Descriptive study of causes of death and COVID-19-associated morbidities from the New York City electronic death record: first wave of the pandemic March-July 2020.

OBJECTIVE: Assessing excess deaths from benchmarks across causes of death during the first wave of the COVID-19 pandemic and identifying morbidities most frequently mentioned alongside COVID-19 deaths in the death record. METHODS: Descriptive study of death records between 11 March 2020 and 27 July 2020, from the New York City Bureau of Vital Statistics. Mortality counts and percentages were compared with the average for the same calendar period of the previous 2 years. Distributions of morbidities from among forty categories of conditions were generated citywide and by sex, race/ethnicity and four age groups. Causes of death were assumed to follow Poisson processes for Z-score construction. RESULTS: Within the study period, 46 563 all-cause deaths were reported; 132.9% higher than the average for the same period of the previous 2 years (19 989). Of those 46 563 records, 19 789 (42.5%) report COVID-19 as underlying cause of death. COVID-19 was the most prevalent cause across all demographics, with respiratory conditions (prominently pneumonia), hypertension and diabetes frequently mentioned morbidities. Black non-Hispanics had greater proportions of mentions of pneumonia, hypertension, and diabetes. Hispanics had the largest proportion of COVID-19 deaths (52.9%). Non-COVID-19 excess deaths relative to the previous 2-year averages were widely reported. CONCLUSION: Mortality directly due to COVID-19 was accompanied by significant increases across most other causes from their reference averages, potentially suggesting a sizable COVID-19 death undercount. Indirect effects due to COVID-19 may partially account for some increases, but findings are hardly dispositive. Unavailability of vaccines for the time period precludes any impact over excess deaths. Respiratory and cardiometabolic-related conditions were most frequently reported among COVID-19 deaths across demographic characteristics.

Recent Advances in the Use of the Mortality Syndromic Surveillance System-New York City, 2015-2020.

OBJECTIVE: New York City's automated mortality syndromic surveillance system monitors temporal and spatial patterns in mortality. To describe the use of the syndromic surveillance system, we used the system to find mortality patterns for the 15 leading causes of death and for deaths from rare and reportable diseases in New York City from February 2015 through June 2020. We used results to find aberrations that indicate threats to public health. METHODS: We used unobserved components models to analyze time series of mortality counts for leading causes of death, historical limits methods for rare and reportable diseases, and SaTScan for temporal-spatial cluster analysis. We obtained data on the number of deaths from the electronic death registry system maintained by the city's Bureau of Vital Statistics. RESULTS: The mortality syndromic surveillance system detected an increase in the number of deaths from heart disease by April 1, 2020, when 75.0 deaths occurred on March 24, 2020, instead of an expected 45.8 deaths (95% upper prediction limit of 61.0) and an increase in the number of deaths from all causes on March 20, 2020, when 194.0 deaths were observed while 150.1 deaths were expected (95% upper prediction limit of 178.0). The number of deaths from all causes returned to normal the week beginning June 14, 2020, when 990.0 deaths were observed and 998.8 deaths were expected. PRACTICE IMPLICATIONS: When compared with efforts from New York City to provide yearly vital statistics, the automated mortality syndromic surveillance system can provide timely mortality data with fewer resources and raise the capacity to detect anomalous increases in mortality.

Palpable Breast Findings in High-risk Patients: Are Self- and Clinical Breast Exams Worthwhile?

OBJECTIVE: To assess breast imaging findings, biopsy rates, and malignancy rates in areas of palpable concern in women at high risk for breast cancer. METHODS: An IRB-approved retrospective review of a tertiary cancer center's breast imaging database was performed. Breast imaging and electronic medical records of high-risk women with palpable findings detected on self- or clinical breast examination from January 1, 2010, to January 1, 2016, were reviewed. Descriptive statistical analyses were conducted. RESULTS: Imaging correlates for 322 palpable findings in 238 high-risk women included 55/203 (27.1%) on mammography, 183/302 (60.6%) on US, and 20/47 (42.6%) on MRI. Biopsies were performed for 104/322 (32.3%) palpable findings: 95/104 (91.3%) under imaging guidance and 9/104 (8.7%) under palpation after negative imaging. Of 322 palpable findings, 16 (5.0%) were malignant in 16/238 (6.7%) women, yielding a positive predictive value of biopsy of 16.8% (95% CI: 9.2%-24%). Women diagnosed with cancer had 16/16 (100%) sonographic, 9/14 (64.3%) mammographic, and 7/7 (100%) MRI correlates. Cancer histopathology included 12 invasive ductal carcinomas, 1 ductal carcinoma in situ, 1 invasive lobular carcinoma, 1 malignant phyllodes tumor, and 1 metastatic carcinoid tumor. Over two years of follow-up imaging in 183/238 (76.9%) women were reviewed; 7/183 (3.8%) were diagnosed with breast cancer at least one year after presenting with a palpable concern in a different location. CONCLUSION: High-risk women with palpable findings exhibit a 6.7% malignancy rate, indicating the value of imaging workup in this population. In our cohort, imaging demonstrated a high negative predictive value.

Survival Outcomes of Screening with Breast MRI in Women at Elevated Risk of Breast Cancer.

OBJECTIVE: To determine survival outcomes in women with breast cancer detected at combined screening with breast MRI and mammography versus screening mammography alone. METHODS: This is an institutional review board-approved retrospective study, and the need for informed consent was waived. A total of 3002 women with an increased risk of breast cancer were screened between 2001 and 2004. Of the 3002 women, 1534 (51.1%) had 2780 combined screenings (MRI and mammography) and 1468 (48.9%) had 4811 mammography-only screenings. The Χ 2 test and the Kaplan-Meier method were used to compare cancer detection rates and survival rates. RESULTS: The overall cancer detection rate was significantly higher in the MRI plus mammography group compared with the mammography-only group (1.4% [40 of 2780] vs 0.5% [23 of 4811]; P < 0.001). No interval cancers occurred in the MRI plus mammography group, whereas 9 interval cancers were found in the mammography-only group. During a median follow-up of 10.9 years (range: 0.7 to 15.2), a total of 11 recurrences and 5 deaths occurred. Of the 11 recurrences, 6 were in the MRI plus mammography group and 5 were in the mammography-only group. All five deaths occurred in the mammography-only group. Disease-free survival showed no statistically significant difference between the two groups (P = 0.32). However, overall survival was significantly improved in the MRI plus mammography group (P = 0.002). CONCLUSION: Combined screening with MRI and mammography in women at elevated risk of breast cancer improves cancer detection and overall survival.

Preoperative Localization of Breast MRI Lesions: MRI-guided Marker Placement With Radioactive Seed Localization as an Alternative to MRI-guided Wire Localization.

OBJECTIVE: Preoperative MRI-guided wire localization (MWL) presents challenges to both the physician and patient. In this study, we examined the efficiency and outcome of MRI-guided marker placement followed by mammographic-guided radioactive seed localization (MMP/RSL) as an alternative localization method. The primary outcome parameter was pathology upon excision. The secondary outcome parameters were total procedure time and clinical indication for localization. METHODS: A retrospective review of a large tertiary cancer center's breast imaging database was performed. Records of 21 patients with MMP/RSL (24 markers) from August 2013 to January 2019 were compared with 34 patients receiving MWL (48 wires) from January 2016 to January 2019. Multiple factors, including age, prelocalization pathology, postsurgical pathology, concordance, re-excision rates, and total procedure time required for each technique, were compared. Univariate and descriptive statistical analyses were performed. RESULTS: Mean patient age in years (MMP/RSL = 54.1 ± 13.1, MWL = 55.1 ± 10.8, P = 0.389), time in MR scanner in minutes (MMP/RSL = 31.7 ± 12.0, MWL = 35.8 ± 13.1, P = 0.678), and postsurgical pathology malignancy rates (MMP/RSL = 71.4%, MWL = 65.7%, P = 0.7715) were similar without statistically significant differences. As expected, the mean total procedure time was slightly longer without a statistically significant difference (47.3 ± 19.8 min versus 35.8 ± 13.1 min, P = 0.922) for the MMP/RSL group. All patients in both groups underwent successful localization with 100% radiologic-pathology concordance. Re-excision rates were lower for the MMP/RSL group (9.5%) versus the MWL group (16.7%); however, they were not found to be statistically significant (P = 0.7104). CONCLUSION: MMP/RSL is a feasible alternative to MWL and may alleviate many challenges presented by MWL. Further studies are needed.

Multiparametric 18F-FDG PET/MRI of the Breast: Are There Differences in Imaging Biomarkers of Contralateral Healthy Tissue Between Patients With and Without Breast Cancer?

The rationale was to assess whether there are differences in multiparametric 18F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined 18F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer 18F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from 18F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in 18F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric 18F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes.

PURPOSE: To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. PROCEDURES: In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. RESULTS: For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). CONCLUSIONS: Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

Incidence of benign and malignant peri-implant fluid collections and masses on magnetic resonance imaging in women with silicone implants.

BACKGROUND: To assess the incidence of benign and malignant peri-implant fluid collections and/or masses on magnetic resonance imaging (MRI) in women with silicone implants who are being screened for silent implant rupture. METHODS: The institutional review board approved this HIPAA-compliant retrospective study and waived informed consent. Women who underwent silicone implant oncoplastic and/or cosmetic surgery and postoperative implant-protocol MRI from 2000 to 2014 were included. Peri-implant fluid collections and/or masses were measured volumetrically. A benign peri-implant fluid collection and/or mass was pathologically proven or defined as showing 2 years of imaging and/or clinical stability. A malignant peri-implant fluid collection was pathologically proven. Incidence of peri-implant fluid collections and/or masses and positive predictive value (PPV) were calculated on a per-patient level using proportions and exact 95% confidence intervals (CIs). Fisher's exact test was used in the analysis to test statistical significance pre-defined as P-value < 0.05. RESULTS: A total of 1070 women with silicone implants were included (mean age, 50.7 years; range, 40.4-53.8). Median time between reconstructive surgery and first MRI was 88.9 months (range, 0.8-1363.3). Eighteen women (1.7%) had a peri-implant fluid collection and/or mass: 15/18 (83.3%) had adequate follow-up; and only 1/15 was malignant implant associated anaplastic large cell lymphoma, with a PPV of 6.7% (95% CI: 0.003-0.0005). The median peri-implant fluid collection size was 89 mL (range, 18-450 mL). CONCLUSION: Peri-implant fluid collections and/or masses identified at silicone implant protocol breast MR imaging are rarely seen 24 months after reconstructive surgery. Image-guided fine-needle aspiration with flow cytometry may be warranted to evaluate for implant-associated lymphoma.

Limited role of DWI with apparent diffusion coefficient mapping in breast lesions presenting as non-mass enhancement on dynamic contrast-enhanced MRI.

BACKGROUND: Available data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI. METHODS: In this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI (n = 24: 12 benign and 12 malignant) and poor DWI quality (n = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10-3 mm2/s) or malignant (≤ 1.3 × 10-3 mm2/s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured. RESULTS: There were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = - 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy. CONCLUSIONS: Diagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.

Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers.

Objective: To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers. Materials and Methods: In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype. Results: HER2-positive lesions demonstrated significantly higher mean (p=0.034), Perc50 (p=0.046), and Perc90 (p=0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined. Conclusion: Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast cancer.

Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results.

BACKGROUND: To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. METHODS: One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. RESULTS: In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). CONCLUSIONS: In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

Preoperative breast MRI features associated with positive or close margins in breast-conserving surgery.

PURPOSE: To determine preoperative magnetic resonance imaging (MRI) features associated with positive or close margins in patients with breast cancer who underwent breast-conserving surgery (BCS). MATERIALS AND METHODS: A retrospective review identified 249 patients with invasive ductal carcinoma (IDC) who underwent preoperative MRI and BCS as a primary procedure between 2008 and 2010. The MR images were reviewed for descriptions of findings with no new interpretations made. Margins were defined as positive (tumor touching the inked specimen margin), close (<2 mm tumor-free margin), or negative (≥2 mm tumor-free margin). Multivariate logistic regression analysis was performed to evaluate imaging and clinical factors predictive of positive or close margins. RESULTS: Of the 249 patients, 83 (33.3%) had positive or close margins and 166 (66.7%) had negative margins on the initial BCS specimen. Multivariate analysis showed that multifocal disease (odds ratio, 4.8; 95% CI, 1.9-12.2; p =  0.001), nonmass enhancement lesion (odds ratio, 3.0; 95% CI, 1.5-6.2, p =  0.003), greater background parenchymal enhancement (odds ratio, 2.5; 95% CI, 1.1-5.6; p =  0.023), larger lesion size (odds ratio, 1.3; 95% CI, 1.0-1.7, p =  0.032), and presence of ductal carcinoma in situ on needle biopsy (odds ratio, 2.4; 95% CI, 1.3-4.6; p = 0.008) were independent predictors of positive or close margins. CONCLUSIONS: Multifocal disease, nonmass enhancement lesion, or greater background parenchymal enhancement on preoperative breast MRI were significantly associated with positive or close margins. Identifying these MRI features before surgery can be helpful to reduce the reoperation rate in BCS.

Mammographic screening in male patients at high risk for breast cancer: is it worth it?

PURPOSE: To investigate the utility of mammography for breast cancer screening in a population of males at increased risk for breast cancer. METHODS: In this HIPAA-compliant institutional review board-approved single-institution study, mammography records and clinical data of 827 male patients who underwent digital mammography from September 2011-July 2018 were analyzed via the electronic medical record. 664 of these men presented with masses, pain, or nipple discharge and were excluded from this study. The remaining 163 asymptomatic men with familial and/or personal history of breast cancer, or with a known germline mutation in BRCA, underwent screening mammography and were included in this analysis. RESULTS: 163 asymptomatic men (age: mean 63 years, range 24-87 years) underwent 806 screening mammograms. 125/163 (77%) had a personal history of breast cancer and 72/163 (44%) had a family history of breast cancer. 24/163 (15%) were known mutation carriers: 4/24 (17%) BRCA1 and 20/24 (83%) BRCA2. 792/806 (98%) of the screening mammograms were negative (BI-RADS 1 or 2); 10/806 (1.2%) were classified as BI-RADS 3, all of which were eventually downgraded to BI-RADS 2 on follow-up. 4/806 (0.4%) mammograms were abnormal (BI-RADS 4/5): all were malignant. The cancer detection rate in this cohort was 4.9 cancers/1000 examinations. CONCLUSIONS: In our cohort, screening mammography yielded a cancer detection rate of 4.9 cancers/1000 examinations which is like the detection rate of screening mammography in a population of women at average risk, indicating that screening mammography is of value in male patients at high risk for breast cancer.

MRI evaluation of axillary and intramammary lymph nodes in the postoperative period.

Our study aimed to evaluate if breast-conserving surgery and adjuvant treatment could affect the morphological features of axillary and intramammary lymph nodes on magnetic resonance imaging (MRI) in patients with invasive breast cancer and clinically negative axilla. In this single-center study, we retrospectively evaluated 50 patients who had (a) breast-conserving surgery, (b) clinically negative axilla, (c) preoperative MRI within 3 months before surgery, and (d) postoperative MRI within 12 months after surgery. Axillary and intramammary lymph nodes on postoperative MRI were identified and then compared with preoperative MRI by two breast radiologists with regards to the following: enlargement, cortical thickening, presence of fatty hilum, irregularity, heterogeneity, matting, and axillary lymph node asymmetry. Three hundred and two axillary and eight intramammary lymph nodes were evaluated. Enlargement and cortical thickening were seen in 5/50 (10%) patients in three axillary and two intramammary lymph nodes. None of the lymph nodes on postoperative MRI demonstrated occurrence of lack of fatty hilum, irregularity, heterogeneity, matting or axillary lymph node asymmetry. No evidence of recurrence was observed on 2-year follow-up. Lymph node enlargement and cortical thickening may be observed in a few patients in the postoperative period. Nevertheless, in patients with clinically negative axilla, these changes in morphology are often related to treatment rather than malignancy and favor short-term follow-up as an alternative to lymph node biopsy.

A multiparametric [18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis.

PURPOSE: To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. METHODS: In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). RESULTS: There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5-10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P < 0.001), and BPE of the contralateral healthy breast (P = 0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis. CONCLUSION: A multiparametric [18F]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer.

BACKGROUND: Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping is one of the most useful additional MRI parameters to improve diagnostic accuracy and is now often used in a multiparameric imaging setting for breast tumor detection and characterization. PURPOSE: To evaluate whether different ADC metrics can also be used for prediction of receptor status, proliferation rate, and molecular subtype in invasive breast cancer. STUDY TYPE: Retrospective. SUBJECTS: In all, 107 patients with invasive breast cancer met the inclusion criteria (mean age 57 years, range 32-87) and underwent multiparametric breast MRI. FIELD STRENGTH/SEQUENCE: 3 T, readout-segmented echo planar imaging (rsEPI) with IR fat suppression, dynamic contrast-enhanced (DCE) T1 -weighted imaging, T2 -weighted turbo-spin echo (TSE) with fatsat. ASSESSMENT: Two readers independently drew a region of interest on ADC maps on the whole tumor (WTu), and on its darkest part (DpTu). Minimum, mean, and maximum ADC values of both WTu and DpTu were compared for receptor status, proliferation rate, and molecular subtypes. STATISTICAL TESTS: Wilcoxon rank sum, Mann-Whitney U-tests for associations between radiologic features and histopathology; histogram and q-q plots, Shapiro-Wilk's test to assess normality, concordance correlation coefficient for precision and accuracy; receiver operating characteristics curve analysis. RESULTS: Estrogen receptor (ER) and progesterone receptor (PR) status had significantly different ADC values for both readers. Maximum WTu (P = 0.0004 and 0.0005) and mean WTu (P = 0.0101 and 0.0136) were significantly lower for ER-positive tumors, while PR-positive tumors had significantly lower maximum WTu values (P = 0.0089 and 0.0047). Maximum WTu ADC was the only metric that was significantly different for molecular subtypes for both readers (P = 0.0100 and 0.0132) and enabled differentiation of luminal tumors from nonluminal (P = 0.0068 and 0.0069) with an area under the curve of 0.685 for both readers. DATA CONCLUSION: Maximum WTu ADC values may be used to differentiate luminal from other molecular subtypes of breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:836-846.

Quantitative in vivo proton MR spectroscopic assessment of lipid metabolism: Value for breast cancer diagnosis and prognosis.

BACKGROUND: Breast magnetic resonance spectroscopy (1 H-MRS) has been largely based on choline metabolites; however, other relevant metabolites can be detected and monitored. PURPOSE: To investigate whether lipid metabolite concentrations detected with 1 H-MRS can be used for the noninvasive differentiation of benign and malignant breast tumors, differentiation among molecular breast cancer subtypes, and prediction of long-term survival outcomes. STUDY TYPE: Retrospective. SUBJECTS: In all, 168 women, aged ≥18 years. FIELD STRENGTH/SEQUENCE: Dynamic contrast-enhanced MRI at 1.5 T: sagittal 3D spoiled gradient recalled sequence with fat saturation, flip angle = 10°, repetition time / echo time (TR/TE) = 7.4/4.2 msec, slice thickness = 3.0 mm, field of view (FOV) = 20 cm, and matrix size = 256 × 192. 1 H-MRS: PRESS with TR/TE = 2000/135 msec, water suppression, and 128 scan averages, in addition to 16 reference scans without water suppression. ASSESSMENT: MRS quantitative analysis of lipid resonances using the LCModel was performed. Histopathology was the reference standard. STATISTICAL TESTS: Categorical data were described using absolute numbers and percentages. For metric data, means (plus 95% confidence interval [CI]) and standard deviations as well as median, minimum, and maximum were calculated. Due to skewed data, the latter were more adequate; unpaired Mann-Whitney U-tests were performed to compare groups without and with Bonferroni correction. ROC analyses were also performed. RESULTS: There were 111 malignant and 57 benign lesions. Mean voxel size was 4.4 ± 4.6 cm3 . Six lipid metabolite peaks were quantified: L09, L13 + L16, L21 + L23, L28, L41 + L43, and L52 + L53. Malignant lesions showed lower L09, L21 + L23, and L52 + L53 than benign lesions (P = 0.022, 0.027, and 0.0006). Similar results were observed for Luminal A or Luminal A/B vs. other molecular subtypes. At follow-up, patients were split into two groups based on median values for the six peaks; recurrence-free survival was significantly different between groups for L09, L21 + L23, and L28 (P = 0.0173, 0.0024, and 0.0045). DATA CONCLUSION: Quantitative in vivo 1 H-MRS assessment of lipid metabolism may provide an additional noninvasive imaging biomarker to guide therapeutic decisions in breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:239-249.

Stereotactic breast biopsy efficiency: Does a pre-biopsy grid image help?

OBJECTIVE: Prior to stereotactic breast biopsy, some radiologists obtain a mammogram image with an overlying alphanumeric grid to mark the skin overlying the target. Our purpose is to determine if this grid image affects stereotactic biopsy efficiency and accuracy, including total images obtained, procedure time and need for retargeting. MATERIALS AND METHODS: IRB approved, HIPAA compliant retrospective review of prone stereotactic biopsy cases targeting calcifications 9/1/2015 to 9/1/2016 was performed. Images and reports were reviewed for number and type of images obtained, evidence of retargeting and biopsy table time. Attending radiologist, technologist and trainee involvement were recorded. Statistical analysis was performed utilizing SAS statistical software v 9.4 (SAS Institute, Cary, NC). RESULTS: Of 463 women (avg age 58.0 years, range 30-94), 392/463 (84.7%) had grid images obtained pre-biopsy. Grid patients had more images total than non-grid (avg 9.26 versus 8.44 images/patient; p < 0.0001) but spent less time on the biopsy table (avg 15 min 2 s versus 16 min 44 s/procedure; p < 0.0001). Non-grid patients were more likely to undergo initial retargeting (45% non-grid vs 30% of grid patients; p = 0.013); however, later retargeting after needle placement was comparable (p = 0.3). CONCLUSION: Grid imaging increases images obtained but decreases retargeting and biopsy table time at the expense of mammogram room/technologist time to obtain the grid image. The overall result is longer total procedure time (grid time plus table time) for the patient/technologist. A grid image therefore has limited usefulness and should be used judiciously in cases where prone positioning is challenging to patients.

Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy.

BACKGROUND: The MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon recommends that a breast MRI protocol contain T2 -weighted and dynamic contrast-enhanced (DCE) MRI sequences. The addition of diffusion-weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE-MRI, DWI, and T2 -weighted imaging are most strongly associated with a breast cancer diagnosis. PURPOSE/HYPOTHESIS: To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI-RADS recommended descriptors for breast MRI with DCE, T2 -weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping. STUDY TYPE: Retrospective. SUBJECTS: In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014. FIELD STRENGTH/SEQUENCE: IR inversion recovert DCE-MRI dynamic contrast-enhanced magnetic resonance imaging VIBE Volume-Interpolated-Breathhold-Examination FLASH turbo fast-low-angle-shot TWIST Time-resolved angiography with stochastic Trajectories. ASSESSMENT: Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T2 -weighted imaging according to BI-RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE-MRI BI-RADS descriptors, T2 -weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10-3 mm2 /sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference. STATISTICAL TESTS: χ2 test, Fisher's exact test, Kruskal-Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer-Lemeshow test of goodness-of-fit, receiver operating characteristics analysis. RESULTS: In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T2 -weighted imaging variables were not included in the final models. DATA CONCLUSION: mpMRI with DCE-MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE-MRI and DWI identifies breast cancer with a high diagnostic accuracy. T2 -weighted imaging does not significantly contribute to breast cancer diagnosis. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:864-874.

Second opinion interpretation of breast ultrasound images-Is it worth another look?

PURPOSE: To determine if real-time breast ultrasound (RTUR) after second opinion reinterpretation of submitted static ultrasound images at a comprehensive cancer center impacts clinical management, specifically by detecting additional cancer and preventing unnecessary biopsy. MATERIALS AND METHODS: In this IRB-approved and HIPAA-compliant retrospective study, 209 patients were included who had breast ultrasound studies from outside facilities submitted for second opinion review between January 2013 and May 2014, and who subsequently underwent RTUR at our institution within three months of the outside study. Findings on submitted exams were compared with those on RTUR and disagreements between them were annotated to indicate the presence or absence of suspicious lesions and recommendation for biopsy. Changes in management were defined as any additional biopsies performed or biopsies averted after RTUR and reported as frequencies and percentages using 95% confidence intervals. RESULTS: Following RTUR, 49 additional biopsies were performed in 43/209 patients (20.6%; 95% CI 15.1-26.1%). Additional cancer was found in 12/49 (24.5%) biopsies in 11/209 patients (5.3%; 95% CI 2.2-8.2%). Forty biopsies in 31/209 (14.8%; CI 10.0-19.7%) patients originally recommended were canceled after RTUR. Overall, a change in management after RTUR was observed in 68/209 patients (32.5%; 95% CI 26.1-38.9%), including patients with either additional biopsies performed or biopsies averted. CONCLUSION: RTUR was found to be an important tool in the management of patients at our comprehensive cancer center. Although additional false-positive lesions may be detected on RTUR, a great number of patients will benefit from RTUR in finding additional cancers or avoiding unnecessary biopsies.