Tumor-Infiltrating Lymphocyte Level Consistently Correlates with Lower Stiffness Measured by Shear-Wave Elastography: Subtype-Specific Analysis of Its Implication in Breast Cancer.

Background: We aimed to elucidate the clinical significance of tumor stiffness across breast cancer subtypes and establish its correlation with the tumor-infiltrating lymphocyte (TIL) levels using shear-wave elastography (SWE). Methods: SWE was used to measure tumor stiffness in breast cancer patients from January 2016 to August 2020. The association of tumor stiffness and clinicopathologic parameters, including the TIL levels, was analyzed in three breast cancer subtypes. Results: A total of 803 patients were evaluated. Maximal elasticity (Emax) showed a consistent positive association with an invasive size and the pT stage in all cases, while it negatively correlated with the TIL level. A subgroup-specific analysis revealed that the already known parameters for high stiffness (lymphovascular invasion, lymph node metastasis, Ki67 levels) were significant only in hormone receptor-positive and HER2-negative breast cancer (HR + HER2-BC). In the multivariate logistic regression, an invasive size and low TIL levels were significantly associated with Emax in HR + HER2-BC and HER2 + BC. In triple-negative breast cancer, only TIL levels were significantly associated with low Emax. Linear regression confirmed a consistent negative correlation between TIL and Emax in all subtypes. Conclusions: Breast cancer stiffness presents varying clinical implications dependent on the tumor subtype. Elevated stiffness indicates a more aggressive tumor biology in HR + HER2-BC, but is less significant in other subtypes. High TIL levels consistently correlate with lower tumor stiffness across all subtypes.

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.

Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer.

Shear-wave elastography (SWE) is an effective tool in discriminating malignant lesions of breast and axillary lymph node metastasis in patients with breast cancer. However, the association between the baseline elasticity value of breast cancer and the treatment response of neoadjuvant chemotherapy is yet to be elucidated. Baseline SWE measured mean stiffness (E-mean) and maximum stiffness (E-max) in 830 patients who underwent neoadjuvant chemotherapy and surgery from January 2012 to December 2022. Association of elasticity values with breast pCR (defined as ypTis/T0), pCR (defined as ypTis/T0, N0), and tumor-infiltrating lymphocytes (TILs) was analyzed. Of 830 patients, 356 (42.9%) achieved breast pCR, and 324 (39.0%) achieved pCR. The patients with low elasticity values had higher breast pCR and pCR rates than those with high elasticity values. A low E-mean (adjusted odds ratio (OR): 0.620; 95% confidence interval (CI): 0.437 to 0.878; p = 0.007) and low E-max (adjusted OR: 0.701; 95% CI: 0.494 to 0.996; p = 0.047) were independent predictive factors for breast pCR. Low elasticity values were significantly correlated with high TILs. Pretreatment elasticity values measured using SWE were significantly associated with treatment response and inversely correlated with TILs, particularly in HR+HER2- breast cancer and TNBC.

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.

Lignin Metabolism by Selected Fungi and Microbial Consortia for Plant Stimulation: Implications for Biologically Active Humus Genesis.

Plant lignin is regarded as an important source for soil humic substances (HSs). Nonetheless, it remains unclear whether microbial metabolism on lignin is related to the genesis of unique HS biological activities (e.g., direct plant stimulation). Here, selected white-rot fungi (i.e., Ganoderma lucidum and Irpex lacteus) and plant litter- or mountain soil-derived microbial consortia were exploited to structurally modify lignin, followed by assessing the plant-stimulatory activity of the lignin-derived products. Parts solubilized by microbial metabolism on lignin were proven to exhibit organic moieties of phenol, carboxylic acid, and aliphatic groups and the enhancement of chromogenic features (i.e., absorbance at 450 nm), total phenolic contents, and radical-scavenging capacities with the cultivation times. In addition, high-resolution mass spectrometry revealed the shift of lignin-like molecules toward those showing either more molar oxygen-to-carbon or more hydrogen-to-carbon ratios. These results support the findings that the microbes involved, solubilize lignin by fragmentation, oxygenation, and/or benzene ring opening. This notion was also substantiated by the detection of related exoenzymes (i.e., peroxidases, copper radical oxidases, and hydrolases) in the selected fungal cultures, while the consortia treated with antibacterial agents showed that the fungal community is a sufficient condition to induce the lignin biotransformation. Major families of fungi (e.g., Nectriaceae, Hypocreaceae, and Saccharomycodaceae) and bacteria (e.g., Burkholderiaceae) were identified in the lignin-enriched cultures. All the microbially solubilized lignin products were likely to stimulate plant root elongation in the order selected white-rot fungi > microbial consortia > antibacterial agent-treated microbial consortia. Overall, this study supports the idea that microbial transformation of lignin can contribute to the formation of biologically active organic matter. IMPORTANCE Structurally stable humic substances (HSs) in soils are tightly associated with soil fertility, and it is thus important to understand how soil HSs are naturally formed. It is believed that microbial metabolism on plant matter contributes to natural humification, but detailed microbial species and their metabolisms inducing humic functionality (e.g., direct plant stimulation) need to be further investigated. Our findings clearly support that microbial metabolites of lignin could contribute to the formation of biologically active humus. This research direction appears to be meaningful not only for figuring out the natural processes, but also for confirming natural microbial resources useful for artificial humification that can be linked to the development of high-quality soil amendments.

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.

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.

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.

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.

Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network.

The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n = 13,560, Severance Hospital, SH training set), the internal test (n = 634, SH test set), and the external test (n = 781, Samsung Medical Center, SMC set; n = 200, CHA Bundang Medical Center, CBMC set; n = 200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898-0.937 for the internal test set and 0.821-0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P < 0.001). AUC was not significantly different between CNNE2 and radiologists in the external test sets (P = 0.113, 0.126, and 0.690). CNN showed diagnostic performances comparable to expert radiologists for differentiating thyroid nodules on US in both the internal and external test sets.

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.

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.

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.

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.

Amphiphilic Triazine Polymer Derivatives as Antibacterial And Anti-atopic Agents in Mice Model.

Considering the emergence of bacterial resistance and low proteolytic stability of antimicrobial peptides (AMPs), herein we developed a series of ultra-short triazine based amphipathic polymers (TZP) that are connected with ethylene diamine linkers instead of protease sensitive amide bond. The most potent oligomers, TZP3 and TZP5 not only displayed potent antibacterial action on various drug-resistant pathogens but also exhibited a strong synergic antibacterial activity in combination with chloramphenicol against multidrug-resistant Pseudomonas aeruginosa (MDRPA). Since most of atopic dermatitis (AD) infections are caused by bacterial colonization, we evaluated the potency of TZP3 and TZP5 on AD in vitro and in vivo. In vitro AD analysis of these two polymers showed significant inhibition against the release of ß-hexosaminidase and tumor necrosis factor (TNF-α) from RBL-2H3 cells. In AD-like skin lesions in BALB/c mice model, these two polymers displayed significant potency in suppressing dermal and epidermal thickness, mast cell infiltration and pro-inflammatory cytokines expression. Moreover, these polymers exhibited remarkable efficacy over the allergies caused by the imbalance of Th1/Th2 by regulating total IgE and IgG2a. Finally, the impact of treatment effects of these polymers was examined through analyzing the weights and sizes of spleen and lymph node of AD-induced mice.

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.

Measuring Tumor Extent Based on Subtypes Using Magnetic Resonance Imaging: Radiologic-Pathologic Discordance and High Positive Margin Rates in Breast Cancer.

PURPOSE: We evaluated the clinical value of breast magnetic resonance imaging (MRI) in patients who underwent breast-conserving surgery (BCS). The degree of correlation between pathology size and MRI or ultrasonography (US) size was compared based on breast cancer subtypes. In addition, we investigated the positive margin rates. METHODS: Patients with invasive breast cancer who underwent preoperative breast MRI and US between 2011 and 2016 were included in the study. Lin's concordance correlation coefficient was used to measure the correlation between MRI or US andpathologic tumor extent. Tumor extent was defined as pathologic tumor size, including in situ carcinoma. Margin positivity was assessed based on frozen-section examination. RESULTS: A total of 516 patients with a single tumor who underwent BCS were included in the study. The correlation between pathologic size and MRI was significantly higher than that of US (r = 0.6975 vs. 0.6211, p = 0.001). The superiority of MRI over US in measuring the pathologic extent was only observed in triple-negative breast cancer (TNBC; r = 0.8089 vs. 0.6014, p < 0.001). The agreement between MRI or US and tumor extent was low for the human epidermal growth factor receptor 2 (HER2)-positive subtype (MRI: 0.5243, US: 0.4898). Moreover, the positive margin rate was higher in the HER2-positive subtype than in the others (luminal/HER2-negative: 11.6%, HER2-positive: 23.2%, TNBC: 17.8%, p = 0.019). The post hoc analysis showed that the HER2-positive subtype was more likely to show positive margins than the luminal/HER2-negative subtype (p = 0.007). CONCLUSION: Breast MRI was superior to US in the preoperative assessment of the pathologic extent of tumor size; this was most evident in TNBC. For HER2-positive tumors, imaging-pathologic discordance resulted in higher positive margin rates than that with other subtypes.