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To investigate the added diagnostic value of abbreviated breast magnetic resonance imaging (MRI) for suspicious microcalcifications on screening mammography. 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. 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. 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. • 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.

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.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.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]).Nomograms based on SWE can predict Oncotype DX RS for use in adjuvant systemic therapy decisions.

To determine whether texture analysis for magnetic resonance imaging (MRI) can predict recurrence in patients with breast cancer treated with neoadjuvant chemotherapy (NAC). 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. 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. 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. • 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.

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 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.

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

To evaluate how breast cancers are depicted by artificial intelligence-based computer-assisted diagnosis (AI-CAD) according to clinical, radiological, and pathological factors.From January 2017 to December 2017, 896 patients diagnosed with 930 breast cancers were enrolled in this retrospective study. Commercial AI-CAD was applied to digital mammograms and abnormality scores were obtained. We evaluated the abnormality score according to clinical, radiological, and pathological characteristics. False-negative results were defined by abnormality scores less than 10.The median abnormality score of 930 breasts was 87.4 (range 0-99). The false-negative rate of AI-CAD was 19.4% (180/930). Cancers with an abnormality score of more than 90 showed a high proportion of palpable lesions, BI-RADS 4c and 5 lesions, cancers presenting as mass with or without microcalcifications and invasive cancers compared with low-scored cancers (all p0.001). False-negative cancers were more likely to develop in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers and DCIS compared to detected cancers.Breast cancers depicted with high abnormality scores by AI-CAD are associated with higher BI-RADS category, invasive pathology, and higher cancer stage.• High-scored cancers by AI-CAD included a high proportion of BI-RADS 4c and 5 lesions, masses with or without microcalcifications, and cancers with invasive pathology. • Among invasive cancers, cancers with higher T and N stage and HER2-enriched subtype were depicted with higher abnormality scores by AI-CAD. • Cancers missed by AI-CAD tended to be in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers by radiologists.

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.

The purpose of this study was to determine whether disease recurrence and intrinsic characteristics of ductal carcinoma in situ (DCIS) are associated with the imaging method of detection in asymptomatic women. This multicenter, retrospective study included 844 women treated for asymptomatic DCIS who had pre-operative mammography and breast ultrasonography (US) studies available. Of the 844 women, 25 (3.0%) developed recurrences. Patients in the US group had significantly lower 5- and 10-y recurrence-free survival (RFS) rates compared with patients in the mammography group (p = 0.011). US-detected DCIS showed significantly lower 5-and 10-y RFS rates compared with mammography-detected DCIS in patients50 y or with mammographically dense breasts (p = 0.002 and 0.002, respectively). US as the detection modality (hazard ratio [HR]: 4.451; 95% confidence interval [CI]: 1.530, 12.950; p = 0.006) and HER2 positivity (HR: 4.036; 95% CI: 1.438; 11.330; p = 0.008) were significantly associated with recurrence. We concluded that US as the detection modality and HER2 positivity were significantly associated with recurrence in patients treated for asymptomatic DCIS.

Background Although sentinel lymph node biopsy (SLNB) is the current standard for identifying lymph metastasis in breast cancer patients, there are complications of SLNB. Purpose To evaluate preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) of invasive breast cancer for predicting sentinel lymph node metastasis. Study type Retrospective. Population In all, 309 patients who underwent clinically node-negative invasive breast cancer surgery FIELD STRENGTH/SEQUENCE: 3.0T, DCE-MRI, DWI. Assessment We collected clinicopathologic variables (age, histologic and nuclear grade, extensive intraductal carcinoma component, lymphovascular invasion, and immunohistochemical profiles) and preoperative MRI features (tumor size, background parenchymal enhancement, internal enhancement, adjacent vessel sign, whole-breast vascularity, initial enhancement pattern, kinetic curve types, quantitative kinetic parameters, tumoral apparent diffusion coefficient [ADC], peritumoral maximal ADC, and peritumoral-tumoral ADC ratio). Statistical tests Multivariate logistic regressions were performed to determine independent variables associated with SLN metastasis, and the area under the receiver operating characteristic curve (AUC) was analyzed for those variables. Results 41 (13.3%) of the patients showed SLN metastasis. With MRI, tumor size (odds ratio [OR], 1.11; 95% confidence interval [CI], 1.06-1.17), heterogeneous (OR, 5.33; 95% CI, 1.71-16.58), and rim (OR, 15.54; 95% CI, 2.12-113.72) enhancement and peritumoral-tumoral ADC ratio (OR, 72.79; 95% CI, 7.15-740.82) were independently associated with SLN metastasis. Clinicopathologic variables independently associated with SLN metastasis included age (OR, 0.96; 95% CI, 0.92-0.99) and CD31 (OR, 2.90; 95% CI, 1.04-8.92). The area under the curve (AUC) of MRI features (0.80; 95% CI, 0.73-0.87) was significantly higher than for clinicopathologic variables (0.68; 95% CI, 0.60-0.77; P = 0.048) and was barely below statistical significance for combined MRI features with clinicopathologic variables (0.84; 95% CI 0.78-0.90, P = 0.057). Data conclusion Preoperative internal enhancement on DCE-MRI and peritumoral-tumoral ADC ratio on DWI might be useful for predicting SLN metastasis in patients with invasive breast cancer. Level of evidence 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:615-626.

To evaluate the effect of training radiology residents on breast ultrasonography (US) according to the Breast Imaging Reporting And Data System (BI-RADS) and the factors that influence the training effect.This multicenter, prospective study was approved by eight institutional review boards. From September 2013 to July 2014, 248 breast masses in 227 women were included for US image acquisition. Representative B-mode and video images of the breast masses were recorded, among which 54 cases were included in the education set and 66 in the test set. Sixty-one radiology residents scheduled for breast imaging training individually reviewed the test set, immediately before, 1 month after, and 6 months after training. Diagnostic performances and US descriptors of the residents were evaluated and compared against those of expert radiologists.Agreements between residents and experienced radiologists showed improvement after training, while agreements between post-training and post-6-month training descriptors did not show significant differences (all p 0.05, respectively). Sensitivity, negative predictive value (NPV), and AUC were significantly improved for residents post-training and post-6-month training (all p 0.05), while approximating the performances of expert radiologists except for AUC (0.836, 0.840, and 0.908, respectively, p 0.05). Low levels of pre-training AUC, total number of breast US examinations, and the number of sessions per week that residents were involved in were factors influencing the improvement of AUC.Training using education material dedicated for breast US imaging effectively improved the diagnostic performances of radiology residents and agreements with experienced radiologists on US BI-RADS features.• Agreements on lesion descriptors between residents and experienced radiologists showed improvement after training, regardless of test point. • Sensitivity, NPV, and AUC were significantly improved for residents in post-training and post-6-month training (all p 0.05). • Low levels of pre-training AUC, total number of breast US examinations, and the number of sessions per week that residents were involved in were factors influencing the improvement of AUC.

Background: To evaluate how artificial intelligence-based computer-assisted diagnosis (AI-CAD) for breast ultrasound (US) influences diagnostic performance and agreements between radiologists with varying experience levels in different workflows.Methods: From Apr 2017 to Jun 2018, images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women were included. Six radiologists (3 inexperienced with < 1 year of experience, 3 experienced with 10-15 years of experience) individually reviewed US images with and without the aid of AI-CAD, first in sequential and then in independent reading. Diagnostic performances and interobserver agreements were calculated and compared between radiologists and AI-CAD. Results: After implementing AI-CAD, specificity, PPV and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). Overall area under the receiving operator characteristics curve (AUC) significantly increased in independent reading, but only for inexperienced radiologists. Agreements for BI-RADS descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in independent reading (PP<0.001) for both inexperienced and experienced radiologists. Conclusions: Using AI-CAD to interpret breast US improves the specificity, PPV and accuracy of radiologists regardless of experience level. AI-CAD may work better in independent reading to improve diagnostic performance and agreements between radiologists, especially for inexperienced radiologists.Trial registration: retrospectively registered

Purpose Preoperative neck ultrasound (US) for lateral cervical lymph nodes is recommended for all patients undergoing thyroidectomy for thyroid malignancy, but it is operator dependent. We aimed to develop a radiomics signature using US images of the primary tumor to preoperatively predict lateral lymph node metastasis (LNM) in patients with conventional papillary thyroid carcinoma (cPTC). Methods Four hundred consecutive cPTC patients from January 2004 to February 2006 were enrolled as the training cohort, and 368 consecutive cPTC patients from March 2006 to February 2007 served as the validation cohort. A radiomics signature, which consisted of 14 selected features, was generated by the least absolute shrinkage and selection operator (LASSO) regression model in the training cohort. The discriminating performance of the radiomics signature was assessed in the validation cohort with the area under the receiver operating characteristic curve (AUC). Results The radiomics signature was significantly associated with lateral cervical lymph node status (p < 0.001). The AUC of its performance in discriminating metastatic and non-metastatic lateral cervical lymph nodes was 0.710 (95% CI: 0.649–0.770) in the training cohort and was 0.621 (95% CI: 0.560–0.682) in the validation cohort. Conclusions The present study showed that US radiomic features of the primary tumor were associated with lateral cervical lymph node status. Although their discriminatory performance was slightly lower in the validation cohort, our study shows that US radiomic features of the primary tumor alone have the potential to predict lateral LNM.

Aim To investigate whether magnetic resonance imaging (MRI) can improve the positive predictive value (PPV) for Breast Imaging-Reporting and Data System (BI-RADS) category 4B mammographic microcalcification. Materials and methods One hundred and eight consecutive patients with BI-RADS category 4B microcalcification without mass on mammography underwent breast MRI and subsequent histopathological confirmation between January 2009 and December 2015. Mammography and MRI findings were reviewed retrospectively, and imaging features were analysed according to the 5th edition of BI-RADS. The PPV of each descriptor was analysed to identify subgroups in which PPV could be improved by the addition of MRI. Results When the criteria of presence of enhancement on MRI was applied to category 4B microcalcification, PPV increased from 0.38 (41 of 108) to 0.82 (37 of 45) and reduced benign biopsy results by 88% (59 of 67). Four ductal carcinoma in situ lesions were missed. For amorphous microcalcification with regional or grouped distribution, MRI images increased PPV without missing malignancy. Conclusion Breast MRI has the potential to improve PPV for category 4B mammographic microcalcification by reducing false-positive findings. If amorphous microcalcification with regional or grouped distribution on mammography shows no enhancement on MRI, follow-up could be considered rather than immediate biopsy. In addition, breast MRI might have the potential to guide the best site to biopsy in category 4B microcalcification.

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.

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.

Purpose This study was conducted to evaluate the diagnostic performance of machine learning in differentiating follicular adenoma from carcinoma using preoperative ultrasonography (US). Methods In this retrospective study, preoperative US images of 348 nodules from 340 patients were collected from two tertiary referral hospitals. Two experienced radiologists independently reviewed each image and categorized the nodules according to the 2015 American Thyroid Association guideline. Categorization of a nodule as highly suspicious was considered a positive diagnosis for malignancy. The nodules were manually segmented, and 96 radiomic features were extracted from each region of interest. Ten significant features were selected and used as final input variables in our in-house developed classifier models based on an artificial neural network (ANN) and support vector machine (SVM). The diagnostic performance of radiologists and both classifier models was calculated and compared. Results In total, 252 nodules from 245 patients were confirmed as follicular adenoma and 96 nodules from 95 patients were diagnosed as follicular carcinoma. As measures of diagnostic performance, the average sensitivity, specificity, and accuracy of the two experienced radiologists in discriminating follicular adenoma from carcinoma on preoperative US images were 24.0%, 84.0%, and 64.8%, respectively. The sensitivity, specificity, and accuracy of the ANN and SVM-based models were 32.3%, 90.1%, and 74.1% and 41.7%, 79.4%, and 69.0%, respectively. The kappa value of the two radiologists was 0.076, corresponding to slight agreement. Conclusion Machine learning-based classifier models may aid in discriminating follicular adenoma from carcinoma using preoperative US.

Purpose There is still a clinical need to easily evaluate the metastatic status of lymph nodes during breast cancer surgery. We hypothesized that ex vivo shear-wave elastography (SWE) would predict precisely the presence of metastasis in the excised lymph nodes. Methods A total of 63 patients who underwent breast cancer surgery were prospectively enrolled in this study from May 2014 to April 2015. The excised axillary lymph nodes were examined using ex vivo SWE. Metastatic status was confirmed based on the final histopathological diagnosis of the permanent section. Lymph node characteristics and elasticity values measured by ex vivo SWE were assessed for possible association with nodal metastasis. Results A total of 274 lymph nodes, harvested from 63 patients, were examined using ex vivo SWE. The data obtained from 228 of these nodes from 55 patients were included in the analysis. Results showed that 187 lymph nodes (82.0%) were nonmetastatic and 41 lymph nodes (18.0%) were metastatic. There was significant difference between metastatic and nonmetastatic nodes with respect to the mean (45.4 kPa and 17.7 kPa, p

The purpose of our study was to assess the cancer detection rate and positive predictive value (PPV) for incidentally detected abnormal axillary lymph nodes with negative mammography and subsequent breast ultrasound (US). We included 7039 screened patients (mean age, 52 y) from January 2012 to March 2015 with negative mammography and subsequent breast US results. In two patients with positive lymph nodes, neither were from breast malignancy. The calculated cancer detection rate, PPV for biopsy and axillary biopsy rate was 0.3 per 1000 axillary US, 14.3% and 0.2%. Cancer rates for patients with a family or personal operation history, with cancer history and with no family/operation/cancer history were 0%, 0.1% and 0.2%, respectively. Our results indicate that the cancer detection rate and PPV for US were too low to recommend routine axillary scanning including screening breast US in patients with negative mammography and subsequent breast US, especially with no history of any cancers.

Simple Summary The presence of lateral neck lymph node (LN) metastasis (N1b) is a known prognostic factor for poor prognosis and high morbidity after surgery in papillary thyroid carcinoma (PTC). Previous studies have suggested that tumor size and metastatic LN characteristics, including LN size, number, ratio, and extranodal extension, are associated with recurrence; however, the prognostic role of imaging features of LNs in the lateral neck have rarely been reported. In this study, we investigated whether ultrasound imaging features of lateral neck LN metastases can be prognostic markers for predicting recurrence and thereby guide surgical extent and posttreatment surveillance in N1b PTC. Abstract 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.

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 max 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 SD was significantly different between ROI-M and ROI-MR and between ROI-C and ROI-CR ( P SD 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.

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.

This study was designed to assess the outcomes of subcentimeter thyroid nodules with highly suspicious ultrasonography (US) features and to investigate the predictive factors associated with malignancy and aggressive biological behavior to determine appropriate candidate factors for active surveillance. Between June 2011 and December 2013, 1866 subcentimeter thyroid nodules with highly suspicious US features that were subjected to US-guided fine needle aspiration and subsequent surgery or US follow-up of at least 2 years were evaluated. A multivariate logistic regression analysis was performed to identify independent clinical characteristics and US features associated with the malignancy rate and aggressive biological behavior. Of the 1866 subcentimeter thyroid nodules, 821 (44.0%) were benign and 1045 (56.0%) were malignant. Age younger than 45 years, presence of microcalcification, and a taller than wide shape on US were associated independently with malignancy in the subcentimeter thyroid nodules (P

To investigate whether monitoring with ultrasound and MR imaging before, during and after neoadjuvant chemotherapy (NAC) can predict axillary response in breast cancer patients. 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. 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

ObjectiveTo develop a nomogram and validate its use for the intraoperative evaluation of nodal metastasis using shear-wave elastography (SWE) elasticity values and nodal sizeMethodsWe constructed a nomogram to predict metastasis using ex vivo SWE values and ultrasound features of 228 axillary LNs from fifty-five patients. We validated its use in an independent cohort comprising 80 patients. In the validation cohort, a total of 217 sentinel LNs were included.ResultsWe developed the nomogram using the nodal size and elasticity values of the development cohort to predict LN metastasis; the area under the curve (AUC) was 0.856 (95% confidence interval (CI), 0.783–0.929). In the validation cohort, 15 (7%) LNs were metastatic, and 202 (93%) were non-metastatic. The mean stiffness (23.54 and 10.41 kPa,p= 0.005) and elasticity ratio (3.24 and 1.49,p= 0.028) were significantly higher in the metastatic LNs than those in the non-metastatic LNs. However, the mean size of the metastatic LNs was not significantly larger than that of the non-metastatic LNs (8.70 mm vs 7.20 mm, respectively;p= 0.123). The AUC was 0.791 (95% CI, 0.668–0.915) in the validation cohort, and the calibration plots of the nomogram showed good agreement.ConclusionsWe developed a well-validated nomogram to predict LN metastasis. This nomogram, mainly based on ex vivo SWE values, can help evaluate nodal metastasis during surgery.Key Points• A nomogram was developed based on axillary LN size and ex vivo SWE values such as mean stiffness and elasticity ratio to easily predict axillary LN metastasis during breast cancer surgery.• The constructed nomogram presented high predictive performance of sentinel LN metastasis with an independent cohort.• This nomogram can reduce unnecessary intraoperative frozen section which increases the surgical time and costs in breast cancer patients.