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.

Comparison of Programmed Cell Death Ligand 1 Status between Core Needle Biopsy and Surgical Specimens of Triple-Negative Breast Cancer.

PURPOSE: Pembrolizumab is currently used to treat advanced triple-negative breast cancer (TNBC) and high-risk early TNBC with neoadjuvant chemotherapy (NAC). The tumor-infiltrating lymphocyte (TIL) level and programmed cell death ligand 1 (PD-L1) status are predictors of response to NAC and immune checkpoint inhibitor treatment. We aimed to investigate whether the PD-L1 status in core needle biopsies (CNBs) could represent the whole tumor in TNBC. MATERIALS AND METHODS: A total of 49 patients diagnosed with TNBC who received upfront surgery without NAC between January 2018 and March 2021 were included. The PD-L1 expression (SP142 and 22C3 clones) and TIL were evaluated in paired CNBs and resected specimens. The concordance PD-L1 status and TIL levels between CNBs and resected specimens were analyzed. RESULTS: PD-L1 positivity was more frequently observed in resected specimens. The overall reliability of TIL level in the CNB was good [intraclass correlation coefficient (ICC)=0.847, p<0.001]. The agreements of PD-L1 status were good and fair, respectively (SP142, κ=0.503, p<0.001; 22C3, κ=0.380, p=0.010). As the core number of CNB increased, the reliability and agreement also improved, especially from five tumor cores (TIL, ICC=0.911, p<0.001; PD-L1 [22C3], κ=0.750, p=0.028). Regarding PD-L1 (SP142), no further improvement was observed with ≥5 tumor cores (κ=0.600, p=0.058). CONCLUSION: CNBs with ≥5 tumor cores were sufficient to represent the TIL level and PD-L1 (22C3) status in TNBC.

Resolution of Nonmass Enhancement Extension to the Nipple at Breast MRI after Neoadjuvant Chemotherapy: Pathologic Response and Feasibility for Nipple-sparing Mastectomy.

Background Nipple-sparing mastectomy (NSM) is usually contraindicated in patients with nonmass enhancement (NME) extension to the nipple at breast MRI. However, little is known about the feasibility of NSM when NME extension to the nipple resolves after neoadjuvant chemotherapy (NAC). Purpose To evaluate whether NSM is an appropriate surgical procedure for patients in whom NME extension to the nipple resolves after NAC. Materials and Methods This retrospective study included 383 women with NME at baseline MRI who underwent NAC followed by mastectomy between January 2007 and March 2022 at a single institution. NME extension to the nipple was assessed using breast MRI before NAC (hereafter, pre-NAC) and after NAC (hereafter, post-NAC). In 326 women who underwent mastectomy with removal of the nipple-areolar complex, the rate of pathologic analysis-confirmed tumor invasion of the nipple compared with NME extension to the nipple at post-NAC breast MRI was evaluated. Tumor involvement of the nipple was also assessed in those with complete pathologic response at posttreatment MRI. Furthermore, the outcomes in 57 women undergoing NSM were investigated, particularly in patients with NME extension to the nipple at initial diagnosis. Results Of the 326 women who underwent mastectomy with removal of the nipple-areolar complex (mean age, 49 years ± 9.4 [SD]), 217 patients (67%) showed NME extension to the nipple on pre-NAC MRI scans. Among the 153 women (70%) in whom the NME extension to the nipple resolved after NAC, the rate of pathologic analysis-confirmed tumor invasion of the nipple was 2.6% (four of 153 women; 95% CI: 0, 6.5). No pathologic analysis-confirmed tumor invasion of the nipple was detected in 31 women with complete response at MRI. Of the 57 women who underwent NSM, 12 (21%) with resolution of NME extension to the nipple after NAC had no relapse during the median follow-up of 31 months (range, 11-80 months). Conclusion Pathologic analysis-confirmed tumor invasion of the nipple was rare in women with resolution of nonmass enhancement extension to the nipple after neoadjuvant chemotherapy (NAC). Therefore, nipple-sparing mastectomy could be feasible in this population, especially in those with complete MRI response to NAC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee in this issue.

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.

Correlation of Yes-Associated Protein 1 with Stroma Type and Tumor Stiffness in Hormone-Receptor Positive Breast Cancer.

(1) Background: Yes-associated protein 1 (YAP1) is an oncogene activated under the dysregulated Hippo pathway. YAP1 is also a mechanotransducer that is activated by matrix stiffness. So far, there are no in vivo studies on YAP1 expression related to stiffness. We aimed to investigate the association between YAP1 activation and tumor stiffness in human breast cancer samples, using immunohistochemistry and shear-wave elastography (SWE). (2) Methods: We included 488 patients with treatment-naïve breast cancer. Tumor stiffness was measured and the mean, maximal, and minimal elasticity values and elasticity ratios were recorded. Nuclear YAP1 expression was evaluated by immunohistochemistry and tumor-infiltrating lymphocytes (TILs); tumor-stroma ratio (TSR) and stroma type of tumors were also evaluated. (3) Results: Tumor stiffness was higher in tumors with YAP1 positivity, low TILs, and high TSR and was correlated with nuclear YAP1 expression; this correlation was observed in hormone receptor positive (HR+) tumors, as well as in tumors with non-collagen-type stroma. (4) Conclusions: We confirmed the correlation between nuclear YAP1 expression and tumor stiffness, and nuclear YAP1 expression was deemed a prognostic candidate in HR+ tumors combined with SWE-measured tumor stiffness.

Residual Tumor Patterns of Breast Cancer on MRI after Neo-Adjuvant Chemotherapy: Impact on Clinicopathologic Factors and Prognosis.

(1) Background: Residual breast cancer after neoadjuvant chemotherapy (NAC) could have a variable image pattern on a follow-up breast magnetic resonance image (MRI). In this study, we compared the clinical outcome of breast cancer patients with different residual tumor patterns (RTP) on a breast MRI after NAC. (2) Methods: A total of 91 patients with breast cancer who received NAC and subsequent curative surgery were selected. All included patient had residual breast cancer after NAC and showed a partial response on a breast MRI. Pre- and post-treatment were reviewed by an experienced radiologist to evaluate different RTP, and classified into two groups: concentric and scattered patterns. The clinicopathologic parameters and survival outcomes [recurrence-free survival (RFS) and distant metastasis-free survival (DMFS)] were analyzed according to different RTP. (3) Results: Patients with a scattered pattern had a larger extent of pre-treated non-mass enhancement and more frequently received total mastectomy. With a median follow-up period of 37 months, RTP were not significantly associated with RFS or DMFS. (4) Conclusions: In the patients with residual breast cancer after NAC, RTP on an MRI had no effect on the patients' clinical outcome. The curative resection of the tumor bed and securing the negative resection margins appear to be important in the treatment of patients with residual breast cancer after NAC.

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.

Diagnostic Accuracy of Nonmass Enhancement at Breast MRI in Predicting Tumor Involvement of the Nipple: A Prospective Study in a Single Institution.

Background Although nonmass enhancement (NME) extension to the nipple at preoperative MRI frequently leads to sacrifice of the nipple-areolar complex (NAC), its correlation with pathologically confirmed NAC involvement is unclear. Purpose To evaluate the diagnostic accuracy of using NME extension to the subareolar region at breast MRI to predict pathologic nipple involvement and the eligibility for nipple-sparing mastectomy. Materials and Methods From November 2017 to November 2019, the authors prospectively enrolled participants with breast cancer and NME within 2 cm of the nipple at breast MRI who underwent surgery that included removal of the NAC. The authors evaluated NME extensions that were ipsilateral and contiguous with the biopsy-proven tumor lesions on images acquired during the early contrast phases. Pathologic nipple involvement and the distance from the nipple to the nearest cancer cell were evaluated by using serial vertical sectioning of the area extending from the entire NAC to the tumor. The primary end point was the positive predictive value (PPV) of NME, which was calculated as follows: (number with pathologic nipple invasion and NME extension to the nipple at breast MRI/number with NME extension to the nipple at breast MRI) × 100. Results Of 64 women (mean age, 52 years ± 9.8 [standard deviation]), 49 (77%) had NME extension to the nipple at breast MRI. The PPV of NME extension to the nipple was 86% (42 of 49 women; 95% CI: 73, 94). Among the 15 participants without NME extension to the nipple, only one (7%) had pathologic nipple involvement. The diagnostic accuracy of using NME extension to the nipple was 88% (56 of 64 women; 95% CI: 77, 95). The radiologic distance correlated well with the pathologic distance (Spearman correlation coefficient = 0.71, P = .003). Conclusion Nonmass enhancement extension to the nipple base at preoperative MRI has a high positive predictive value for identifying tumor involvement of the nipple, a contraindication to nipple-sparing mastectomy. © RSNA, 2021 Online supplemental material is available for this article.

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

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

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

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

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

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

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.

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

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

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

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

Initial Serum Ammonia as a Predictor of Mortality in Patients with Acute Glufosinate Ammonium Herbicide Poisoning

Purpose@#The association of the initial serum ammonia level with in-hospital mortality in patients with acute glufosinate-ammonium herbicide poisoning was studied. @*Methods@#This retrospective cohort study was conducted between March 2012 and August 2019 in the emergency department after glufosinate-ammonium herbicide poisoning. Survivors and non-survivors were analyzed using a Mann-Whitney U test and Fisher’s exact test. Multivariate logistic regression analysis was performed to determine the independent risk factors for mortality. @*Results@#One hundred and six patients were enrolled; 11 died, yielding a mortality of 10.4%. The serum bicarbonate level was significantly lower in the non-survival group than the survival group. Age, serum ammonia, blood urea nitrogen, creatinine levels, SOFA score, and APACHE II score were significantly higher in the non-survival group than the survival group. Age, serum ammonia, and creatinine level were independent risk factors for mortality in multivariate logistic regression analysis. @*Conclusion@#The initial serum ammonia level is associated with mortality in patients with acute glufosinate herbicide poisoning.

Initial Serum Ammonia as a Predictor of Mortality in Patients with Acute Glufosinate Ammonium Herbicide Poisoning

Purpose@#The association of the initial serum ammonia level with in-hospital mortality in patients with acute glufosinate-ammonium herbicide poisoning was studied. @*Methods@#This retrospective cohort study was conducted between March 2012 and August 2019 in the emergency department after glufosinate-ammonium herbicide poisoning. Survivors and non-survivors were analyzed using a Mann-Whitney U test and Fisher’s exact test. Multivariate logistic regression analysis was performed to determine the independent risk factors for mortality. @*Results@#One hundred and six patients were enrolled; 11 died, yielding a mortality of 10.4%. The serum bicarbonate level was significantly lower in the non-survival group than the survival group. Age, serum ammonia, blood urea nitrogen, creatinine levels, SOFA score, and APACHE II score were significantly higher in the non-survival group than the survival group. Age, serum ammonia, and creatinine level were independent risk factors for mortality in multivariate logistic regression analysis. @*Conclusion@#The initial serum ammonia level is associated with mortality in patients with acute glufosinate herbicide poisoning.

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.