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.

Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis.

BACKGROUND: Artificial intelligence-based computer-assisted diagnosis (AI-CAD) is increasingly used for mammographic exams, and its role in mammographic density assessment should be evaluated. PURPOSE: To assess the inter-modality agreement between radiologists, automated volumetric density measurement program (Volpara), and AI-CAD system in breast density categorization using the Breast Imaging-Reporting and Data System (BI-RADS) density categories. MATERIAL AND METHODS: A retrospective review was conducted on 1015 screening digital mammograms that were performed in Asian female patients (mean age = 56 years ± 10 years) in our health examination center between December 2022 and January 2023. Four radiologists with two different levels of experience (expert and general radiologists) performed density assessments. Agreement between the radiologists, Volpara, and AI-CAD (Lunit INSIGHT MMG) was evaluated using weighted kappa statistics and matched rates. RESULTS: Inter-reader agreement between expert and general radiologists was substantial (k = 0.65) with a matched rate of 72.8%. The agreement was substantial between expert or general radiologists and Volpara (k = 0.64-0.67) with a matched rate of 72.0% but moderate between expert or general radiologists and AI-CAD (k = 0.45-0.58) with matched rates of 56.7%-67.0%. The agreement between Volpara and AI-CAD was moderate (k = 0.53) with a matched rate of 60.8%. CONCLUSION: The agreement in breast density categorization between radiologists and automated volumetric density measurement program (Volpara) was higher than the agreement between radiologists and AI-CAD (Lunit INSIGHT MMG).

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.

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.

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.

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.

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.

Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics.

BACKGROUND: To demonstrate the value of an artificial intelligence (AI) software in the detection of mammographically occult breast cancers and to determine the clinicopathologic patterns of the cancers additionally detected using the AI software. METHODS: By retrospectively reviewing our institutional database (January 2017-September 2019), we identified women with mammographically occult breast cancers and analyzed their mammography with an AI software that provided a malignancy score (range 0-100; > 10 considered as positive). The hot spots in the AI report were compared with the US and MRI findings to determine if the cancers were correctly marked by the AI software. The clinicopathologic characteristics of the AI-detected cancers were analyzed and compared with those of undetected cancers. RESULTS: Among the 1890 breast cancers, 6.8% (128/1890) were mammographically occult, among which 38.3% (49/128) had positive results in the AI analysis. Of them, 81.6% (40/49) were correctly marked by the AI software and determined as "AI-detected cancers." As such, 31.3% (40/128) of mammographically occult breast cancers could be identified by the AI software. Of the AI-detected cancers, 97.5% were found in heterogeneously or extremely dense breasts, 52.5% were asymptomatic, 86.5% were invasive, and 29.7% had axillary lymph node metastasis. Compared with undetected cancers, the AI-detected cancers were more likely to be found in younger patients (p < 0.001), undergo neoadjuvant chemotherapy as well as mastectomy rather than breast-conserving operation (both p < 0.001), and accompany axillary lymph node metastasis (p = 0.003). CONCLUSIONS: AI conferred an added value in the detection of mammographically occult breast cancers.

Prediction of Underestimation Using Contrast-Enhanced Spectral Mammography in Patients Diagnosed as Ductal Carcinoma In Situ on Preoperative Core Biopsy.

BACKGROUND: To assess the performance of contrast-enhanced spectral mammography (CESM) for the prediction of DCIS underestimation in comparison with mammography, breast US, and breast MRI. PATIENTS AND METHODS: We prospectively enrolled patients diagnosed with DCIS on preoperative core biopsy. Visibility, lesion type, and extent on each imaging modality, CESM gray values (CGV) were evaluated. Pathologic features of core biopsy and surgery were recorded. Chi-square or Fisher's exact test were used for univariate analysis. Multivariate logistic regression analysis was used to find independent predictors for DCIS underestimation and receiver operating characteristic (ROC) curve analysis was performed. RESULTS: A total of 113 lesions in 108 patients were analyzed (50 pure DCIS; 63 underestimated DCIS). Visibility on mammography, breast US, CESM, and breast MRI were 44%, 76%, 58%, and 80% for pure DCIS, and 73%, 81%, 86%, and 92% for underestimated DCIS. Tumor extents on surgical pathology of pure and underestimated DCIS were 1.11 ± 1.35 cm and 2.61 ± 2.09 cm. On multivariate analysis, nuclear grade and suspected invasion on core biopsy, visibility on mammography, and extent on breast MRI were independent factors for the model 1, whereas nuclear grade on core biopsy, extent on CESM, and mean CGV on MLO-recombined image were independent factors for the model 2. Area under ROC curve (AUC) was 0.843 for model 1 including breast MRI, whereas AUC was 0.823 for model 2 including CESM, which didn't show a significant difference (P = .968). CONCLUSION: For detecting underestimated DCIS, CESM was superior to mammography and breast US, and comparable to breast MRI.

Preoperative Breast MRI in Women 35 Years of Age and Younger with Breast Cancer: Benefits in Surgical Outcomes by Using Propensity Score Analysis.

Background The role of preoperative MRI in women 35 years of age or younger with breast cancer remains controversial. Purpose To determine the association between preoperative MRI and surgical outcomes in women aged 35 years or younger with breast cancer by using propensity score (PS) analysis to investigate the impact of preoperative MRI. Materials and Methods Women 35 years of age or younger diagnosed with breast cancer between 2007 and 2017 who had or had not undergone preoperative breast MRI were retrospectively identified. The MRI detection rate of additional suspicious lesions was analyzed, and changes in surgical management were recorded. Inverse probability weighting (IPW) and PS matching were used to adjust 19 variables and to create a balance between the two groups. Surgical outcomes were compared by using univariable logistic regression. Results Among 964 women (mean age ± standard deviation, 32 years ± 3), 665 (69%) had undergone preoperative MRI (MRI group; mean age, 32 years ± 3) and 299 (31%) had not (no-MRI group; mean age, 32 years ± 3). In the MRI group, additional suspicious lesions were found in 178 of the 665 women (27%), with 88 of those 178 women (49%) having malignant lesions. The surgical management was changed in 99 of the 665 women (15%) due to MRI findings, which was appropriate for 62 of those 99 women (63%). In the IPW analysis, the MRI group showed lower odds of repeat surgery (odds ratio [OR], 0.13; 95% CI: 0.07, 0.21; P < .001) and higher odds of initial mastectomy (OR, 1.62; 95% CI: 1.17, 2.25; P = .004). However, there was no difference in the overall mastectomy rate (OR, 1.24; 95% CI: 0.91, 1.68; P = .17) compared with the no-MRI group. These results were consistent when using the PS matching method. Conclusion Preoperative MRI in young women with breast cancer is useful for detecting additional malignancy and improving surgical outcomes by reducing the repeat surgery rate, with a similar likelihood of overall mastectomy. © RSNA, 2021 Online supplemental material is available for this article.

Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer.

BACKGROUND: Oncotype DX is a multigene assay used in breast cancer, and the result provided as a 'recurrence score (RS)' corresponds to the risk of a cancer recurrence and the chemotherapeutic benefit in estrogen receptor (ER)-positive human epidermal growth factor receptor (HER)2-negative invasive breast cancer. However, its accessibility is limited. PURPOSE: To evaluate whether magnetic resonance imaging (MRI) could be used to predict Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer. MATERIAL AND METHODS: We enrolled 473 patients with ER-positive HER2-negative invasive breast cancer who underwent a preoperative MRI and Oncotype DX assay between January 2015 and December 2018. The MRI was reviewed and associations between Oncotype DX RS values were evaluated. Logistic regression analysis was used to identify independent predictors of high and low RS. RESULTS: Of the 485 cancers, 288 (59.4%) had low (<18), 155 (31.9%) had intermediate (18-30), and 42 (8.7%) had high (≥31) RS. Multiple logistic regression analysis revealed that a round shape (odds ratio [OR] = 2.554, P = 0.089) and low proportion of washout component (OR = 1.011, P = 0.014) were associated with low RS and that heterogeneously dense (OR = 3.205, P = 0.007) or scattered fibroglandular (OR = 3.776, P = 0.005) breast tissue, a non-spiculated margin (OR = 5.435, P = 0.007), and low proportion of persistent component (OR = 1.012, P = 0.036) were associated with high RS. CONCLUSION: MRI features showed the potential for the discrimination of Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

Comparison of the Imaging Features of Lobular Carcinoma In Situ and Invasive Lobular Carcinoma of the Breast.

Purpose: To investigate the usefulness of imaging features for differentiating between small lobular carcinoma in situ (LCIS) and invasive lobular carcinoma (ILC). Materials and Methods: It included 52 female with LCISs (median 45 years, range 32-67 years) and 180 female with ILCs (median 49 years, range 36-75 years), with the longest diameter of ≤ 2 cm, who were evaluated between January 2012 and December 2016. All the female underwent mammography and ultrasonography. Twenty female with LCIS and 150 female with ILC underwent MRI. The clinical and imaging features were compared, and multivariate analysis was performed to identify the independent predictors of LCIS. Female with LCIS were also subgrouped by lesion size and compared with the female with ILC. Results: Multivariate analysis showed that younger age [odds ratio (OR) = 1.100], smaller lesion size (OR = 1.103), oval or round shape (OR = 4.098), parallel orientation (OR = 5.464), and isoechotexture (OR = 3.360) were significant independent factors predictive of LCIS. The area under the receiver operating characteristic curve for distinguishing LCIS from ILC was 0.904 (95% confidence interval, 0.857-0.951). Subgroup analysis showed that benign features were more prevalent in female with smaller LCISs (≤ 1 cm) than in those with ILC. Conclusion: Small LCISs tend to demonstrate more benign features than small ILCs. Several imaging features are independently predictive of LCIS.

Association of mammography and ultrasound features with MammaPrint in patients with estrogen receptor-positive, HER2-negative, node-positive invasive breast cancer.

BACKGROUND: MammaPrint is a 70-gene signature microarray assay that predicts the likelihood of recurrence of breast cancer and chemotherapeutic benefits. PURPOSE: To investigate the association between mammography and ultrasound (US) features and MammaPrint results in patients with estrogen receptor (ER)-positive, HER2-negative, node-positive invasive breast cancer, and to identify the predictive factors for high risk of recurrence. MATERIAL AND METHODS: This retrospective study included 251 patients with ER-positive, HER2-negative, 1-3 node-positive invasive breast cancer. Mammography and US findings were reviewed according to the BI-RADS criteria. The association between MammaPrint results and the clinicopathological and imaging features was evaluated. Logistic regression analysis was performed to identify independent predictors for high risk of recurrence. RESULTS: Of the patients, 143 (57.0%) and 108 (43.0%) had low and high risks for recurrence on MammaPrint, respectively. Young age (odds ratio [OR] 1.08; 95% confidence interval (CI) 1.04-1.12; P<0.001), posterior enhancement on US (OR 2.45; 95% CI 1.16-5.20; P = 0.019), absence of posterior shadowing on US (OR 3.19; 95% CI 1.17-8.62; P = 0.023), high histologic grade (OR 113.36; 95% CI 6.79-1893.53; P = 0.001), and high Ki-67 level (OR 4.90; 95% CI 2.62-9.17; P<0.001) were independently associated with high risk of recurrence on multivariate logistic regression analysis. CONCLUSION: Posterior features in US may predict a high risk of recurrence in patients with ER-positive, HER2-negative, node-positive invasive breast cancer, which may be useful in enhancing the diagnostic value of MammaPrint and aid in the decision-making process regarding treatment.