Shear-wave elastography-based nomograms predicting 21-gene recurrence score for adjuvant chemotherapy decisions in patients with breast cancer.

PURPOSE: To develop and validate nomograms based on shear-wave elastography (SWE) combined with clinicopathologic features for predicting Oncotype DX recurrence score (RS) for use with adjuvant systemic therapy guidelines. METHODS: In a retrospective study, patients with breast cancer who underwent definitive surgery of the breast between August 2011 and December 2019 were eligible for this study. Those with surgery between August 2011 and March 2019 were assigned to a development set and the rest were assigned to an independent validation set. Clinicopathologic features and SWE elasticity indices were assessed with logistic regression to develop nomograms for predicting RS ≥ 16 and ≥ 26. Analysis of the area under the receiver operating characteristic curve (AUROC) was used to assess the performance of the nomograms. RESULTS: Of a total 381 women (mean age, 51 ± 9 years), 286 (mean age, 51 ± 9 years) were in the development set and 95 (mean age, 51 ± 9 years) in the validation set. All SWE elasticity indices were independently associated with each RS cutoff (odds ratio, 1.006-1.039 for RS ≥ 16; odds ratio, 1.008-1.076 for RS ≥ 26). Nomograms based on SWE combined with clinicopathologic features were developed and validated for RS ≥ 16 (mean elasticity [AUROC, 0.74; 95% CI: 0.68, 0.80] and maximum elasticity [AUROC, 0.74; 95% CI: 0.69, 0.80]) and for RS ≥ 26 (mean elasticity [AUROC, 0.81; 95% CI: 0.73, 0.89], maximum elasticity [AUROC, 0.82; 95% CI: 0.74, 0.89], and elasticity ratio [AUROC, 0.86; 95% CI: 0.80, 0.93]). CONCLUSION: Nomograms based on SWE can predict Oncotype DX RS for use in adjuvant systemic therapy decisions.

Added value of abbreviated breast magnetic resonance imaging for assessing suspicious microcalcification on screening mammography-a prospective study.

OBJECTIVES: To investigate the added diagnostic value of abbreviated breast magnetic resonance imaging (MRI) for suspicious microcalcifications on screening mammography. METHODS: This prospective study included 80 patients with suspicious calcifications on screening mammography who underwent abbreviated MRI before undergoing breast biopsy between August 2017 and September 2020. The abbreviated protocol included one pre-contrast and the first post-contrast T1-weighted series. MRI examinations were interpreted as either positive or negative based on the visibility of any significant enhancement. The positive predictive value (PPV) was compared before and after the MRI. RESULTS: Of the 80 suspicious microcalcifications, 33.8% (27/80) were malignant and 66.2% (53/80) were false positives. Abbreviated MRI revealed 33 positive enhancement lesions, and 25 and two lesions showed true-positive and false-negative findings, respectively. Abbreviated MRI increased PPV from 33.8 (27 of 80 cases; 95% CI: 26.2%, 40.8%) to 75.8% (25 of 33 cases; 95% CI: 62.1%, 85.7%). A total of 85% (45 of 53) false-positive diagnoses were reduced after abbreviated MRI assessment. CONCLUSIONS: Abbreviated MRI added significant diagnostic value in patients with suspicious microcalcifications on screening mammography, as demonstrated by a significant increase in PPV with a potential reduction in unnecessary biopsy. KEY POINTS: • Abbreviated breast magnetic resonance imaging increased the positive predictive value of suspicious microcalcifications on screening mammography from 33.8 (27/80 cases) to 75.8% (25/33 cases) (p < .01). • Abbreviated magnetic resonance imaging helped avoid unnecessary benign biopsies in 85% (45/53 cases) of lesions without missing invasive cancer.

A convolutional deep learning model for improving mammographic breast-microcalcification diagnosis.

This study aimed to assess the diagnostic performance of deep convolutional neural networks (DCNNs) in classifying breast microcalcification in screening mammograms. To this end, 1579 mammographic images were collected retrospectively from patients exhibiting suspicious microcalcification in screening mammograms between July 2007 and December 2019. Five pre-trained DCNN models and an ensemble model were used to classify the microcalcifications as either malignant or benign. Approximately one million images from the ImageNet database had been used to train the five DCNN models. Herein, 1121 mammographic images were used for individual model fine-tuning, 198 for validation, and 260 for testing. Gradient-weighted class activation mapping (Grad-CAM) was used to confirm the validity of the DCNN models in highlighting the microcalcification regions most critical for determining the final class. The ensemble model yielded the best AUC (0.856). The DenseNet-201 model achieved the best sensitivity (82.47%) and negative predictive value (NPV; 86.92%). The ResNet-101 model yielded the best accuracy (81.54%), specificity (91.41%), and positive predictive value (PPV; 81.82%). The high PPV and specificity achieved by the ResNet-101 model, in particular, demonstrated the model effectiveness in microcalcification diagnosis, which, in turn, may considerably help reduce unnecessary biopsies.

Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy.

OBJECTIVES: To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC). METHODS: This retrospective study included 130 women who received NAC and underwent subsequent surgery for breast cancer between January 2012 and August 2017. We assessed common features, including standard morphologic MRI features and clinicopathologic features. We used a  commercial software and analyzed texture features from pretreatment and midtreatment MRI. A random forest (RF) method was performed to build a model for predicting recurrence. The diagnostic performance of this model for predicting recurrence was assessed and compared with those of five other machine learning classifiers using the Wald test. RESULTS: Of the 130 women, 21 (16.2%) developed recurrence at a median follow-up of 35.4 months. The RF classifier with common features including clinicopathologic and morphologic MRI features showed the lowest diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.83). The texture analysis with the RF method showed the highest diagnostic performances for pretreatment T2-weighted images and midtreatment DWI and ADC maps showed better diagnostic performance than that of an analysis of common features (AUC, 0.94 vs. 0.83, p < 0.05). The RF model based on all sequences showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers. CONCLUSIONS: Texture analysis using an RF model for pretreatment and midtreatment MRI may provide valuable prognostic information for predicting recurrence in patients with breast cancer treated with NAC and surgery. KEY POINTS: • RF model-based texture analysis showed a superior diagnostic performance than traditional MRI and clinicopathologic features (AUC, 0.94 vs.0.83, p < 0.05) for predicting recurrence in breast cancer after NAC. • Texture analysis using RF classifier showed the highest diagnostic performances (AUC, 0.94) for pretreatment T2-weighted images and midtreatment DWI and ADC maps. • RF model showed a better diagnostic performance for predicting recurrence than did the five other machine learning classifiers.

Preoperative Nodal US Features for Predicting Recurrence in N1b Papillary Thyroid Carcinoma.

This study aimed to investigate whether preoperative ultrasonographic (US) features of metastatic lymph nodes (LNs) are associated with tumor recurrence in patients with N1b papillary thyroid carcinoma (PTC). We enrolled 692 patients (mean age, 41.9 years; range, 6-80 years) who underwent total thyroidectomy and lateral compartment LN dissection between January 2009 and December 2015 and were followed-up for 12 months or longer. Clinicopathologic findings and US features of the index tumor and metastatic LNs in the lateral neck were reviewed. A Kaplan-Meier analysis and Cox proportion hazard model were used to analyze the recurrence-free survival rates and features associated with postoperative recurrence. Thirty-seven (5.3%) patients had developed recurrence at a median follow-up of 66.5 months. On multivariate Cox proportional hazard analysis, male sex (hazard ratio [HR], 2.277; 95% confidence interval [CI]: 1.131, 4.586; p = 0.021), age ≥55 years (HR, 3.216; 95% CI: 1.529, 6.766; p = 0.002), LN size (HR, 1.054; 95% CI: 1.024, 1.085; p < 0.001), and hyperechogenicity of LN (HR, 8.223; 95% CI: 1.689, 40.046; p = 0.009) on US were independently associated with recurrence. Preoperative US features of LNs, including size and hyperechogenicity, may be valuable for predicting recurrence in patients with N1b PTC.

Fully automated measurements of volumetric breast density adapted for BIRADS 5th edition: a comparison with visual assessment.

BACKGROUND: Since the 5th edition of BI-RADS was released, prior studies have compared BI-RADS and quantitative fully automated volumetric assessment, but with software packages that were not recalibrated according to the 5th edition. PURPOSE: To investigate mammographic density assessment of automated volumetric measurements recalibrated according to the BI-RADS 5th edition compared with visual assessment. MATERIAL AND METHODS: A total of 4000 full-field digital mammographic examinations were reviewed by three radiologists for the BI-RADS 5th edition density category by consensus after individual assessments. Volumetric density data obtained using Quantra and Volpara software were collected. The comparison of visual and volumetric density assessments was performed in total and according to the presence of cancer. RESULTS: Among 4000 examinations, 129 were mammograms of breast cancer. Compared to visual assessment, volumetric measurements showed higher category B (40.6% vs. 19.8%) in Quantra, and higher category D (40.4% vs. 14.7%) and lower category A (0.2% vs. 5.0%) in Volpara (P < 0.0001). All volumetric data showed a difference according to visually assessed categories and were correlated between the two volumetric measurements (P < 0.0001). The group with cancer showed a lower proportion of fatty breast than that without cancer: 17.8% vs. 46.9% for Quantra (P < 0.0001) and 9.3% vs. 21.5% for Volpara (P = 0.003). Both measurements showed significantly higher mean density data in the group with cancer than without cancer (P < 0.005 for all). CONCLUSION: Automated volumetric measurements adapted for the BI-RADS 5th edition showed different but correlated results with visual assessment and each other. Recalibration of volumetric measurement has not completely reflected the visual assessment.

Prediction of axillary response by monitoring with ultrasound and MRI during and after neoadjuvant chemotherapy in breast cancer patients.

PURPOSE: To investigate whether monitoring with ultrasound and MR imaging before, during and after neoadjuvant chemotherapy (NAC) can predict axillary response in breast cancer patients. MATERIALS AND METHODS: A total of 131 breast cancer patients with clinically positive axillary lymph node (LN) who underwent NAC and subsequent surgery were enrolled. They had ultrasound and 3.0 T-MR examinations before, during and after NAC. After reviewing ultrasound and MR images, axillary LN features and tumour size (T size) were noted. According to LN status after surgery, imaging features and their diagnostic performances were analysed. RESULTS: Of the 131 patients, 60 (45.8%) had positive LNs after surgery. Pre-NAC T size at ultrasound and MR was different in positive LN status after surgery (p < 0.01). There were significant differences in mid- and post-NAC number, cortical thickness (CxT), T size and T size reduction at ultrasound and mid- and post-NAC CxT, hilum, T size and T size reduction, and post-NAC ratio of diameter at MR (p < 0.03). On multivariate analysis, pre-NAC MR T size (OR, 1.03), mid-NAC ultrasound T size (OR, 1.05) and CxT (OR, 1.53), and post-NAC MR T size (OR, 1.06) and CxT (OR, 1.64) were independently associated with positive LN (p < 0.004). Combined mid-NAC ultrasound T size and CxT showed the best diagnostic performance with AUC of 0.760. CONCLUSION: Monitoring ultrasound and MR axillary LNs and T size can be useful to predict axillary response to NAC in breast cancer patients. KEY POINTS: • Monitoring morphologic features of LNs is useful to predict axillary response. • Monitoring tumour size by imaging is useful to predict axillary response. • The axillary ultrasound during NAC showed the highest diagnostic performance.

Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer.

Background Previous studies have suggested that texture analysis is a promising tool in the diagnosis, characterization, and assessment of treatment response in various cancer types. Therefore, application of texture analysis may be helpful for early prediction of pathologic response in breast cancer. Purpose To investigate whether texture analysis of features from MRI is associated with pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. Materials and Methods This retrospective study included 136 women (mean age, 47.9 years; range, 31-70 years) who underwent NAC and subsequent surgery for breast cancer between January 2012 and August 2017. Patients were monitored with 3.0-T MRI before (pretreatment) and after (midtreatment) three or four cycles of NAC. Texture analysis was performed at pre- and midtreatment T2-weighted MRI, contrast material-enhanced T1-weighted MRI, diffusion-weighted MRI, and apparent diffusion coefficient (ADC) mapping by using commercial software. A random forest method was applied to build a predictive model for classifying those with pCR with use of texture parameters. Diagnostic performance for predicting pCR was assessed and compared with that of six other machine learning classifiers (adaptive boosting, decision tree, k-nearest neighbor, linear support vector machine, naive Bayes, and linear discriminant analysis) by using the Wald test and DeLong method. Results Forty of the 136 patients (29%) achieved pCR after NAC. In the prediction of pCR, the random forest classifier showed the lowest diagnostic performance with pretreatment ADC (area under the receiver operating characteristic curve [AUC], 0.53; 95% confidence interval: 0.44, 0.61) and the highest diagnostic performance with midtreatment contrast-enhanced T1-weighted MRI (AUC, 0.82; 95% confidence interval: 0.74, 0.88) among pre- and midtreatment T2-weighted MRI, contrast-enhanced T1-weighted MRI, diffusion-weighted MRI, and ADC mapping. Conclusion Texture parameters using a random forest method of contrast-enhanced T1-weighted MRI at midtreatment of neoadjuvant chemotherapy were valuable and associated with pathologic complete response in breast cancer. © RSNA, 2019 Online supplemental material is available for this article.

Scoring System to Stratify Malignancy Risks for Mammographic Microcalcifications Based on Breast Imaging Reporting and Data System 5th Edition Descriptors.

OBJECTIVE: To develop a scoring system stratifying the malignancy risk of mammographic microcalcifications using the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS). MATERIALS AND METHODS: One hundred ninety-four lesions with microcalcifications for which surgical excision was performed were independently reviewed by two radiologists according to the 5th edition of BI-RADS. Each category's positive predictive value (PPV) was calculated and a scoring system was developed using multivariate logistic regression. The scores for benign and malignant lesions or BI-RADS categories were compared using an independent t test or by ANOVA. The area under the receiver operating characteristic curve (AUROC) was assessed to determine the discriminatory ability of the scoring system. Our scoring system was validated using an external dataset. RESULTS: After excision, 69 lesions were malignant (36%). The PPV of BI-RADS descriptors and categories for calcification showed significant differences. Using the developed scoring system, mean scores for benign and malignant lesions or BI-RADS categories were significantly different (p < 0.001). The AUROC of our scoring system was 0.874 (95% confidence interval, 0.840-0.909) and the PPV of each BI-RADS category determined by the scoring system was as follows: category 3 (0%), 4A (6.8%), 4B (19.0%), 4C (68.2%), and 5 (100%). The validation set showed an AUROC of 0.905 and PPVs of 0%, 8.3%, 11.9%, 68.3%, and 94.7% for categories 3, 4A, 4B, 4C, and 5, respectively. CONCLUSION: A scoring system based on BI-RADS morphology and distribution descriptors could be used to stratify the malignancy risk of mammographic microcalcifications.

Clinical Imaging of Glycogen-rich Clear Cell Carcinoma of the Breast: A Case Series with Literature Review.

Glycogen-rich clear cell carcinoma (GRCC) of the breast is a rare malignant tumor. Most previous reports focused on clinicopathologic findings of GRCC and imaging findings were not precisely described. Here, we report imaging findings of three cases of GRCC along with a literature review. GRCC of the breast was depicted as a mass with irregular or oval shape on mammography and complex cystic and solid composition or focal cystic change on ultrasound. GRCC showed internal high signal intensity on T2-weighted MRI with rim enhancement after contrast injection. These might suggest the possibility of GRCC in differentiating breast tumors.

Identification of Preoperative Magnetic Resonance Imaging Features Associated with Positive Resection Margins in Breast Cancer: A Retrospective Study.

Objective: To determine which preoperative breast magnetic resonance imaging (MRI) findings and clinicopathologic features are associated with positive resection margins at the time of breast-conserving surgery (BCS) in patients with breast cancer. Materials and Methods: We reviewed preoperative breast MRI and clinicopathologic features of 120 patients (mean age, 53.3 years; age range, 27-79 years) with breast cancer who had undergone BCS in 2015. Tumor size on MRI, multifocality, patterns of enhancing lesions (mass without non-mass enhancement [NME] vs. NME with or without mass), mass characteristics (shape, margin, internal enhancement characteristics), NME (distribution, internal enhancement patterns), and breast parenchymal enhancement (BPE; weak, strong) were analyzed. We also evaluated age, tumor size, histology, lymphovascular invasion, T stage, N stage, and hormonal receptors. Univariate and multivariate logistic regression analyses were used to determine the correlation between clinicopathological features, MRI findings, and positive resection margins. Results: In univariate analysis, tumor size on MRI, multifocality, NME with or without mass, and segmental distribution of NME were correlated with positive resection margins. Among the clinicopathological factors, tumor size of the invasive breast cancer and in situ components were significantly correlated with a positive resection margin. Multivariate analysis revealed that NME with or without mass was an independent predictor of positive resection margins (odds ratio [OR] = 7.00; p < 0.001). Strong BPE was a weak predictor of positive resection margins (OR = 2.59; p = 0.076). Conclusion: Non-mass enhancement with or without mass is significantly associated with a positive resection margin in patients with breast cancer. In patients with NME, segmental distribution was significantly correlated with positive resection margins.

Comparison of the diagnostic performances of ultrasonography, CT and fine needle aspiration cytology for the prediction of lymph node metastasis in patients with lymph node dissection of papillary thyroid carcinoma: A retrospective cohort study.

PURPOSE: To evaluate the diagnostic performances of ultrasonographic (US) findings, computed tomography (CT) findings and fine needle aspiration cytology (FNAC) for the prediction of cervical lymph node (LN) metastases of papillary thyroid carcinoma (PTC) to determine which LN should be dissected. METHODS: 376 LNs in 302 patients who underwent both US-guided skin surface LN markings and CT before LN dissection were analyzed retrospectively. Indications for LN dissection were suspicious US findings of LN metastases (n = 300), suspicious CT findings (n = 67) or surgeon's request (n = 9). Diagnostic performances of US, CT and FNAC (including thyroglobulin (Tg)) were evaluated. The correlations of suspicious US, CT finding or malignant FNAC with the size, number and the presence of extranodal extension of metastatic LNs were analyzed. RESULTS: US indication of LN dissection was significantly correlated with malignancy (p < .0001). Values of area under the curve of highly suspicious US findings and FNAC+Tg were significantly higher than that of CT (0.786, 0.878, 0.585, p < .0001, respectively). Suspicious US, CT findings and malignant FNAC+Tg were significantly associated with the largest size of metastatic LNs (p = .003, p = .0003, and p = .0006, respectively) and total number of metastatic LNs (p = .007, p = .038, and p = .005, respectively). CONCLUSION: The diagnostic performance of US or FNAC was superior to CT and highly suspicious US findings could be complimentary to FNAC results in predicting LN metastases of PTC. LN dissection should be performed for the LNs with any suspicious US findings or malignant FNAC results rather than LNs with only suspicious CT findings.

Performance of shear-wave elastography for breast masses using different region-of-interest (ROI) settings.

Background Various size and shape of region of interest (ROI) can be applied for shear-wave elastography (SWE). Purpose To investigate the diagnostic performance of SWE according to ROI settings for breast masses. Material and Methods To measure elasticity for 142 lesions, ROIs were set as follows: circular ROIs 1 mm (ROI-1), 2 mm (ROI-2), and 3 mm (ROI-3) in diameter placed over the stiffest part of the mass; freehand ROIs drawn by tracing the border of mass (ROI-M) and the area of peritumoral increased stiffness (ROI-MR); and circular ROIs placed within the mass (ROI-C) and to encompass the area of peritumoral increased stiffness (ROI-CR). Mean (Emean), maximum (Emax), and standard deviation (ESD) of elasticity values and their areas under the receiver operating characteristic (ROC) curve (AUCs) for diagnostic performance were compared. Results Means of Emean and ESD significantly differed between ROI-1, ROI-2, and ROI-3 ( P < 0.0001), whereas means of Emax did not ( P = 0.50). For ESD, ROI-1 (0.874) showed a lower AUC than ROI-2 (0.964) and ROI-3 (0.975) ( P < 0.002). The mean ESD was significantly different between ROI-M and ROI-MR and between ROI-C and ROI-CR ( P < 0.0001). The AUCs of ESD in ROI-M and ROI-C were significantly lower than in ROI-MR ( P = 0.041 and 0.015) and ROI-CR ( P = 0.007 and 0.004). Conclusion Shear-wave elasticity values and their diagnostic performance vary based on ROI settings and elasticity indices. Emax is recommended for the ROIs over the stiffest part of mass and an ROI encompassing the peritumoral area of increased stiffness is recommended for elastic heterogeneity of mass.

Comparison of Visual Assessment of Breast Density in BI-RADS 4th and 5th Editions With Automated Volumetric Measurement.

OBJECTIVE: The purpose of this study was to compare visual assessments of mammographic breast density by radiologists using BI-RADS 4th and 5th editions in correlation with automated volumetric breast density measurements. MATERIALS AND METHODS: A total of 337 consecutive full-field digital mammographic examinations with standard views were retrospectively assessed by two radiologists for mammographic breast density according to BI-RADS 4th and 5th editions. Fully automated measurement of the volume of fibroglandular tissue and total breast and percentage breast density was performed with a commercially available software program. Interobserver and intraobserver agreement was assessed with kappa statistics. The distributions of breast density categories for both editions of BI-RADS were compared and correlated with volumetric data. RESULTS: Interobserver agreement on breast density category was moderate to substantial (κ = 0.58-0.63) with use of BI-RADS 4th edition and substantial (κ = 0.63-0.66) with use of the 5th edition but without significant difference between the two editions. For intraobserver agreement between the two editions, the distributions of density category were significantly different (p < 0.0001), the proportions of dense breast increased, and the proportion of fatty breast decreased with use of the 5th edition compared with the 4th edition (p < 0.0001). All volumetric breast density data, including percentage breast density, were significantly different among density categories (p < 0.0001) and had significant correlation with visual assessment for both editions of BI-RADS (p < 0.01). CONCLUSION: Assessment using BI-RADS 5th edition revealed a higher proportion of dense breast than assessment using BI-RADS 4th edition. Nevertheless, automated volumetric density assessment had good correlation with visual assessment for both editions of BI-RADS.

Pre-Operative Evaluation of Axillary Lymph Node Status in Patients with Suspected Breast Cancer Using Shear Wave Elastography.

The aim of this study was to evaluate shear wave elastography (SWE) for pre-operative evaluation of axillary lymph node (LN) status in patients with suspected breast cancer. A total of 130 axillary LNs in 130 patients who underwent SWE before fine-needle aspiration, core biopsy or surgery were analyzed. On gray-scale images, long and short axes, shape (elliptical or round), border (sharp or unsharp) and cortical thickening (concentric, eccentric or no fatty hilum) of LNs were assessed. On SWE, mean, maximum, minimum, standard deviation and the lesion-to-fat ratio (Eratio) values of elasticity were collected. Gray-scale and SWE features were compared statistically between metastatic and benign LNs using the χ2-test and independent t-test. Diagnostic performance of each feature was evaluated using the area under the receiver operating characteristic curve (AUC). Logistic regression analysis was used to determine gray-scale or SWE features independently associated with metastatic LNs. Of the 130 LNs, 65 (50%) were metastatic and 65 (50%) were benign after surgery. Metastatic LNs were significantly larger (p = 0.018); had higher elasticity indexes at SWE (p < 0.0001); and had higher proportions of round shape (p = 0.033), unsharp border (p = 0.048) and eccentric cortical thickening or no fatty hilum (p = 0.005) compared with benign LNs. On multivariate analysis, Eratio was independently associated with metastatic LNs (odds ratio = 3.312, p = 0.008). Eratio had the highest AUC among gray-scale (0.582-0.719) and SWE (0.900-0.950) variables. SWE had good diagnostic performance in metastatic axillary LNs, and Eratio was independently associated with metastatic LNs.

Shear-wave elastography in breast ultrasonography: the state of the art.

Shear-wave elastography (SWE) is a recently developed ultrasound technique that can visualize and measure tissue elasticity. In breast ultrasonography, SWE has been shown to be useful for differentiating benign breast lesions from malignant breast lesions, and it has been suggested that SWE enhances the diagnostic performance of ultrasonography, potentially improving the specificity of conventional ultrasonography using the Breast Imaging Reporting and Data System criteria. More recently, not only has SWE been proven useful for the diagnosis of breast cancer, but has also been shown to provide valuable information that can be used as a preoperative predictor of the prognosis or response to chemotherapy.

The clinical significance of accompanying NME on preoperative MR imaging in breast cancer patients.

PURPOSE: To investigate the significance of accompanying NME in invasive ductal carcinoma (IDC) on preoperative MR imaging and assess the factors affecting the significance. METHODS: Between January 2015 and February 2016, 163 consecutive patients with IDC who underwent preoperative MR imaging and subsequent surgery were enrolled and reviewed. Index cancer mass size and total extent with accompanying NME on MR images was measured and compared with pathologic size. Positive NME was defined as pathological result of IDC or DCIS. To identify affecting factors associated with frequency of accompanying NME on MR and positive pathologic result, clinicopathologic features were compared between breast cancers with NME and without NME, and between breast cancers with positive NME and negative NME using the Student t-test or Chi-square test. RESULTS: Of the 163 invasive breast cancers, 123(75.5%) cancers presented as only mass feature and 40(24.5%) cancers had accompanying NME around the index mass. Of the 40 accompanying NME, 22 (55%) had positive pathologic results and 18 (45%) had negative results. The HER2 positive status was significantly associated with positive pathologic results of accompanying NME (P = .016). CONCLUSION: Accompanying NME on preoperative MR imaging showed malignant pathologic results in 55%. The HER2 positive IDC was more frequently accompanied by malignant NME.

Management for BI-RADS category 3 lesions detected in preoperative breast MR imaging of breast cancer patients.

OBJECTIVE: To retrospectively evaluate characteristics of and determine appropriate follow-up recommendations for BI-RADS category 3 lesions detected in preoperative MRI of breast cancer patients. METHODS: BI-RADS category 3 assessments were identified from the breast MRI database for 5,110 consecutive breast cancer patients who had undergone preoperative MRI and surgery. Patient and lesion characteristics, malignancy rate, and interval between lesion detection and cancer diagnosis were analysed. Histopathological results or imaging at or after 2-year follow-up were used as reference standards. RESULTS: Of the 626 lesions, morphological features included a single focus in 26.5% (n = 166), multiple foci in 47.1% (n = 295), mass in 21.7% (n = 136) and non-mass enhancement in 4.6% (n = 29). Cancer was found in 0.8% (5/626) at a median interval of 50 months (range, 29-66 months). Malignancy rate according to morphological feature was: 1.8% (3/166) in a single focus, 0.7% (1/136) in mass and 3.4% (1/29) in non-mass enhancement. All detected cancers were stage 0 or IA. CONCLUSIONS: Annual follow-up might be adequate for BI-RADS category 3 lesions detected at preoperative MRI because of the 0.8% (5/626) malignancy rate, long interval between lesion detection and cancer diagnosis, and early stage of diagnosed cancers. KEY POINTS: • BI-RADS category 3 lesions on preoperative MRI had 0.8% malignancy rate. • All cancer diagnoses from BI-RADS 3 occurred after 24-month follow-up. • Annual follow-up might be adequate for BI-RADS 3 detected on preoperative MRI.

Diagnostic performance of tomosynthesis and breast ultrasonography in women with dense breasts: a prospective comparison study.

PURPOSE: To compare the diagnostic performances of tomosynthesis and ultrasonography as adjunctives to digital mammography in women with dense breasts. METHODS: A total of 778 women with dense breasts underwent digital mammography with tomosynthesis and ultrasonography for screening and diagnostic purposes. The findings of tomosynthesis and ultrasonography were evaluated independently. The primary endpoint was overall diagnostic accuracy determined by area under the receiver operating characteristic curve (AUC). Secondary endpoints included sensitivity, specificity, and predictive values. RESULTS: Of the 778 participants, 698 women (140 breast cancers) were included in the analysis. Based on the AUC findings, the non-inferiority of tomosynthesis to ultrasonography was established in the overall group as well as in all subgroups except for that comprising women with extremely dense breast composition. There were no significant differences in AUC between tomosynthesis and ultrasonography among asymptomatic participants and participants who underwent imaging for screening (0.912 vs. 0.934 [P = 0.403] and 0.987 vs. 0.950 [P = 0.270], respectively). Tomosynthesis exhibited lower sensitivity (91.4 vs. 96.4%; P = 0.039), and higher specificity (83.9 vs. 70.4%; P < 0.001) and positive predictive value (58.7 vs. 45.0%; P < 0.001) than ultrasonography. CONCLUSIONS: Tomosynthesis exhibits comparable performance to ultrasonography as an adjunct to mammography for diagnosis of breast cancer, except among women with extremely dense breasts.