Optimizing angular range in digital breast tomosynthesis: A phantom study investigating lesion detection across varied breast density and thickness.

PURPOSE: To determine the optimal angular range (AR) for digital breast tomosynthesis (DBT) systems that provides highest lesion visibility across various breast densities and thicknesses. METHOD: A modular DBT phantom, consisting of tissue-equivalent adipose and glandular modules, along with a module embedded with test objects (speckles, masses, fibers), was used to create combinations simulating different breast thicknesses, densities, and lesion locations. A prototype DBT system operated at four ARs (AR±7.5°, AR±12.5°, AR±19°, and AR±25°) to acquire 11 projection images for each combination, with separate fixed doses for thin and thick combinations. Three blinded radiologists independently assessed lesion visibility in reconstructed images; assessments were averaged and compared using linear mixed models. RESULTS: Speckle visibility was highest with AR±7.5° or AR±12.5°, decreasing with wider ARs in all density and thickness combinations. The difference between AR±7.5° and AR±12.5° was not statistically significant, except for the tube-side speckles in thin-fatty combinations (5.83 [AR±7.5°] vs. 5.39 [AR±12.5°], P = 0.019). Mass visibility was not affected by AR in thick combinations, while AR±12.5° exhibited the highest mass visibility for both thin-fatty and thin-dense combinations (P = 0.032 and 0.007, respectively). Different ARs provided highest fiber visibility for different combinations; however, AR±12.5° consistently provided highest or comparable visibility. AR±12.5° showed highest overall lesion visibility for all density and thickness combinations. CONCLUSIONS: AR±12.5° exhibited the highest overall lesion visibility across various phantom thicknesses and densities using a projection number of 11.

Statistical Methods for Comparing Predictive Values in Medical Diagnosis.

Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.

A predictive model using MRI and clinicopathologic features for breast cancer recurrence in young women treated with upfront surgery.

OBJECTIVES: To identify preoperative breast MR imaging and clinicopathological variables related to recurrence and develop a risk prediction model for recurrence in young women with breast cancer treated with upfront surgery. METHODS: This retrospective study analyzed 438 consecutive women with breast cancer aged 35 years or younger between January 2007 and December 2016. Breast MR images before surgery were independently reviewed by breast radiologists blinded to patient outcomes. The clinicopathological data including patient demographics, clinical features, and tumor characteristics were reviewed. Univariate and multivariate logistic regression analyses were used to identify the independent factors associated with recurrence. The risk prediction model for recurrence was developed, and the discrimination and calibration abilities were assessed. RESULTS: Of 438 patients, 95 (21.7%) developed recurrence after a median follow-up of 65 months. Tumor size at MR imaging (HR = 1.158, p = 0.006), multifocal or multicentric disease (HR = 1.676, p = 0.017), and peritumoral edema on T2WI (HR = 2.166, p = 0.001) were identified as independent predictors of recurrence, while adjuvant endocrine therapy (HR = 0.624, p = 0.035) was inversely associated with recurrence. The prediction model showed good discrimination ability in predicting 5-year recurrence (C index, 0.707 in the development cohort; 0.686 in the validation cohort) and overall recurrence (C index, 0.699 in the development cohort; 0.678 in the validation cohort). The calibration plot demonstrated an excellent correlation (concordance correlation coefficient, 0.903). CONCLUSION: A prediction model based on breast MR imaging and clinicopathological features showed good discrimination to predict recurrence in young women with breast cancer treated with upfront surgery, which could contribute to individualized risk stratification. CLINICAL RELEVANCE STATEMENT: Our prediction model, incorporating preoperative breast MR imaging and clinicopathological features, predicts recurrence in young women with breast cancer undergoing upfront surgery, facilitating personalized risk stratification and informing tailored management strategies. KEY POINTS: Younger women with breast cancer have worse outcomes than those diagnosed at more typical ages. The described prediction model showed good discrimination performance in predicting 5-year and overall recurrence. Incorporating better risk stratification tools in this population may help improve outcomes.

Background Breast Parenchymal Signal During Menstrual Cycle on Diffusion-Weighted MRI: A Prospective Study in Healthy Premenopausal Women.

OBJECTIVE: To prospectively investigate the influence of the menstrual cycle on the background parenchymal signal (BPS) and apparent diffusion coefficient (ADC) of the breast on diffusion-weighted MRI (DW-MRI) in healthy premenopausal women. MATERIALS AND METHODS: Seven healthy premenopausal women (median age, 37 years; range, 33-49 years) with regular menstrual cycles participated in this study. DW-MRI was performed during each of the four phases of the menstrual cycle (four examinations in total). Three radiologists independently assessed the BPS visual grade on images with b-values of 800 sec/mm² (b800), 1200 sec/mm² (b1200), and a synthetic 1500 sec/mm² (sb1500). Additionally, one radiologist conducted a quantitative analysis to measure the BPS volume (%) and ADC values of the BPS (ADCBPS) and fibroglandular tissue (ADCFGT). Changes in the visual grade, BPS volume (%), ADCBPS, and ADCFGT during the menstrual cycle were descriptively analyzed. RESULTS: The visual grade of BPS in seven women varied from mild to marked on b800 and from minimal to moderate on b1200 and sb1500. As the b-value increased, the visual grade of BPS decreased. On b800 and sb1500, two of the seven volunteers showed the highest visual grade in the early follicular phase (EFP). On b1200, three of the seven volunteers showed the highest visual grades in EFP. The BPS volume (%) on b800 and b1200 showed the highest value in three of the six volunteers with dense breasts in EFP. Three of the seven volunteers showed the lowest ADCBPS in the EFP. Four of the seven volunteers showed the highest ADCBPS in the early luteal phase (ELP) and the lowest ADCFGT in the late follicular phase (LFP). CONCLUSION: Most volunteers did not exhibit specific BPS patterns during their menstrual cycles. However, the highest BPS and lowest ADCBPS were more frequently observed in EFP than in the other menstrual cycle phases, whereas the highest ADCBPS was more common in ELP. The lowest ADCFGT was more frequent in LFP.

Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations.

OBJECTIVES: To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. METHODS: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. RESULTS: The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). CONCLUSION: The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. CLINICAL RELEVANCE STATEMENT: The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. KEY POINTS: • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

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

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

Diagnostic yield of contrast-enhanced abdominal staging CT in patients with initially diagnosed breast cancer.

PURPOSE: To estimate the diagnostic yield (DY) of abdominal staging CT for detecting breast cancer liver metastasis (BCLM) in patients with initially diagnosed breast cancer and to determine the indications for abdominal staging CT. METHODS: Patients with newly diagnosed breast cancer who underwent abdominal CT as an initial staging work-up between January 2019 and December 2020 were retrospectively analyzed. DY was calculated and analyzed according to patient age, type of treatments, histologic type, histologic grade, lymphovascular invasion, Ki-67 status, hormone receptor status, subtype, and the American Joint Committee on Cancer anatomical staging. RESULTS: A total of 2056 patients (mean age, 51 ± 11 years) were included. The DY of abdominal staging CT for detecting BCLM was 1.1 % (22 of 2056). DY was significantly higher in stage III than in stage I or II cancers (3.9 % [18 of 467] vs. 0 % [0 of 412] or 0.4 % [4 of 1158], respectively, p < .001), and in human epidermal growth factor receptor-2 (HER2)-enriched cancers than in luminal or triple negative cancers (2.9 % [16 of 560] vs. 0.4 % [4 of 1090] or 0.5 % [2 of 406], respectively, p < .001). CONCLUSIONS: The DY of abdominal staging CT for detecting BCLM was low among all patients with initially diagnosed breast cancer. However, although abdominal staging CT for detecting BCLM is probably unnecessary in all patients, it can be clinically useful in patients with stage III or human epidermal growth factor receptor-2-enriched breast cancers.

Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring.

DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.

Prediction of the MammaPrint Risk Group Using MRI Features in Women With Estrogen Receptor-Positive, HER2-Negative, and 1 to 3 Node-Positive Invasive Breast Cancer.

BACKGROUND: MammaPrint assigns chemotherapeutic benefits to patients with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and 1 to 3 node-positive invasive breast cancer. However, its cost and time-consuming nature limit its use in certain clinical settings. We aimed to develop and validate the prediction models for the low MammaPrint risk group using clinicopathologic and MRI features. PATIENTS AND METHODS: Overall, 352 women with ER-positive, HER2-negative, and 1 to 3 node-positive invasive breast cancer were retrospectively reviewed and assigned to development (n = 235) and validation sets (n = 117). Univariate and multivariate analyses identified features associated with the low MammaPrint risk group. The area under the receiver operating characteristic curves (AUROCs) of models based on clinicopathologic, MRI, and combined features were evaluated. RESULTS: Development set multivariate analysis showed that clinicopathologic features including low histologic grade (odds ratio [OR], 5.29; P = .02), progesterone receptor-positivity (OR, 3.23; P = .01), and low Ki-67 (OR, 6.05; P < .001) and MRI features, including peritumoral edema absence (OR, 2.24; P = .04) and a high proportion of persistent components (OR, 1.15; P = .004) were significantly associated with the low MammaPrint risk group. The AUROCs of models based on clinicopathologic, MRI, and combined features were 0.77, 0.64, and 0.80 in the development and 0.66, 0.60, and 0.70 in the validation sets, respectively. CONCLUSION: The combined model incorporating clinicopathologic and MRI features showed potential in predicting the low MammaPrint risk group, and may support decision-making in clinical settings with limited access to MammaPrint.

Assessing internal mammary lymph node metastasis by breast magnetic resonance imaging in breast cancer.

The internal mammary lymph nodes (IMLNs) are a main pathway of metastasis in breast cancer, and breast magnetic resonance imaging (MRI) plays an important role in staging that disease. We investigated the MRI parameters that can predict metastatic IMLNs and evaluated their diagnostic performance by comparing the breast MRI findings for metastatic and benign IMLNs. From January 2016 to December 2020, 474 cases of enlarged IMLNs on breast MRI were identified. By cytopathology or integrated positron emission tomography/computed tomography (PET/CT), 168 IMLNs were confirmed as metastatic, and 81 were confirmed as benign. Breast MRIs were reviewed by 2 radiologists, and various parameters (node axes, fatty hilum, necrosis, margin characteristics, restricted diffusion, and involved levels; primary tumor location and skin involvement) were assessed. Independent t-tests, receiver operating characteristic (ROC) curve analyses, chi-square tests, and Fisher exact tests were performed to compare and evaluate the diagnostic accuracy of the imaging findings. Significant differences in the breast MRI findings for the short and long axes, fatty hilum, necrosis, margin characteristics, diffusion restriction, and tumor location were observed between benign and metastatic IMLNs. Compared with the long axis and the ratio of the axes, the short axis had the best diagnostic value (higher area under the ROC curve) for predicting metastatic IMLNs. In conclusion, breast MRI parameters such as short axis, presence of fatty hilum, necrosis, margin characteristics, and diffusion restriction can be used to evaluate and differentiate benign from metastatic IMLNs, offering valuable insights to improve diagnosis and treatment planning in breast cancer.

Improving mammography interpretation for both novice and experienced readers: a comparative study of two commercial artificial intelligence software.

OBJECTIVES: To evaluate the improvement of mammography interpretation for novice and experienced radiologists assisted by two commercial AI software. METHODS: We compared the performance of two AI software (AI-1 and AI-2) in two experienced and two novice readers for 200 mammographic examinations (80 cancer cases). Two reading sessions were conducted within 4 weeks. The readers rated the likelihood of malignancy (range, 1-7) and the percentage probability of malignancy (range, 0-100%), with and without AI assistance. Differences in AUROC, sensitivity, and specificity were analyzed. RESULTS: Mean AUROC increased in both novice (0.86 to 0.90 with AI-1 [p = 0.005]; 0.91 with AI-2 [p < 0.001]) and experienced readers (0.87 to 0.92 with AI-1 [p < 0.001]; 0.90 with AI-2 [p = 0.004]). Sensitivities increased from 81.3 to 88.8% with AI-1 (p = 0.027) and to 91.3% with AI-2 (p = 0.005) in novice readers, and from 81.9 to 90.6% with AI-1 (p = 0.001) and to 87.5% with AI-2 (p = 0.016) in experienced readers. Specificity did not decrease significantly in both novice (p > 0.999, both) and experienced readers (p > 0.999 with AI-1 and 0.282 with AI-2). There was no significant difference in the performance change depending on the type of AI software (p > 0.999). CONCLUSION: Commercial AI software improved the diagnostic performance of both novice and experienced readers. The type of AI software used did not significantly impact performance changes. Further validation with a larger number of cases and readers is needed. CLINICAL RELEVANCE STATEMENT: Commercial AI software effectively aided mammography interpretation irrespective of the experience level of human readers. KEY POINTS: • Mammography interpretation remains challenging and is subject to a wide range of interobserver variability. • In this multi-reader study, two commercial AI software improved the sensitivity of mammography interpretation by both novice and experienced readers. The type of AI software used did not significantly impact performance changes. • Commercial AI software may effectively support mammography interpretation irrespective of the experience level of human readers.

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

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

Correlation of shear-wave elastography parameters with the molecular subtype and axillary lymph node status in breast cancer.

PURPOSE: To examine correlations between shear-wave elastography (SWE) parameters with molecular subtype and axillary lymph node (LN) status of breast cancer. METHODS: We retrospectively analyzed 545 consecutive women (mean age, 52.7 ± 10.7 years; range, 26-83) with breast cancer who underwent preoperative breast ultrasound with SWE between December 2019 and January 2021. SWE parameters (Emax, Emean, and Eratio) and the histopathologic information from surgical specimens including histologic type, histologic grade, size of invasive cancer, hormone receptor and HER2 status, Ki-67 proliferation index, and axillary LN status were analyzed. The relationships between SWE parameters and histopathologic findings were analyzed using an independent sample t-test, one-way ANOVA test with Tukey's post hoc test, and logistic regression analyses. RESULTS: Higher stiffness values of SWE were associated with larger lesion size (>20 mm) on ultrasound, high histologic grade, larger invasive cancer size (>20 mm), high Ki-67, and axillary LN metastasis. Emax and Emean were the lowest in the luminal A-like subtype, and all three parameters were the highest in the triple-negative subtype. Lower value of Emax was independently associated with the luminal A-like subtype (P = 0.04). Higher value of Emean was independently associated with axillary LN metastasis for tumors ≤ 20 mm (P = 0.03). CONCLUSION: Increases in the tumor stiffness values on SWE were significantly associated with aggressive histopathologic features of breast cancer. Lower stiffness values were associated with the luminal A-like subtype, and tumors with higher stiffness values were associated with axillary LN metastasis in small breast cancers.

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

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

Effects of Preoperative Breast MRI on Breast Cancer Survival Outcomes in Women Aged 35 Years and Younger.

Background The impact of preoperative breast MRI on the long-term outcomes in patients with breast cancer who are 35 years and younger has not been established. Purpose To evaluate the impact of preoperative breast MRI on recurrence-free survival (RFS) and overall survival (OS) in women with breast cancer who are 35 years and younger by using propensity score matching. Materials and Methods A total of 708 women who were 35 years and younger (mean age, 32 years ± 3 [SD]) and diagnosed with breast cancer from 2007 to 2016 were retrospectively identified. Patients who underwent preoperative MRI (MRI group) were matched with those who did not (no MRI group) according to 23 patient and tumor characteristics. RFS and OS were compared using the Kaplan-Meier method. Cox proportional hazards regression analysis was used to estimate the hazard ratios (HRs). Results Of 708 women, 125 patient pairs were matched. In the MRI group versus the no MRI group, the mean follow-up time was 82 months ± 32 versus 106 months ± 42, and the rates of total recurrence and death were 22% (104 of 478 patients) versus 29% (66 of 230 patients) and 5% (25 of 478 patients) versus 12% (28 of 230 patients), respectively. The time to recurrence was 44 months ± 33 in the MRI group and 56 months ± 42 in the no MRI group. After propensity score matching, the MRI and no MRI groups did not show significant differences in total recurrence (HR, 1.0; P = .99), local-regional recurrence (HR, 1.3; P = .42), contralateral breast recurrence (HR, 0.7; P = .39), or distant recurrence (HR, 0.9; P = .79). The MRI group showed a tendency toward better OS, but this was not statistically significant (HR, 0.47; P = .07). In the entire unmatched cohort, MRI was not an independent significant factor for predicting RFS or OS. Conclusion Preoperative breast MRI was not a significant prognostic factor for recurrence-free survival in women 35 years and younger with breast cancer. A tendency toward better overall survival was observed in the MRI group, but this was not significant. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kim and Moy in this issue.

Outcomes and imaging features of microinvasive carcinoma and ductal carcinoma in situ: Matched cohort study.

INTRODUCTION: The purpose of this study is to investigate the differences in clinical outcomes between microinvasive carcinoma (mIC) and ductal carcinoma in situ (DCIS) and compare the imaging features of both using mammography, US and MRI. MATERIALS AND METHODS: This retrospective study was approved by our institutional review board. Between January 2011 and December 2013, 516 women with mIC or DCIS confirmed by surgery were included. Patients were matched with propensity score matching to compare recurrence-free survival (RFS). RFS was compared using a Cox proportional hazards model. Imaging features were also compared between the two groups. RESULTS: Among 516 women, 219 mIC and 297 DCIS tumors were identified. After matching, 132 women were allocated to each group. The mean follow-up duration was 80.2 months. In the matched cohort, no statistically significant association was observed between the DCIS and mIC groups in terms of total recurrence (hazard ratio [HR]: 1.7; 95% confidence interval [CI]: 0.8-4.0; P = 0.19), local-regional recurrence (HR: 3.4; 95% CI: 0.9-12.3, P = 0.07), or contralateral recurrence (HR: 0.9; 95% CI: 0.3-2.8, P = 0.89). Non-mass lesions at US (P = 0.004), moderate or marked background parenchymal enhancement (P = 0.04), and higher peak enhancement (P = 0.02) at MRI were more commonly seen in the mIC group than in the DCIS group. CONCLUSION: Microinvasive carcinomas are distinct from DCIS in terms of imaging features, but no statistically significant association in recurrence survival.

The Accuracy of Mammography, Ultrasound, and Magnetic Resonance Imaging For the Measurement of Invasive Breast Cancer With Extensive Intraductal Components.

BACKGROUND: The precise preoperative evaluation of radiologic tumor size with extensive intraductal component (EIC) is important. This study compared the accuracy of mammography, ultrasound (US), and magnetic resonance imaging (MRI) to measure invasive breast cancer with EIC. METHODS: Between 2007 and 2012, we collected data from 6816 patients who underwent surgery for invasive breast cancer at our institution. We reviewed the postoperative surgical reports of the tumors, in which the invasive tumor size and EIC were measured separately. Finally, we included 370 women who underwent preoperative mammography, US, and MRI. Each modality was retrospectively reviewed to measure the size of invasive breast cancer with EIC. The reference standard was surgical pathologic size and the accuracies of the image were evaluated. RESULTS: Spearman's correlation coefficient for the size of invasive cancer with EIC was good between MRI (r = 0.741) and pathology, and moderate between mammography (r = 0.661) or US (r = 0.514) and pathology. Both mass and nonmass lesions showed good correlations (intraclass correlation coefficient [ICC] = 0.672 and 0.612, respectively) in MRI. Furthermore, the subgroup of tumors without microcalcifications showed a higher correlation with MRI (ICC = 0.796) than with mammography (ICC = 0.620). However, the subgroup with microcalcifications showed a good correlation with mammography (ICC = 0.702) compared to MRI (ICC = 0.680) and US (ICC = 0.532). CONCLUSION: The lesion on mammography, US, and MRI reflected preoperative size of invasive cancer with EIC. MRI shows a higher correlation than mammography and US. However, cancer with calcifications of mammography shows a more accurate size than MRI or US.

[Patterns in the Use and Perception of Digital Breast Tomosynthesis: A Survey of Korean Breast Radiologists]. / 디지털 ìœ ë°© í† ëª¨ì‹ í Œì‹œìŠ¤ì— 대한 국내 사용 현황과 인식에 관한 설문조사 연구.

Purpose: To evaluate the pattern of use and the perception of digital breast tomosynthesis (DBT) among Korean breast radiologists. Materials and Methods: From March 22 to 29, 2021, an online survey comprising 27 questions was sent to members of the Korean Society of Breast Imaging. Questions related to practice characteristics, utilization and perception of DBT, and research interests. Results were analyzed based on factors using logistic regression. Results: Overall, 120 of 257 members responded to the survey (response rate, 46.7%), 67 (55.8%) of whom reported using DBT. The overall satisfaction with DBT was 3.31 (1-5 scale). The most-cited DBT advantages were decreased recall rate (55.8%), increased lesion conspicuity (48.3%), and increased cancer detection (45.8%). The most-cited DBT disadvantages were extra cost for patients (46.7%), insufficient calcification characterization (43.3%), insufficient improvement in diagnostic performance (39.2%), and radiation dose (35.8%). Radiologists reported increased storage requirements and interpretation time for barriers to implementing DBT. Conclusion: Further improvement of DBT techniques reflecting feedback from the user's perspective will help increase the acceptance of DBT in Korea.

Features of breast cancer initially assessed as probably benign on ultrasound: A retrospective study.

We evaluated the features of breast cancers initially assessed as probably benign at ultrasound (US). Of the 7098 patients who underwent breast cancer surgery at our institution between 2014 and 2016, 179 lesions in 178 patients who had both a prior US with Breast Imaging Reporting and Data System (BI-RADS) category 3 assessment and a recent US with a diagnosis of breast cancer were enrolled. Prior and recent US findings and category were retrospectively reassessed in line with the BI-RADS Atlas and analyzed. Of the 179 BI-RADS 3 lesions, 105 (59%) were retrospectively reassessed to category 4 and 74 (41%) retained category 3. Noncircumscribed margin, irregular shape, posterior enhancement, and nonparallel orientation were more frequently observed in the reassessment category 4 group than in the reassessment category 3 group (94% vs 43%, 81% vs 19%, 16% vs 4%, 14% vs 0%, respectively). The recent US revealed that 150 of the 179 lesions (84%) had > 20% size increase, and 121 (68%) showed morphologic changes. Margin was the most frequently observed morphologic feature to change (41%, 73/179). Care should be taken to look for subtle but suspicious US features and changes in mass, especially of margin, for early diagnosis of breast cancer.

Corrigendum: Diffusion-Weighted Magnetic Resonance Imaging for Breast Cancer Screening in High-Risk Women: Design and Imaging Protocol of a Prospective Multicenter Study in Korea.

This corrects the article on p. 218 in vol. 24, PMID: 33913277.