How Feminist Heterosexual Men Navigate Their Sexual Desire While Following Feminist Principles.

Feminist considerations have influenced how women and men view sex, affecting not only women's perspectives but also men's feelings about sexual desire with regard to gender equity. This might be especially the case among men who self-identify as feminist. However, how men should manage their sexual desire or communicate about it within relationships with women is not always clear in this evolving social climate. Thus, the current study aimed to explore the successes and/or struggles feminist heterosexual men experience while navigating their desires alongside feminist considerations. To explore this, we recruited feminist-identified heterosexual men in long-term relationships. We asked participants (N = 30) a series of questions regarding their sexual desire, considering the context of their long-term relationships and evolving gender norms, during a one-on-one interview via Zoom. Using thematic analysis, we identified 11 themes from the interview data. We found that, though the feminist men in this study were all aware of negative societal perceptions of heterosexual men's sexual desire, most men in this study did not feel conflicts between their feminist principles and their own sexual desires. This is because they reported already following feminist principles; those who felt ambivalent navigated this by communicating with their partners. Findings demonstrate the usefulness and positive impact men report feminism having on them, their sexuality, and their long-term relationships, by allowing them to better engage with their sexuality and partners.

Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.

PURPOSE: To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. MATERIALS AND METHODS: We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). RESULTS: The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). CONCLUSION: AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions.

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.

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.

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.

Comparison of resection margin status after single or double radiopaque marker insertion for tumor localization in breast cancer patients receiving neoadjuvant chemotherapy.

PURPOSE: Insertion of radiopaque markers is helpful for tumor localization in patients receiving neoadjuvant chemotherapy (NAC) followed by breast-conserving surgery (BCS). The aim of this retrospective study was to investigate the pathologic margin status in patients with single or double marker insertion. METHODS: We reviewed the records of 130 patients with marker insertion prior to NAC followed by BCS from January 2016 to September 2019. Under ultrasonography guidance, single or double markers were inserted to localize a tumor in the breast. The incidence of additional resection after frozen biopsy and re-excision after permanent pathologic diagnosis was analyzed. RESULTS: In a total of 130 patients, 104 had a single marker in the center of the tumor and 26 had double markers at the periphery of the tumor before NAC. Among 69 patients with residual invasive tumors after NAC, there was no difference in the additional resection rate after frozen biopsy (single vs. double markers; 14.3% vs. 38.5%, P = .059) or the re-excision rate after final pathologic diagnosis (0% vs. 7.7%, P = .188). After propensity score matching for tumor size and subtypes, the two groups showed no differences in the additional resection rate after frozen biopsy (7.7% vs. 19.2%, P = .139) or the re-excision rate (0% vs. 3.8%, P = .308). After a median follow-up of 19 months (range 8-48 months), local recurrence-free survival did not differ between the two groups (log-rank P = .456). CONCLUSIONS: Number of inserted markers for tumor localization did not affect the pathologic margin status after BCS.

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.

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.

A nomogram constructed using intraoperative ex vivo shear-wave elastography precisely predicts metastasis of sentinel lymph nodes in breast cancer.

OBJECTIVE: To develop a nomogram and validate its use for the intraoperative evaluation of nodal metastasis using shear-wave elastography (SWE) elasticity values and nodal size METHODS: We 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. RESULTS: We 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. CONCLUSIONS: We 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.

Grayscale Ultrasound Radiomic Features and Shear-Wave Elastography Radiomic Features in Benign and Malignant Breast Masses.

PURPOSE: To identify and compare diagnostic performance of radiomic features between grayscale ultrasound (US) and shear-wave elastography (SWE) in breast masses. MATERIALS AND METHODS: We retrospectively collected 328 pathologically confirmed breast masses in 296 women who underwent grayscale US and SWE before biopsy or surgery. A representative SWE image of the mass displayed with a grayscale image in split-screen mode was selected. An ROI was delineated around the mass boundary on the grayscale image and copied and pasted to the SWE image by a dedicated breast radiologist for lesion segmentation. A total of 730 candidate radiomic features including first-order statistics and textural and wavelet features were extracted from each image. LASSO regression was used for data dimension reduction and feature selection. Univariate and multivariate logistic regression was performed to identify independent radiomic features, differentiating between benign and malignant masses with calculation of the AUC. RESULTS: Of 328 breast masses, 205 (62.5 %) were benign and 123 (37.5 %) were malignant. Following radiomic feature selection, 22 features from grayscale and 6 features from SWE remained. On univariate analysis, all 6 SWE radiomic features (P < 0.0001) and 21 of 22 grayscale radiomic features (P < 0.03) were significantly different between benign and malignant masses. After multivariate analysis, three grayscale radiomic features and two SWE radiomic features were independently associated with malignant breast masses. The AUC was 0.929 for grayscale US and 0.992 for SWE (P < 0.001). CONCLUSION: US radiomic features may have the potential to improve diagnostic performance for breast masses, but further investigation of independent and larger datasets is needed.

Vitamin E-Conjugated Phosphopeptide Inhibitor of the Polo-Box Domain of Polo-Like Kinase 1.

Polo-like kinase 1 (Plk1) regulates cell cycle and cell proliferation, and is currently considered a potential biomarker in clinical trials for many cancers. A characteristic feature of Plks is their C-terminal polo-box domain (PBD). Pro-Leu-His-Ser-pThr (PLHS[pT])-the phosphopeptide inhibitor of the PBD of Plk1-induces apoptosis in cancer cells. However, because of the low cell membrane-penetration ability of PLHS[pT], new approaches are required to overcome these drawbacks. We therefore developed a vitamin E (VE) conjugate that is biodegradable by intracellular redox enzymes as an anticancer drug-delivery system. To ensure high efficiency of membrane penetration, we synthesized VE-S-S-PLHS[pT]KY (1) by conjugating PLHS[pT] to VE via a disulfide bond. We found that 1 penetrated cancer cell membranes, blocked cancer cell proliferation, and induced apoptosis in cancer cells through cell cycle arrest in the G2/M phase. We synthesized a radiolabeled peptide (124I-1), and the radioligand was evaluated in in vivo tumor uptake using positron emission tomography. This study shows that combination conjugates are an excellent strategy for specifically targeting Plk PBD. These conjugates have a dual function, with possible uses in anticancer therapy and tumor diagnosis.

Comparison of tumor-infiltrating lymphocytes of breast cancer in core needle biopsies and resected specimens: a retrospective analysis.

PURPOSE: Neoadjuvant chemotherapy (NAC) is being increasingly used to treat locally advanced breast cancer and to conserve the breast. In triple-negative breast cancer and HER2-positive breast cancer, a high density of tumor-infiltrating lymphocytes (TILs) is an important predictor of NAC response. Thus far, it remains unclear whether the TIL scores in core needle biopsies (CNBs) are closely representative of those in the whole tumor section in resected specimens. This study aimed to evaluate the concordance between the TIL scores of CNBs and resected specimens of breast cancer. METHODS: A total of 220 matched pairs of CNBs and resected specimens of breast cancer were included. Stromal TILs were scored on slides stained with hematoxylin and eosin. Clinicopathologic parameters and the agreement of the TIL scores between CNBs and resected specimens were statistically analyzed. RESULTS: The average TIL score was approximately 4.4% higher for the resected specimens than for the CNBs. When the tumors were divided into two groups according to a 60% TIL score cut-off (low and intermediate TIL vs. high TIL), 8.2% showed discordance between the CNB and resected specimen. The overall intraclass correlation coefficient (ICC) value of the TIL score was 0.895 (95% confidence interval, 0.864-0.920, P < 0.001), and all molecular subtypes showed ICC values over 0.8 (P < 0.001). The ICC values were > 0.9 when ≥ 5 cores were included in the CNBs. Tumors with discordant TILs were characterized by histologic grade III, ER negativity, high proliferative index, and HER2 and triple-negative subtypes. A high proliferative index was an independent risk factor for TIL discordance. CONCLUSIONS: The TIL score in CNB specimens is a reliable value that reflects the TIL status of the entire tumor in resected specimens of breast cancer. More than five CNB cores may accurately predict the TIL score of the entire tumor.

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.

Accuracy of Ultrasound for Preoperative Assessment of Tumor Size in Patients With Newly Diagnosed Breast Cancer: Is It Affected by the Background Parenchymal Echotexture?

OBJECTIVES: To assess the impact of the background parenchymal echotexture on the accuracy of tumor size estimation using breast ultrasound (US). METHODS: A total of 140 women with newly diagnosed invasive breast cancer from January 2014 to December 2015 were enrolled in this study. Two radiologists retrospectively reviewed US images in consensus for background parenchymal echotexture interpretation. The maximum tumor diameter from static images was recorded. Tumor size measurements were considered as having agreement with histologic results if they were within ±5 mm compared to the pathologic size. The relationship between the accuracy of tumor size measurement by the background parenchymal echotexture and clinicopathologic characteristics was evaluated. RESULTS: Of these 140 patients, 77 (55.0%) showed a homogeneous background parenchymal echotexture, whereas 63 (45.0%) showed a heterogeneous echotexture. The mean tumor size was 1.9 cm (range, 0.5-4.9 cm). The overall accuracy of tumor size measurement was 76.4% (104 of 140). Tumors of women with a homogeneous background parenchymal echotexture were more accurately measured than those of women with a heterogeneous echotexture (87.0% versus 63.5%; P = .001). Tumors with a small size (<2 cm; P = .018) and ductal carcinoma in situ-negative (P = .031), human epidermal growth factor receptor 2 (HER2)-negative (P = .053), and triple-negative (P = .016) types were also more accurately measured. The independent factors associated with inaccurate tumor size measurement were a heterogeneous background parenchymal echotexture, a large tumor size, and the HER2-enriched type (P < .05). CONCLUSIONS: The background parenchymal echotexture affected the accuracy of tumor size estimation using breast US. Invasive breast cancers with large (≥2 cm) tumors and the HER2-enriched type showed significantly lower breast US accuracy compared to others.

Evaluation of Screening US-detected Breast Masses by Combined Use of Elastography and Color Doppler US with B-Mode US in Women with Dense Breasts: A Multicenter Prospective Study.

Purpose To investigate the value of the combined use of elastography and color Doppler ultrasonography (US) with B-mode US for evaluation of screening US-detected breast masses in women with dense breasts. Materials and Methods This prospective, multicenter study included asymptomatic women with dense breasts who were referred for screening US between November 2013 and December 2014. Eligible women had a newly detected breast mass at conventional B-mode US screening, for which elastography and color Doppler US were performed. The following outcome measures were compared between B-mode US and the combination of B-mode US, elastography, and color Doppler US: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and the number of false-positive findings at screening US. Results Among 1021 breast masses (mean size, 1.0 cm; range, 0.3-3.0 cm) in 1021 women (median age, 45 years), 68 were malignant (56 invasive). Addition of elastography and color Doppler US to B-mode US increased the AUC from 0.87 (95% confidence interval [CI]: 0.82, 0.91) to 0.96 (95% CI: 0.95, 0.98; P < .001); specificity from 27.0% (95% CI: 24.2%, 29.9%) to 76.4% (95% CI: 73.6%, 79.1%; P < .001) without loss in sensitivity (95% CI: -1.5%, 1.5%; P > .999); and PPV from 8.9% (95% CI: 7.0%, 11.2%) to 23.2% (95% CI: 18.5%, 28.5%; P < .001), while avoiding 67.7% (471 of 696) of unnecessary biopsies for nonmalignant lesions. Conclusion Addition of elastography and color Doppler US to B-mode US can increase the PPV of screening US in women with dense breasts while reducing the number of false-positive findings without missing cancers. © RSNA, 2017 Online supplemental material is available for this article.

Predictive Factors for Active Surveillance of Subcentimeter Thyroid Nodules with Highly Suspicious US Features.

PURPOSE: 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. METHODS: 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. RESULTS: 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 < 0.05). Of 1041 evaluated papillary microcarcinomas, a multivariate analysis revealed that male gender, presence of microcalcification, and a taller than wide on US were independently associated with lymph node metastasis and ATA intermediate risk (P < 0.01). CONCLUSIONS: Age younger than 45 years, male gender, and subcentimeter thyroid nodules exhibiting microcalcification, and a taller than wide shape on US might be not good candidate factors for active surveillance.

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.

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.

Shear-Wave Elastography for Papillary Thyroid Carcinoma can Improve Prediction of Cervical Lymph Node Metastasis.

BACKGROUND: This study aimed to investigate whether the elasticity index of shear-wave elastography (SWE) can predict cervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC). METHODS: This retrospective study included 363 patients with a surgical diagnosis of PTC who underwent preoperative SWE evaluation. The elasticity indices of PTC (E mean, E max, E min, E ratio-p, and E ratio-m) and gray-scale ultrasound (US) parameters (extrathyroidal extension, multifocality, and cervical LN metastasis) were correlated with the pathologic staging parameters. The optimal cutoff values for the elasticity indices were determined for the prediction of cervical LN metastasis, and diagnostic performance was compared between gray-scale US and the combined application of gray-scale US and SWE. RESULTS: The findings showed E mean and E max to be associated with central LN metastasis (P = 0.037) and E min to be associated with lateral LN metastasis (P = 0.015). An E mean value higher than 124 kPa or an E max value higher than 138 kPa with suspicious gray-scale US findings improved the sensitivity and area under the curve (AUC) for predicting central LN metastasis (sensitivity, 45.4 and 44.6 % vs. 28 %, P < 0.001; AUC, 0.659 and 0.667 vs. 0.615, P = 0.011 and 0.019), whereas an E min value higher than 63 kPa with suspicious gray-scale US findings improved the sensitivity and AUC for predicting lateral LN metastasis (sensitivity, 95.8 vs. 75 %, P = 0.025; AUC, 0.924 vs. 0.871, P = 0.047). CONCLUSION: The quantitative elasticity index of PTC on preoperative SWE could be useful for predicting cervical LN metastasis.

Automated Volumetric Breast Density Measurements in the Era of the BI-RADS Fifth Edition: A Comparison With Visual Assessment.

OBJECTIVE: The purpose of this study is to evaluate automated volumetric measurements in comparison with visual assessment of mammographic breast density by use of the fifth edition of BI-RADS. MATERIALS AND METHODS: A total of 1185 full-field digital mammography examinations with standard views were retrospectively analyzed. All images were visually assessed by two blinded radiologists according to breast density category in the fifth edition of the BI-RADS lexicon. Automated volumetric breast density assessment was performed using two different software programs, Quantra and Volpara. A weighted kappa value was calculated to assess the degree of agreement among the visual and volumetric assessments of the density category. The volumes of fibroglandular tissue or total breast and the percentage breast density provided by the two software programs were compared. RESULTS: Compared with a visual assessment, the agreement of density category ranged from moderate to substantial in Quantra (κ = 0.54-0.61) and fair to moderate in Volpara (κ = 0.32-0.43). The distribution of density category was statistically significantly different among visual and volumetric measurements (p < 0.0001). Quantra assigned category A and B (43.5%) more frequently than did the radiologists (25.6%) or Volpara (16.0%). Volpara assigned category D (42.1%) more frequently than did the radiologists (19.5%) or Quantra (15.4%). Between the two software programs, the means of all volumetric data were statistically significantly different (p < 0.0001), but were well correlated (γ = 0.79-0.99; p < 0.0001). CONCLUSION: More mammographic examinations were classified as nondense breast tissue using the Quantra software and as dense breast tissue using the Volpara software, as compared with visual assessments according to the BI-RADS fifth edition.