Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study.

BACKGROUND: Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70-80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm on mammogram images to classify BI-RADS 4 suspicious lesions aiming to reduce unnecessary breast biopsies. MATERIALS AND METHODS: This retrospective study included 847 patients with a BI-RADS 4 breast lesion that underwent biopsy at a single institution and included 200 invasive breast cancers, 200 ductal carcinoma in-situ (DCIS), 198 pure atypias, 194 benign, and 55 atypias upstaged to malignancy after excisional biopsy. We employed convolutional neural networks to perform 4 binary classification tasks: (I) benign vs. all atypia + invasive + DCIS, aiming to identify the benign cases for whom biopsy may be avoided; (II) benign + pure atypia vs. atypia-upstaged + invasive + DCIS, aiming to reduce excision of atypia that is not upgraded to cancer at surgery; (III) benign vs. each of the other 3 classes individually (atypia, DCIS, invasive), aiming for a precise diagnosis; and (IV) pure atypia vs. atypia-upstaged, aiming to reduce unnecessary excisional biopsies on atypia patients. RESULTS: A 95% sensitivity for the "higher stage disease" class was ensured for all tasks. The specificity value was 33% in Task I, and 25% in Task II, respectively. In Task III, the respective specificity value was 30% (vs. atypia), 30% (vs. DCIS), and 46% (vs. invasive tumor). In Task IV, the specificity was 35%. The AUC values for the 4 tasks were 0.72, 0.67, 0.70/0.73/0.72, and 0.67, respectively. CONCLUSION: Deep learning of digital mammograms containing BI-RADS 4 findings can identify lesions that may not need breast biopsy, leading to potential reduction of unnecessary procedures and the attendant costs and stress.

Addition of Contrast-enhanced Mammography to Tomosynthesis for Breast Cancer Detection in Women with a Personal History of Breast Cancer: Prospective TOCEM Trial Interim Analysis.

Background Digital breast tomosynthesis (DBT) is often inadequate for screening women with a personal history of breast cancer (PHBC). The ongoing prospective Tomosynthesis or Contrast-Enhanced Mammography, or TOCEM, trial includes three annual screenings with both DBT and contrast-enhanced mammography (CEM). Purpose To perform interim assessment of cancer yield, stage, and recall rate when CEM is added to DBT in women with PHBC. Materials and Methods From October 2019 to December 2022, two radiologists interpreted both examinations: Observer 1 reviewed DBT first and then CEM, and observer 2 reviewed CEM first and then DBT. Effects of adding CEM to DBT on incremental cancer detection rate (ICDR), cancer type and node status, recall rate, and other performance characteristics of the primary radiologist decisions were assessed. Results Among the participants (mean age at entry, 63.6 years ± 9.6 [SD]), 1273, 819, and 227 women with PHBC completed year 1, 2, and 3 screening, respectively. For observer 1, year 1 cancer yield was 20 of 1273 (15.7 per 1000 screenings) for DBT and 29 of 1273 (22.8 per 1000 screenings; ICDR, 7.1 per 1000 screenings [95% CI: 3.2, 13.4]) for DBT plus CEM (P < .001). Year 2 plus 3 cancer yield was four of 1046 (3.8 per 1000 screenings) for DBT and eight of 1046 (7.6 per 1000 screenings; ICDR, 3.8 per 1000 screenings [95% CI: 1.0, 7.6]) for DBT plus CEM (P = .001). Year 1 recall rate for observer 1 was 103 of 1273 (8.1%) for (incidence) DBT alone and 187 of 1273 (14.7%) for DBT plus CEM (difference = 84 of 1273, 6.6% [95% CI: 5.3, 8.1]; P < .001). Year 2 plus 3 recall rate was 40 of 1046 (3.8%) for DBT and 92 of 1046 (8.8%) for DBT plus CEM (difference = 52 of 1046, 5.0% [95% CI: 3.7, 6.3]; P < .001). In 18 breasts with cancer detected only at CEM after integration of both observers, 13 (72%) cancers were invasive (median tumor size, 0.6 cm) and eight of nine (88%) with staging were N0. Among 1883 screenings with adequate reference standard, there were three interval cancers (one at the scar, two in axillae). Conclusion CEM added to DBT increased early breast cancer detection each year in women with PHBC, with an accompanying approximately 5.0%-6.6% recall rate increase. Clinical trial registration no. NCT04085510 © RSNA, 2024 Supplemental material is available for this article.

State of Academic Radiology: Current Challenges, Future Adaptations.

There are approximately 200 academic radiology departments in the United States. While academic medical centers vary widely depending on their size, complexity, medical school affiliation, research portfolio, and geographic location, they are united by their 3 core missions: patient care, education and training, and scholarship. Despite inherent differences, the current challenges faced by all academic radiology departments have common threads; potential solutions and future adaptations will need to be tailored and individualized-one size will not fit all. In this article, we provide an overview based on our experiences at 4 academic centers across the United States, from relatively small to very large size, and discuss creative and innovative ways to adapt, including community expansion, hybrid models of faculty in-person vs teleradiology (traditional vs non-traditional schedule), work-life integration, recruitment and retention, mentorship, among others.

Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk.

Background Background parenchymal enhancement (BPE) at dynamic contrast-enhanced (DCE) MRI of cancer-free breasts increases the risk of developing breast cancer; implications of quantitative BPE in ipsilateral breasts with breast cancer are largely unexplored. Purpose To determine whether quantitative BPE measurements in one or both breasts could be used to predict recurrence risk in women with breast cancer, using the Oncotype DX recurrence score as the reference standard. Materials and Methods This HIPAA-compliant retrospective single-institution study included women diagnosed with breast cancer between January 2007 and January 2012 (development set) and between January 2012 and January 2017 (internal test set). Quantitative BPE was automatically computed using an in-house-developed computer algorithm in both breasts. Univariable logistic regression was used to examine the association of BPE with Oncotype DX recurrence score binarized into high-risk (recurrence score >25) and low- or intermediate-risk (recurrence score ≤25) categories. Models including BPE measures were assessed for their ability to distinguish patients with high risk versus those with low or intermediate risk and the actual recurrence outcome. Results The development set included 127 women (mean age, 58 years ± 10.2 [SD]; 33 with high risk and 94 with low or intermediate risk) with an actual local or distant recurrence rate of 15.7% (20 of 127) at a minimum 10 years of follow-up. The test set included 60 women (mean age, 57.8 years ± 11.6; 16 with high risk and 44 with low or intermediate risk). BPE measurements quantified in both breasts were associated with increased odds of a high-risk Oncotype DX recurrence score (odds ratio range, 1.27-1.66 [95% CI: 1.02, 2.56]; P < .001 to P = .04). Measures of BPE combined with tumor radiomics helped distinguish patients with a high-risk Oncotype DX recurrence score from those with a low- or intermediate-risk score, with an area under the receiver operating characteristic curve of 0.94 in the development set and 0.79 in the test set. For the combined models, the negative predictive values were 0.97 and 0.93 in predicting actual distant recurrence and local recurrence, respectively. Conclusion Ipsilateral and contralateral DCE MRI measures of BPE quantified in patients with breast cancer can help distinguish patients with high recurrence risk from those with low or intermediate recurrence risk, similar to Oncotype DX recurrence score. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zhou and Rahbar in this issue.

Contrast-enhanced Mammography-guided Biopsy: Initial Trial and Experience.

OBJECTIVE: Evaluate lesion visibility and radiologist confidence during contrast-enhanced mammography (CEM)-guided biopsy. METHODS: Women with BI-RADS ≥4A enhancing breast lesions were prospectively recruited for 9-g vacuum-assisted CEM-guided biopsy. Breast density, background parenchymal enhancement (BPE), lesion characteristics (enhancement and conspicuity), radiologist confidence (scale 1-5), and acquisition times were collected. Signal intensities in specimens were analyzed. Patient surveys were collected. RESULTS: A cohort of 28 women aged 40-81 years (average 57) had 28 enhancing lesions (7/28, 25% malignant). Breast tissue was scattered (10/28, 36%) or heterogeneously dense (18/28, 64%) with minimal (12/28, 43%), mild (7/28, 25%), or moderate (9/28, 32%) BPE on CEM. Twelve non-mass enhancements, 11 masses, 3 architectural distortions, and 2 calcification groups demonstrated weak (12/28, 43%), moderate (14/28, 50%), or strong (2/28, 7%) enhancement. Specimen radiography demonstrated lesion enhancement in 27/28 (96%). Radiologists reported complete lesion removal on specimen radiography in 8/28 (29%). Average time from contrast injection to specimen radiography was 18 minutes (SD = 5) and, to post-procedure mammogram (PPM), 34 minutes (SD = 10). Contrast-enhanced mammography PPM was performed in 27/28 cases; 13/19 (68%) of incompletely removed lesions on specimen radiography showed residual enhancement; 6/19 (32%) did not. Across all time points, average confidence was 2.2 (SD = 1.2). Signal intensities of enhancing lesions were similar to iodine. Patients had an overall positive assessment. CONCLUSION: Lesion enhancement persisted through PPM and was visible on low energy specimen radiography, with an average "confident" score. Contrast-enhanced mammography-guided breast biopsy is easily implemented clinically. Its availability will encourage adoption of CEM.

Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.

Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings.

Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes.

BACKGROUND: The abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict the abundance of various cell types in the TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell types in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging. METHODS: We performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell type abundance, we performed linear regression on each radiomic feature/cell type abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell type infiltration status (i.e. "high" vs "low") prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models' performance was measured via area under the receiver operating characteristic curve (AUC). RESULTS: Univariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell type invasion predictions. CONCLUSIONS: On two independent breast cancer cohorts, breast MRI-derived radiomics are associated with the tumor's microenvironment in terms of the abundance of several cell types. Further evaluation with larger cohorts is needed.

Radiomics-informed Deep Curriculum Learning for Breast Cancer Diagnosis.

Convolutional Neural Networks (CNNs) are traditionally trained solely using the given imaging dataset. Additional clinical information is often available along with imaging data but is mostly ignored in the current practice of data-driven deep learning modeling. In this work, we propose a novel deep curriculum learning method that utilizes radiomics information as a source of additional knowledge to guide training using customized curriculums. Specifically, we define a new measure, termed radiomics score, to capture the difficulty of classifying a set of samples. We use the radiomics score to enable a newly designed curriculum-based training scheme. In this scheme, the loss function component is weighted and initialized by the corresponding radiomics score of each sample, and furthermore, the weights are continuously updated in the course of training based on our customized curriculums to enable curriculum learning. We implement and evaluate our methods on a typical computer-aided diagnosis of breast cancer. Our experiment results show benefits of the proposed method when compared to a direct use of radiomics model, a baseline CNN without using any knowledge, the standard curriculum learning using data resampling, an existing difficulty score from self-teaching, and previous methods that use radiomics features as additional input to CNN models.

Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study.

In this work, we assess how pre-training strategy affects deep learning performance for the task of distinguishing false-recall from malignancy and normal (benign) findings in digital mammography images. A cohort of 1303 breast cancer screening patients (4935 digital mammogram images in total) was retrospectively analyzed as the target dataset for this study. We assessed six different convolutional neural network model structures utilizing four different imaging datasets (total > 1.4 million images (including ImageNet); medical images different in terms of scale, modality, organ, and source) for pre-training on six classification tasks to assess how the performance of CNN models varies based on training strategy. Representative pre-training strategies included transfer learning with medical and non-medical datasets, layer freezing, varied network structure, and multi-view input for both binary and triple-class classification of mammogram images. The area under the receiver operating characteristic curve (AUC) was used as the model performance metric. The best performing model out of all experimental settings was an AlexNet model incrementally pre-trained on ImageNet and a large Breast Density dataset. The AUC for the six classification tasks using this model ranged from 0.68 to 0.77. In the case of distinguishing recalled-benign mammograms from others, four out of five pre-training strategies tested produced significant performance differences from the baseline model. This study suggests that pre-training strategy influences significant performance differences, especially in the case of distinguishing recalled- benign from malignant and benign screening patients.

Inaccurate Labels in Weakly-Supervised Deep Learning: Automatic Identification and Correction and Their Impact on Classification Performance.

In data-driven deep learning-based modeling, data quality may substantially influence classification performance. Correct data labeling for deep learning modeling is critical. In weakly-supervised learning, a challenge lies in dealing with potentially inaccurate or mislabeled training data. In this paper, we proposed an automated methodological framework to identify mislabeled data using two metric functions, namely, Cross-entropy Loss that indicates divergence between a prediction and ground truth, and Influence function that reflects the dependence of a model on data. After correcting the identified mislabels, we measured their impact on the classification performance. We also compared the mislabeling effects in three experiments on two different real-world clinical questions. A total of 10,500 images were studied in the contexts of clinical breast density category classification and breast cancer malignancy diagnosis. We used intentionally flipped labels as mislabels to evaluate the proposed method at a varying proportion of mislabeled data included in model training. We also compared the effects of our method to two published schemes for breast density category classification. Experiment results show that when the dataset contains 10% of mislabeled data, our method can automatically identify up to 98% of these mislabeled data by examining/checking the top 30% of the full dataset. Furthermore, we show that correcting the identified mislabels leads to an improvement in the classification performance. Our method provides a feasible solution for weakly-supervised deep learning modeling in dealing with inaccurate labels.

Contrast Enhanced Digital Mammography (CEDM) Helps to Safely Reduce Benign Breast Biopsies for Low to Moderately Suspicious Soft Tissue Lesions.

RATIONALE AND OBJECTIVES: To preliminarily asses if Contrast Enhanced Digital Mammography (CEDM) can accurately reduce biopsy rates for soft tissue BI-RADS 4A or 4B lesions. MATERIALS AND METHODS: Eight radiologists retrospectively and independently reviewed 60 lesions in 54 consenting patients who underwent CEDM under Health Insurance Portability and Accountability Act compliant institutional review board-approved protocols. Readers provided Breast Imaging Reporting & Data System ratings sequentially for digital mammography/digital breast tomosynthesis (DM/DBT), then with ultrasound, then with CEDM for each lesion. Area under the curve (AUC), true positive rates and false positive rates, positive predictive values and negative predictive values were calculated. Statistical analysis accounting for correlation between lesion-examinations and between-reader variability was performed using OR/DBM (for SAS v.3.0), generalized linear mixed model for binary data (proc glimmix, SAS v.9.4, SAS Institute, Cary North Carolina), and bootstrap. RESULTS: The cohort included 49 benign, two high-risk and nine cancerous lesions in 54 women aged 34-74 (average 50) years. Reader-averaged AUC for CEDM was significantly higher than DM/DBT alone (0.85 versus 0.66, p < 0.001) or with US (0.85 versus 0.75, p = 0.001). CEDM increased true positive rates from 0.74 under DB/DBT, and 0.89 with US, to 0.90 with CEDM, (p = 0.019 DM/DBT versus CEDM, p = 0.78 DM/DBT + US versus CEDM) and decreased false positive rates from 0.47 using DM/DBT and 0.61 with US to 0.39 with CEDM (p = 0.017 DM/DBT versus CEDM, p = 0.001 DM/DBT+ US versus CEDM). For an expected cancer rate of 10%, CEDM positive predictive values was 20.5% (95% CI: 16%-27%) and negative predictive values 98.3% (95% CI: 96%-100%). CONCLUSION: Addition of CEDM for evaluation of low-moderate suspicion soft tissue breast lesions can substantially reduce biopsy of benign lesions without compromising cancer detection.

Deep learning modeling using normal mammograms for predicting breast cancer risk.

PURPOSE: To investigate two deep learning-based modeling schemes for predicting short-term risk of developing breast cancer using prior normal screening digital mammograms in a case-control setting. METHODS: We conducted a retrospective Institutional Review Board-approved study on a case-control cohort of 226 patients (including 113 women diagnosed with breast cancer and 113 controls) who underwent general population breast cancer screening. For each patient, a prior normal (i.e., with negative or benign findings) digital mammogram examination [including mediolateral oblique (MLO) view and craniocaudal (CC) view two images] was collected. Thus, a total of 452 normal images (226 MLO view images and 226 CC view images) of this case-control cohort were analyzed to predict the outcome, i.e., developing breast cancer (cancer cases) or remaining breast cancer-free (controls) within the follow-up period. We implemented an end-to-end deep learning model and a GoogLeNet-LDA model and compared their effects in several experimental settings using two mammographic view images and inputting two different subregions of the images to the models. The proposed models were also compared to logistic regression modeling of mammographic breast density. Area under the receiver operating characteristic curve (AUC) was used as the model performance metric. RESULTS: The highest AUC was 0.73 [95% Confidence Interval (CI): 0.68-0.78; GoogLeNet-LDA model on CC view] when using the whole-breast and was 0.72 (95% CI: 0.67-0.76; GoogLeNet-LDA model on MLO + CC view) when using the dense tissue, respectively, as the model input. The GoogleNet-LDA model significantly (all P < 0.05) outperformed the end-to-end GoogLeNet model in all experiments. CC view was consistently more predictive than MLO view in both deep learning models, regardless of the input subregions. Both models exhibited superior performance than the percent breast density (AUC = 0.54; 95% CI: 0.49-0.59). CONCLUSIONS: The proposed deep learning modeling approach can predict short-term breast cancer risk using normal screening mammogram images. Larger studies are needed to further reveal the promise of deep learning in enhancing imaging-based breast cancer risk assessment.

Diagnostic Performance of MRI, Molecular Breast Imaging, and Contrast-enhanced Mammography in Women with Newly Diagnosed Breast Cancer.

Background Staging newly diagnosed breast cancer by using dynamic contrast material-enhanced MRI is limited by access, high cost, and false-positive findings. The utility of contrast-enhanced mammography (CEM) and 99mTc sestamibi-based molecular breast imaging (MBI) in this setting is largely unknown. Purpose To compare extent-of-disease assessments by using MRI, CEM, and MBI versus pathology in women with breast cancer. Materials and Methods In this HIPAA-compliant prospective study, women with biopsy-proven breast cancer underwent MRI, CEM, and MBI between October 2014 and April 2018. Eight radiologists independently interpreted each examination result prospectively and were blinded to interpretations of findings with the other modalities. Visibility of index malignancies, lesion size, and additional suspicious lesions (malignant or benign) were compared during pathology review. Accuracy of index lesion sizing and detection of additional lesions in women without neoadjuvant chemotherapy were compared. Results A total of 102 women were enrolled and 99 completed the study protocol (mean age, 51 years ± 11 [standard deviation]; range, 32-77 years). Lumpectomy or mastectomy was performed in 71 women (79 index malignancies) without neoadjuvant chemotherapy and in 28 women (31 index malignancies) with neoadjuvant chemotherapy. Of the 110 index malignancies, MRI, CEM, and MBI depicted 102 (93%; 95% confidence interval [CI]: 86%, 97%), 100 (91%; 95% CI: 84%, 96%), and 101 (92%; 95% CI: 85%, 96%) malignancies, respectively. In patients without neoadjuvant chemotherapy, pathologic size of index malignancies was overestimated with all modalities (P = .02). MRI led to overestimation of 24% (17 of 72) of malignancies by more than 1.5 cm compared with 11% (eight of 70) with CEM and 15% (11 of 72) with MBI. MRI depicted more (P = .007) nonindex lesions, with sensitivity similar to that of CEM or MBI, resulting in lower positive predictive value of additional biopsies (13 of 46 [28%; 95% CI: 17%, 44%] for MRI; 14 of 27 [52%; 95% CI: 32%, 71%] for CEM; and 11 of 25 [44%; 95% CI: 24%, 65%] for MBI (overall P = .01). Conclusion Contrast-enhanced mammography, molecular breast imaging, and MRI showed similar detection of all malignancies. MRI depicted more nonindex suspicious benign lesions than did contrast-enhanced mammography or molecular breast imaging, leading to lower positive predictive value of additional biopsies. All three modalities led to overestimation of index tumor size, particularly MRI. © RSNA, 2019 Online supplemental material is available for this article.

Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

PURPOSE: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy. EXPERIMENTAL DESIGN: Deep learning convolutional neural network (CNN) models were constructed to classify mammography images into malignant (breast cancer), negative (breast cancer free), and recalled-benign categories. A total of 14,860 images of 3,715 patients from two independent mammography datasets: Full-Field Digital Mammography Dataset (FFDM) and a digitized film dataset, Digital Dataset of Screening Mammography (DDSM), were used in various settings for training and testing the CNN models. The ROC curve was generated and the AUC was calculated as a metric of the classification accuracy. RESULTS: Training and testing using only the FFDM dataset resulted in AUC ranging from 0.70 to 0.81. When the DDSM dataset was used, AUC ranged from 0.77 to 0.96. When datasets were combined for training and testing, AUC ranged from 0.76 to 0.91. When pretrained on a large nonmedical dataset and DDSM, the models showed consistent improvements in AUC ranging from 0.02 to 0.05 (all P > 0.05), compared with pretraining only on the nonmedical dataset. CONCLUSIONS: This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls.

DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence.

We investigated automated quantitative measures of background parenchymal enhancement (BPE) derived from an early versus delayed post-contrast sequence in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for association with breast cancer presence in a case-control study. DCE-MRIs were retrospectively analyzed for 51 cancer cases and 51 controls with biopsy-proven benign lesions, matched by age and year-of-MRI. BPE was quantified using fully-automated validated computer algorithms, separately from three sequential DCE-MRI post-contrast-subtracted sequences (SUB1, SUB2, and SUB3). The association of BPE computed from the three SUBs and other known factors with breast cancer were assessed in terms of odds ratio (OR) and area under the receiver operating characteristic curve (AUC). The OR of breast cancer for the percentage BPE measure (BPE%) quantified from SUB1 was 3.5 (95% Confidence Interval: 1.3, 9.8; p = 0.015) for 20% increments. Slightly lower and statistically significant ORs were also obtained for BPE quantified from SUB2 and SUB3. There was no significant difference (p > 0.2) in AUC for BPE quantified from the three post-contrast sequences and their combination. Our study showed that quantitative measures of BPE are associated with breast cancer presence and the association was similar across three breast DCE-MRI post-contrast sequences.

Persistent Breast Pain Among Women With Histories of Breast-conserving Surgery for Breast Cancer Compared With Women Without Histories of Breast Surgery or Cancer.

OBJECTIVES: This study compared persistent breast pain among women who received breast-conserving surgery for breast cancer and women without a history of breast cancer. METHODS: Breast cancer survivors (n=200) were recruited at their first postsurgical surveillance mammogram (6 to 15 mo postsurgery). Women without a breast cancer history (n=150) were recruited at the time of a routine screening mammogram. All women completed measures of breast pain, pain interference with daily activities and intimacy, worry about breast pain, anxiety symptoms, and depression symptoms. Demographic and medical information were also collected. RESULTS: Persistent breast pain (duration ≥6 mo) was reported by 46.5% of breast cancer survivors and 12.7% of women without a breast cancer history (P<0.05). Breast cancer survivors also had significantly higher rates of clinically significant persistent breast pain (pain intensity score ≥3/10), as well as higher average breast pain intensity and unpleasantness scores. Breast cancer survivors with persistent breast pain had significantly higher levels of depressive symptoms, as well as pain worry and interference, compared with survivors without persistent breast pain or women without a breast cancer history. Anxiety symptoms were significantly higher in breast cancer survivors with persistent breast pain compared with women without a breast cancer history. DISCUSSION: Results indicate that persistent breast pain negatively impacts women with a history of breast-conserving cancer surgery compared with women without that history. Strategies to ameliorate persistent breast pain and to improve adjustment among women with persistent breast pain should be explored for incorporation into standard care for breast cancer survivors.

Core Breast Biopsies Showing Lobular Carcinoma In Situ Should Be Excised and Surveillance Is Reasonable for Atypical Lobular Hyperplasia.

OBJECTIVE: The purpose of this article is to determine the upgrade rate to ductal carcinoma in situ (DCIS) or invasive carcinoma at excision at the same site after percutaneous breast biopsy findings of atypical lobular hyperplasia (ALH) or lobular carcinoma in situ (LCIS) using current imaging and strict pathologic criteria. MATERIALS AND METHODS: From January 2006 through September 2013, 32,960 breast core biopsies were performed; 1084 (3.3%) core biopsies found ALH or classic LCIS. For 447 lesions in 433 women, this was the only high-risk lesion at that site, with no ipsilateral malignancy, and results of excision were available. RESULTS: Among the 447 lesions, 22 (4.9%) were malignant at excision, including 10 invasive carcinomas (two grade 2 and eight grade 1; all node negative) and 12 DCIS. The upgrade rate of LCIS was 9.3% (10/108; 95% CI, 5.1-16.2%) and that of ALH was 3.5% (12/339; 95% CI, 2.0-6.1%; p = 0.02). After excluding five cases with radiologic-pathologic discordance and reclassifying one core from ALH to LCIS at review, the upgrade rate for LCIS remained higher (8.4%; 9/107; 95% CI, 4.5-15.2%) than that for ALH (2.4%; 8/335; 95% CI, 1.2-4.6%; p = 0.01). CONCLUSION: Excision is recommended for LCIS on core biopsy because of its 8.4-9.3% upgrade rate. Excluding discordant cases, patients with other high-risk lesions or concurrent malignancy, the risk of upgrade of ALH was 2.4%. Surveillance at 6, 12, and 24 months can be performed in lieu of excision because a short delay in diagnosis of the few malignancies is not expected to cause harm.

Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer.

BACKGROUND: We investigated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement kinetic variables quantified from normal breast parenchyma for association with presence of breast cancer, in a case-control study. METHODS: Under a Health Insurance Portability and Accountability Act compliant and Institutional Review Board-approved protocol, DCE-MRI scans of the contralateral breasts of 51 patients with cancer and 51 controls (matched by age and year of MRI) with biopsy-proven benign lesions were retrospectively analyzed. Applying fully automated computer algorithms on pre-contrast and multiple post-contrast MR sequences, two contrast enhancement kinetic variables, wash-in slope and signal enhancement ratio, were quantified from normal parenchyma of the contralateral breasts of both patients with cancer and controls. Conditional logistic regression was employed to assess association between these two measures and presence of breast cancer, with adjustment for other imaging factors including mammographic breast density and MRI background parenchymal enhancement (BPE). The area under the receiver operating characteristic curve (AUC) was used to assess the ability of the kinetic measures to distinguish patients with cancer from controls. RESULTS: When both kinetic measures were included in conditional logistic regression analysis, the odds ratio for breast cancer was 1.7 (95 % CI 1.1, 2.8; p = 0.017) for wash-in slope variance and 3.5 (95 % CI 1.2, 9.9; p = 0.019) for signal enhancement ratio volume, respectively. These odds ratios were similar on respective univariate analysis, and remained significant after adjustment for menopausal status, family history, and mammographic density. While percent BPE was associated with an odds ratio of 3.1 (95 % CI 1.2, 7.9; p = 0.018), in multivariable analysis of the three measures, percent BPE was non-significant (p = 0.897) and the two kinetics measures remained significant. For the differentiation of patients with cancer and controls, the unadjusted AUC was 0.71 using a combination of the two measures, which significantly (p = 0.005) outperformed either measure alone (AUC = 0.65 for wash-in slope variance and 0.63 for signal enhancement ratio volume). CONCLUSIONS: Kinetic measures of wash-in slope and signal enhancement ratio quantified from normal parenchyma in DCE-MRI are jointly associated with presence of breast cancer, even after adjustment for mammographic density and BPE.

Recall Rate Reduction with Tomosynthesis During Baseline Screening Examinations: An Assessment From a Prospective Trial.

RATIONALE AND OBJECTIVES: Assess results of a prospective, single-site clinical study evaluating digital breast tomosynthesis (DBT) during baseline screening mammography. MATERIALS AND METHODS: Under an institutional review board-approved Health Insurance Portability and Accountability Act (HIPAA)-compliant protocol, consenting women between ages 34 and 56 years scheduled for their initial and/or baseline screening mammogram underwent both full field digital mammography (FFDM) and DBT. The FFDM and the FFDM plus DBT images were interpreted independently in a reader by mode balanced approach by two of 14 participating radiologists. A woman was recalled for a diagnostic work-up if either radiologist recommended a recall. We report overall recall rates and related diagnostic outcome from the 1080 participants. Proportion of recommended recalls (Breast Imaging Reporting and Data System 0) were compared using a generalized linear mixed model (SAS 9.3) with a significance level of P = .0294. RESULTS: The fraction of women without breast cancer recommended for recall using FFDM alone and FFDM plus DBT were 412 of 1074 (38.4%) and 274 of 1074 (25.5%), respectively (P < .001). Large inter-reader variability in terms of recall reduction was observed among the 14 readers; however, 11 of 14 readers recalled fewer women using FFDM plus DBT (5 with P < .015). Six cancers (four ductal carcinomas in situ [DCIS] and two invasive ductal carcinomas [IDC]) were detected. One IDC was detected only on DBT and one DCIS cancer was detected only on FFDM, whereas the remaining cancers were detected on both modalities. CONCLUSIONS: The use of FFDM plus DBT resulted in a significant decrease in recall rates during baseline screening mammography with no reduction in sensitivity.

Transvaginal Drainage of Pelvic Abscesses and Collections Using Transabdominal Ultrasound Guidance.

Objectives. To evaluate clinical outcomes following transvaginal catheter placement using transabdominal ultrasound guidance for management of pelvic fluid collections. Methods. A retrospective review was performed for all patients who underwent transvaginal catheter drainage of pelvic fluid collections utilizing transabdominal ultrasound guidance between July 2008 and July 2013. 24 consecutive patients were identified and 24 catheters were placed. Results. The mean age of patients was 48.1 years (range = 27-76 y). 88% of collections were postoperative (n = 21), 8% were from pelvic inflammatory disease (n = 2), and 4% were idiopathic (n = 1). Of the 24 patients, 83% of patients (n = 20) had previously undergone a hysterectomy and 1 patient (4%) was pregnant at the time of drainage. The mean volume of initial drainage was 108 mL (range = 5 to 570). Catheters were left in place for an average of 4.3 days (range = 1-17 d). Microbial sampling was performed in all patients with 71% (n = 17) returning a positive culture. All collections were successfully managed percutaneously. There were no technical complications. Conclusions. Transvaginal catheter drainage of pelvic fluid collections using transabdominal ultrasound guidance is a safe and clinically effective procedure. Appropriate percutaneous management can avoid the need for surgery.