invasive cancers compared with low-scored cancers (all p < 0.001). False-negative cancers were more likely to develop in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers and DCIS compared to detected cancers. CONCLUSION: Breast cancers depicted with high abnormality scores by AI-CAD are associated with higher BI-RADS category, invasive pathology, and higher cancer stage. KEY POINTS: • High-scored cancers by AI-CAD included a high proportion of BI-RADS 4c and 5 lesions, masses with or without microcalcifications, and cancers with invasive pathology. • Among invasive cancers, cancers with higher T and N stage and HER2-enriched subtype were depicted with higher abnormality scores by AI-CAD. • Cancers missed by AI-CAD tended to be in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers by radiologists.

Mammographic Surveillance After Breast-Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection.

BACKGROUND. Postoperative mammograms present interpretive challenges due to postoperative distortion and hematomas. The application of digital breast tomosyn-thesis (DBT) and artificial intelligence-based computer-aided detection (AI-CAD) after breast-conserving therapy (BCT) has not been widely investigated. OBJECTIVE. The purpose of our study was to assess the impact of additional DBT or AI-CAD on recall rate and diagnostic performance in women undergoing mammographic surveillance after BCT. METHODS. This retrospective study included 314 women (mean age, 53.3 ± 10.6 [SD] years; four with bilateral breast cancer) who underwent BCT followed by DBT (mean interval from surgery to DBT, 15.2 ± 15.4 months). Three breast radiologists independently reviewed images in three sessions: digital mammography (DM), DM with DBT (DM plus DBT), and DM with AI-CAD (DM plus AI-CAD). Recall rates and diagnostic performance were compared between DM, DM plus DBT, and DM plus AI-CAD using the readers' mean results. RESULTS. Of the 314 women, six breast recurrences (three ipsilateral and three contralateral) had developed at the time of surveillance mammography. The ipsilateral breast recall rate was lower for DM plus AI-CAD (1.9%) than for DM (11.2%) or DM plus DBT (4.1%) (p < .001). The contralateral breast recall rate was significantly lower for DM plus AI-CAD (1.5%, p < .001) than for DM (6.6%) but for not DM plus DBT (2.7%, p = .08). In the ipsilateral breast, accuracy was higher for DM plus AI-CAD (97.0%) than for DM (88.5%) or DM plus DBT (94.8%) (p < .05); specificity was higher for DM plus AI-CAD (98.3%) than for DM (89.3%) or DM plus DBT (96.1%) (p < .05); sensitivity was significantly lower for DM plus AI-CAD (22.2%) than for DM (66.7%, p = .03) but not DM plus DBT (22.2%, p > .99). In the contralateral breast, accuracy was significantly higher for DM plus AI-CAD (97.1%) than for DM (92.5%, p < .001) but not DM plus DBT (96.1%, p = .25); specificity was significantly higher for DM plus AI-CAD (98.6%) than for DM (93.7%, p < .001) but not DM plus DBT (97.5%) (p = .09); sensitivity was not different between DM (33.3%), DM plus DBT (22.2%), and DM plus AI-CAD (11.1%) (p > .05). CONCLUSION. After BCT, adjunct DBT or AI-CAD reduced recall rates and improved accuracy in the ipsilateral and contralateral breasts compared with DM. In the ipsilateral breast, the addition of AI-CAD resulted in a lower recall rate and higher accuracy than the addition of DBT. CLINICAL IMPACT. AI-CAD may help address the challenges of interpreting post-BCT surveillance mammograms.

Application of Artificial Intelligence Computer-Assisted Diagnosis Originally Developed for Thyroid Nodules to Breast Lesions on Ultrasound.

As thyroid and breast cancer have several US findings in common, we applied an artificial intelligence computer-assisted diagnosis (AI-CAD) software originally developed for thyroid nodules to breast lesions on ultrasound (US) and evaluated its diagnostic performance. From January 2017 to December 2017, 1042 breast lesions (mean size 20.2 ± 11.8 mm) of 1001 patients (mean age 45.9 ± 12.9 years) who underwent US-guided core-needle biopsy were included. An AI-CAD software that was previously trained and validated with thyroid nodules using the convolutional neural network was applied to breast nodules. There were 665 benign breast lesions (63.0%) and 391 breast cancers (37.0%). The area under the receiver operating characteristic curve (AUROC) of AI-CAD to differentiate breast lesions was 0.678 (95% confidence interval: 0.649, 0.707). After fine-tuning AI-CAD with 1084 separate breast lesions, the diagnostic performance of AI-CAD markedly improved (AUC 0.841). This was significantly higher than that of radiologists when the cutoff category was BI-RADS 4a (AUC 0.621, P < 0.001), but lower when the cutoff category was BI-RADS 4b (AUC 0.908, P < 0.001). When applied to breast lesions, the diagnostic performance of an AI-CAD software that had been developed for differentiating malignant and benign thyroid nodules was not bad. However, an organ-specific approach guarantees better diagnostic performance despite the similar US features of thyroid and breast malignancies.

Outcomes Following Negative Screening MRI Results in Korean Women with a Personal History of Breast Cancer: Implications for the Next MRI Interval.

Background There are limited data on outcomes following screening breast MRI in women with a personal history of breast cancer (PHBC). Purpose To investigate outcomes and factors associated with subsequent cancers following a negative screening MRI study in women with a PHBC. Materials and Methods Consecutive women with a PHBC and a negative prevalence screening breast MRI result between August 2014 and December 2016 were retrospectively identified. Inclusion criteria were prevalence screening MRI performed as part of routine surveillance protocol (1-3 years after treatment) and follow-up data for at least 12 months. The incidence and characteristics of subsequent cancers were reviewed. Logistic regression analysis was used to investigate associations between clinical-pathologic characteristics and subsequent cancers. Performance metrics were compared among screening MRI, mammography, and US. Results A total of 993 women (mean age ± standard deviation, 53 years ± 10) were evaluated. Ten second in-breast cancers (ie, ipsilateral or contralateral) occurred at a median interval of 31.8 months (range, 13.3-44.8 months) after MRI, of which eight (80%) were ductal carcinoma in situ (DCIS) or node-negative T1 cancers. Only one node-negative T1mi (tumor ≤1 mm) second in-breast cancer visible on a mammogram was detected within 24 months of MRI. Of second in-breast cancers, 40% (four of 10) were detected only at subsequent screening MRI, which was performed a median of 30.5 months after negative prevalence screening MRI. Ten local-regional recurrences occurred at a median interval of 16.9 months (range, 6-35 months). Previous treatment for DCIS was associated with second in-breast cancers (odds ratio, 3.73; 95% CI: 1.04, 13.38; P = .04). In 1048 women who underwent prevalence screening MRI (including all Breast Imaging Reporting and Data System categories), MRI showed a lower abnormal interpretation rate and higher specificity than mammography or US (P < .001 for all). Conclusion After a negative screening MRI result, 90% of subsequent cancers were detected at intervals longer than 24 months and there was a low second in-breast cancer rate (1%). © RSNA, 2021 Supplemental material is available for this article. See also the editorial by Chang in this issue.

Added Value of MRI for Invasive Breast Cancer including the Entire Axilla for Evaluation of High-Level or Advanced Axillary Lymph Node Metastasis in the Post-ACOSOG Z0011 Trial Era.

Background In the post-American College of Surgeons Oncology Group Z0011 trial era, radiologists have increasingly focused on excluding high-level or advanced axillary lymph node metastasis (ALNM) by using an additional MRI scan positioned higher than lower axillae; however, the value of these additional scans remains undetermined. Purpose To evaluate whether a standard MRI protocol is sufficient to exclude high-level or advanced ALNM in breast cancer or additional MRI of entire axilla is needed. Materials and Methods This retrospective study evaluated women with invasive breast cancer who underwent breast MRI from April 2015 to December 2016. Some underwent neoadjuvant chemotherapy (NAC) and others underwent upfront surgery. Standard (routine axial scans including the lower axillae) and combined (routine axial scans plus additional scans including the entire axilla) MRI protocols were compared for high-level or advanced ALNM detection. Clinical-pathologic characteristics were analyzed. Uni- and multivariable logistic regression was performed to identify predictors of high-level or advanced ALNM. Results A total of 435 women (mean age ± standard deviation, 52 years ± 11) were evaluated (65 in the NAC group, 370 in the non-NAC group). With the standard MRI protocol, predictors of high-level ALNM were peritumoral edema (odds ratio [OR], 12.3; 95% CI: 3.9, 39.4; P < .001) and positive axilla (OR, 5.9; 95% CI: 2.0, 15.2; P < .001). Only three of 289 women with negative axillae without peritumoral edema had high-level ALNM. Predictors of advanced ALNM were positive axillae (OR, 8.9; 95% CI: 3.7, 21.5; P < .001) and peritumoral edema (OR, 2.8; 95% CI: 1.1, 6.9; P = .03). Only six of 310 women who had negative axillae without peritumoral edema had advanced ALNM. Conclusion The performance of standard MRI was satisfactory in excluding high-level and advanced axillary lymph node metastasis in most patients with breast cancer. However, the presence of peritumoral edema or positive axillae in the MRI findings emphasizes the benefits of a combined MRI protocol. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Abe in this issue.

Chronological Trends of Breast Ductal Carcinoma In Situ: Clinical, Radiologic, and Pathologic Perspectives.

BACKGROUND: Because no prior studies have evaluated the chronological trends of ductal carcinoma in situ (DCIS) despite the increasing number of surgeries performed for DCIS, this study analyzed how the clinical, radiologic, and pathologic characteristics of DCIS changed during a 10-year period. METHODS: Of 7123 patients who underwent primary breast cancer surgery at a single institution from 2006 to 2015, 792 patients with pure DCIS were included in this study. The chronological trends of age, symptoms, method for detecting either mammography or ultrasonography, tumor size, nuclear grade, comedonecrosis, and molecular markers were calculated using Poisson regression for all patients and asymptomatic patients. RESULTS: During 10 years, DCIS surgery rates significantly increased (p < 0.001). Despite the high percentage of DCIS detected on mammography, the detection rate for DCIS by mammography significantly decreased (97.3% in 2006 to 67.6% in 2015; p = 0.025), whereas the detection rate by ultrasound significantly increased (2.7% to 31.0%; p < 0.001). Conservation surgery rates (odds ratio [OR], 1.058), low-to-intermediate nuclear grade rates (OR, 1.069), and the absence of comedonecrosis (OR, 1.104) significantly increased over time (all p < 0.05). Estrogen receptor (ER) negativity (OR, 0.935) and human epidermal growth factor receptor 2 (HER2) positivity rates (OR, 0.953) significantly decreased (all p < 0.05). The same trends were observed for the 613 asymptomatic patients. CONCLUSION: The rate of DCIS detected on ultrasound only significantly increased during 10 years. Low-to-intermediate nuclear grade rates significantly increased, whereas ER negativity and HER2 positivity rates significantly decreased during the same period. These findings suggest that DCIS detected on screening ultrasound is less aggressive than DCIS detected on mammography.

Calcifications with suspicious morphology at mammography: should they all be considered with the same clinical significance?

OBJECTIVES: To evaluate the positive predictive values (PPVs) of calcifications with suspicious morphology by incorporating distribution and clinical factors in two separate cohorts to provide more practical guidance for management. METHODS: This retrospective study included 1076 consecutive women from two cohorts (cohort A, 556; cohort B, 520), with calcifications of suspicious morphology seen on mammography that were pathologically confirmed or followed with mammography. Reader-averaged PPVs of the calcifications were analyzed and compared by logistic regression using the generalized estimating equation. Multivariate logistic regression analysis was performed to evaluate independent factors associated with the PPVs of suspicious calcifications. RESULTS: Overall reader-averaged PPVs of suspicious calcifications were 16.8% and 15.2% in cohort A and B, respectively. Reader-averaged PPVs according to morphology in cohort A and B were as follows: amorphous 9.1%, 6.4%; coarse heterogeneous 16.1%, 22.1%; fine pleomorphic 78.8%, 44.7%; and fine linear branching 78.6%, 85.1%, respectively (p < 0.001). PPVs for diffuse amorphous combinations were 2.6% and 2.6%, and for regional amorphous calcifications, 3.6% and 3.1%, respectively. Among diffuse amorphous calcifications, the PPVs for women ≥ 50 years and women without a personal history of breast cancer ranged from 0.0 to 1.9%. CONCLUSIONS: Amorphous calcifications have lower reader-averaged PPVs compared to calcifications with other suspicious morphology, falling into the BI-RADS 4a assessment (PPV 2-10%). Amorphous calcifications with diffuse distributions detected in women > 50 years old and without a personal history of breast cancer have reader-averaged PPVs < 2.0%. Further prospective studies are necessary to confirm if these patients can be managed with imaging follow-up. KEY POINTS: • In two cohorts, reader-averaged positive predictive values (PPVs) for suspicious calcifications showed lower rates for amorphous calcifications. • In two separate cohorts, reader-averaged PPVs showed lower rates for diffuse amorphous calcifications, falling into the BI-RADS 4a assessment category (PPV 2-10%). • Diffuse amorphous calcifications detected in women > 50 years old and without a personal history of breast cancer have reader-averaged PPVs < 2.0%.

Comparison of diagnostic performance of the ACR and Kwak TIRADS applying the ACR TIRADS' size thresholds for FNA.

OBJECTIVE: To investigate the diagnostic performances and unnecessary fine-needle aspiration (FNA) rates of two point-scale based TIRADS and compare them with a modified version using the ACR TIRADS' size thresholds. METHODS: Our Institutional Review Board approved this retrospective study and waived the requirement for informed consent. A total of 2106 thyroid nodules 10 mm or larger in size in 2084 patients with definitive cytopathologic findings were included. Ultrasonography categories were assigned according to each guideline. We applied the ACR TIRADS' size thresholds for FNA to the Kwak TIRADS and defined it as the modified Kwak TIRADS (mKwak TIRADS). Diagnostic performances and unnecessary FNA rates were evaluated for both the original and modified guidelines. RESULTS: Of the original guidelines, the ACR TIRADS had higher specificity, accuracy, and area under the receiver operating characteristic curve (AUC) (63.1%, 68.9%, and 0.748, respectively). When the size threshold of the ACR TIRADS was applied to the Kwak TIRADS, the resultant mKwak TIRADS had higher specificity, accuracy, and AUC (64.7%, 70.3%, and 0.765, respectively) than the ACR TIRADS. The mKwak TIRADS also had a lower unnecessary FNA rate than the ACR TIRADS (54.8% and 56.4%, respectively). The false-negative rate of the Kwak TIRADS was the lowest (1.9%) among all TIRADS. CONCLUSION: The modified Kwak TIRADS incorporating the size thresholds of the ACR TIRADS showed higher diagnostic performance and a lower unnecessary FNA rate than the original point-scale based TIRADS. KEY POINTS: • Of the original guidelines, the ACR TIRADS had the highest specificity, accuracy, and area under the receiver operating characteristic curve (AUC) (63.1%, 68.9%, and 0.748, respectively). • When the size threshold of the ACR TIRADS was applied to the Kwak TIRADS, the resultant modified version of Kwak TIRADS had higher specificity, accuracy, and AUC (64.7%, 70.3%, and 0.765, respectively) than the ACR TIRADS. • The false-negative rate of the Kwak TIRADS was the lowest (1.9%) among all TIRADS.

Combining radiomics with ultrasound-based risk stratification systems for thyroid nodules: an approach for improving performance.

OBJECTIVES: To develop a radiomics score using ultrasound images to predict thyroid malignancy and to investigate its potential as a complementary tool to improve the performance of risk stratification systems. METHODS: We retrospectively included consecutive patients who underwent fine-needle aspiration (FNA) for thyroid nodules that were cytopathologically diagnosed as benign or malignant. Nodules were randomly assigned to a training and test set (8:2 ratio). A radiomics score was developed from the training set, and cutoff values based on the maximum Youden index (Rad_maxY) and for 5%, 10%, and 20% predicted malignancy risk (Rad_5%, Rad_10%, Rad_20%, respectively) were applied to the test set. The performances of the American College of Radiology (ACR) and the American Thyroid Association (ATA) guidelines were compared with the combined performances of the guidelines and radiomics score with interpretations from expert and nonexpert readers. RESULTS: A total of 1624 thyroid nodules from 1609 patients (mean age, 50.1 years [range, 18-90 years]) were included. The radiomics score yielded an AUC of 0.85 (95% CI: 0.83, 0.87) in the training set and 0.75 (95% CI: 0.69, 0.81) in the test set (Rad_maxY). When the radiomics score was combined with the ACR or ATA guidelines (Rad_5%), all readers showed increased specificity, accuracy, and PPV and decreased unnecessary FNA rates (all p < .05), with no difference in sensitivity (p > .05). CONCLUSION: Radiomics help predict thyroid malignancy and improve specificity, accuracy, PPV, and unnecessary FNA rate while maintaining the sensitivity of the ACR and ATA guidelines for both expert and nonexpert readers. KEY POINTS: • The radiomics score yielded an AUC of 0.85 and 0.75 in the training and test set, respectively. • For all readers, combining a 5% predicted malignancy risk cutoff for the radiomics score with the ACR and ATA guidelines significantly increased specificity, accuracy, and PPV and decreased unnecessary FNA rates, with no decrease in sensitivity. • Radiomics can help predict malignancy in thyroid nodules in combination with risk stratification systems, by improving specificity, accuracy, and PPV and unnecessary FNA rates while maintaining sensitivity for both expert and nonexpert readers.

Radiofrequency Irradiation Attenuates High-Mobility Group Box 1 and Toll-like Receptor Activation in Ultraviolet B-Induced Skin Inflammation.

Ultraviolet B (UVB) exposure activates various inflammatory molecules of keratinocytes in the epidermis layer. Such UVB-mediated skin inflammation leaves post-inflammatory hyperpigmentation (PIH). Reports show a close relationship between PIH and high-mobility group box 1 (HMGB1) and its receptors. General clinical treatments of PIH, such as oral medication and laser treatment, have reported side effects. Recent studies reported the effects of radiofrequency (RF) irradiation on restoring dermal collagen, modulating the dermal vasculature, and thickening the basement membrane. To validate how RF regulates the inflammatory molecules from UVB-irradiated keratinocytes, we used UVB-radiated keratinocytes and macrophages, as well as animal skin. In addition, we examined two cases of RF-irradiated skin inflammatory diseases. We validated the effects of RF irradiation on keratinocytes by measuring expression levels of HMGB1, Toll-like receptors (TLRs), and other inflammatory factors. The results show that the RF modulates UVB-radiated keratinocytes to secrete fewer inflammatory factors and also modulates the expression of macrophages from HMGB1, TLRs, and inflammatory factors. RF irradiation could alleviate inflammatory skin diseases in patients. RF irradiation can regulate the macrophage indirectly through modulating the keratinocyte and inflammatory molecules of macrophages reduced in vitro and in vivo. Although the study is limited by the low number of cases, it demonstrates that RF irradiation can regulate skin inflammation in patients.

Factors Predicting Breast Cancer Development in Women During Surveillance After Surgery for Atypical Ductal Hyperplasia of the Breast: Analysis of Clinical, Radiologic, and Histopathologic Features.

BACKGROUND: This study aimed to evaluate the risk of breast cancer development for women under surveillance after surgery for atypical ductal hyperplasia (ADH), as well as the clinical and pathologic factors associated with breast cancer development. METHODS: From November 2003 to December 2014, the study included 205 women (mean age, 47.1 ± 11.2 years; range 18-73 years) with a pathologic diagnosis of ADH at surgical excision who had preoperative mammography and ultrasonography (US) images and pathology slides available for review. The patients were classified into three groups according to the detection method as follows: negative group (with ADH occult on imaging), mammography group (with ADH detected on mammography), and US group (with ADH detected on US only). Clinical, radiologic, and histopathologic factors associated with breast cancer development after ADH surgery were evaluated. RESULTS: Breast cancer developed in 15 patients (7.3%) during surveillance after ADH surgery (follow-up period, 63.9 ± 40.8 months). Palpable lesions had significantly higher rates of breast cancer development after ADH surgery (26.7% vs 6.8%; P = 0.045). Breast cancer development after ADH surgery did not differ according to the detection method (P = 0.654). Palpability was significantly associated with breast cancer development during surveillance after ADH surgery (hazard ratio, 3.579; 95% confidence interval 1.048-12.220; P = 0.042). CONCLUSION: The breast cancer development rate for women under surveillance after ADH surgery was 7.3%. Palpability at the time of ADH diagnosis was significantly associated with breast cancer development.

Comparing recall rates following implementation of digital breast tomosynthesis to synthetic 2D images and digital mammography on women with breast-conserving surgery.

OBJECTIVES: To evaluate the recall rates of digital mammography (DM) and synthetic images after adding digital breast tomosynthesis (DBT) in patients with breast-conserving surgery. METHODS: From November 2015 to April 2017, 229 women with breast-conserving surgery due to breast cancer who underwent DBT after surgery were included (mean interval, 12.9 ± 1.4 months). All women underwent combo-mode DBT examinations including full-field DM, tomosynthesis, and reconstructed synthetic 2D images. Three board-certified breast radiologists reviewed the images sequentially: synthetic 2D+DBT and, 1 month later, DM and then DM+DBT. Recall rates and the abnormality type causing the recall were calculated and compared for each mammographic modality and breast density. RESULTS: Of the 229 patients included, 230 mammography images were reviewed. One patient (0.4%) developed locoregional recurrences during follow-up (mean duration, 25.8 ± 4.5 months). Recall rates for synthetic 2D+DBT were significantly lower than for DM alone (4.1% (2.6-6.2) vs. 11.6% (9.2-14.5), respectively; p < 0.001). Recall rates did not differ between synthetic 2D+DBT and DM+DBT (4.1% (2.6-6.2) vs. 2.9% (1.9-4.5), respectively; p = 0.234). Recall rates of synthetic 2D+DBT and DM+DBT were significantly lower than those of DM alone, regardless of mammographic breast density (all p < 0.05, respectively). CONCLUSION: Adding DBT to synthetic 2D images or DM shows significant reduction in recall rates compared with DM alone for women who undergo breast-conserving surgery for breast cancer, regardless of mammographic density. KEY POINTS: • Recall rates for synthetic 2D+DBT were significantly lower than those of DM alone (4.1% (2.6-6.2) vs. 11.6% (9.2-14.5), respectively; p < 0.001). • No significant differences were seen in recall rates between synthetic 2D+DBT and DM+DBT (4.1 (2.6-6.2) vs. 2.9 (1.9-4.5), respectively; p = 0.234). • Reader-averaged recall rates after adding DBT to synthetic 2D or DM were significantly lower than those of DM alone, regardless of mammographic breast density (all p < 0.05, respectively).