Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy. © RSNA, 2018 Online supplemental material is available for this article.

Objective. DNA sequencing tests are enabling physicians to interrogate the molecular profiles of patients' tumors, but most oncologists have not been trained in advanced genomics. We initiated a molecular tumor board to provide expert multidisciplinary input for these patients. Materials and Methods. A team that included clinicians, basic scientists, geneticists, and bioinformatics/pathway scientists with expertise in various cancer types attended. Molecular tests were performed in a Clinical Laboratory Improvement Amendments environment. Results. Patients (n 5 34, since December 2012) had received a median of three prior therapies. The median time from physician order to receipt of molecular diagnostic test results was 27 days (range: 14-77 days). Patients had a median of 4 molecular abnormalities (range: 1-14 abnormalities) found by next-generation sequencing (182-or 236-gene panels).Seventy-four genes were involved, with 123 distinct abnormalities. Importantly, no two patients had the same aberrations, and 107 distinct abnormalities were seen only once. Among the 11 evaluable patients whose treatment had been informed by molecular diagnostics, 3 achieved partial responses (progression-free survival of 3.4 months, $6.5 months, and 7.6 months).The most common reasons for being unable to act on the molecular diagnostic results were that patients were ineligible for or could not travel to an appropriately targeted clinical trial and/or that insurance would not cover the cognate agents. Conclusion. Genomic sequencing is revealing complex molecular profiles that differ by patient. Multidisciplinary molecular tumor boards may help optimize management. Barriers to personalized therapy include access to appropriately targeted drugs. The Oncologist 2014;19:631-636Implications for Practice: Our study relates our experience with the initiation of molecular tumor board meetings, which are a new vehicle for managing patients with complex malignancies on whom molecular diagnostics have been performed. This experience could be of significant importance to oncologists who are increasingly faced with advanced molecular diagnostic data, yet have minimal training in genomics. Our article should help clinicians to handle practical issues related to setting up and efficiently utilizing molecular tumor board meetings. We also aim at helping oncologists and health care systems understand and address practical, logistical, and scientific issues, such as the challenges associated with interpretation of molecular testing for patients with advanced cancer. INTRODUCTIONTechnological developments in genomic sequencing are advancing at a breathtaking rate. These tests are rapidly being made available in the clinic, potentially facilitating a personalized treatment strategy [1][2][3][4]. The collaboration between biologists who interpret and confirm the functional relevance of molecular abnormalities and clinicians who assess relationships to cancer prognosis and response to therapy has led to the discovery of the activity of molecu...

Initially developed in 1993, the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon serves to standardize breast imaging reports, improve communication with referring physicians, and provide a quality assurance tool. The long-awaited BI-RADS fifth edition consolidates, improves, and expands the lexicon for mammography, breast ultrasonography (US), and breast magnetic resonance (MR) imaging. The new edition has increased the number of imaging examples to nearly 600. The breast MR imaging lexicon is significantly expanded since it first appeared in the fourth edition. New terms have been added to the US lexicon to reflect technologic advances. Minor but important changes have been made to the mammography section. Calcification descriptors in the lexicon are now consolidated into two categories: benign and suspicious. The controversial "intermediate concern" grouping has been eliminated, and a table in the lexicon summarizes the literature supporting the recommendation to biopsy such calcifications. New descriptors such as "developing asymmetry" are illustrated, and abstracts are provided to reference their significance. A generous guidance section is included after the lexicon description for each modality. Useful frequently asked questions are succinctly answered, and the literature to support each answer is included in the reference section for each modality. This review article illustrates and highlights changes to the BI-RADS lexicon and provides readers with a general overview to familiarize them with the fifth edition. (©)RSNA, 2016.

Purpose We propose a deep learning‐based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol. Methods Several transfer learning techniques are employed to develop classifiers based on a set of 882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convert those images to red, green, blue (RGB) to more efficiently utilize the discriminative power of the convolutional neural network pretrained on the ImageNet dataset. We present how this conversion can be determined during fine‐tuning using back‐propagation. Next, we compare the performance of the transfer learning techniques with and without the color conversion. To show the usefulness of our approach, we additionally evaluate it using two publicly available datasets. Results Color conversion increased the areas under the receiver operating curve for each transfer learning method. For the better‐performing approach utilizing the fine‐tuning and the matching layer, the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves for the radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two separate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890. Conclusions The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to help radiologists with breast mass classification in ultrasound.

In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To address this issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKs was to adjust network’s receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions. The proposed method was developed and evaluated using US images collected from 882 breast masses. Moreover, we used three datasets of US images collected at different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Net achieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluated on three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improved mean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman’s rank coefficient of 0.7, between the utilization of dilated convolutions and breast mass size in the case of network’s expansion path. Our study shows the usefulness of deep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weights can be found at github.com/mbyr/bus_seg .

Background Quantitative diffusion‐weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker. Purpose To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi‐institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures. Study Type Prospective. Subjects In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer. Field Strength/Sequence DWI was acquired before and after patient repositioning using a four b‐value, single‐shot echo‐planar sequence at 1.5T or 3.0T. Assessment A QA procedure by trained operators assessed artifacts, fat suppression, and signal‐to‐noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast‐enhanced images. Twenty cases were evaluated multiple times to assess intra‐ and interoperator variability. Segmentation similarity was assessed via the Sørenson–Dice similarity coefficient. Statistical Tests Repeatability and reproducibility were evaluated using within‐subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients. Results In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane‐based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10−3 mm2/sec (95% CI 0.13, 0.19). The results were similar across field strengths and timepoint subgroups. Reproducibility was excellent: interreader ICC = 0.92 (95% CI 0.80, 0.97) and intrareader ICC = 0.91 (95% CI 0.78, 0.96). Data Conclusion Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi‐institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617–1628.

In anticipation of breast density notification legislation in the state of California, which would require notification of women with heterogeneously and extremely dense breast tissue, a working group of breast imagers and breast cancer risk specialists was formed to provide a common response framework. The California Breast Density Information Group identified key elements and implications of the law, researching scientific evidence needed to develop a robust response. In particular, issues of risk associated with dense breast tissue, masking of cancers by dense tissue on mammograms, and the efficacy, benefits, and harms of supplementary screening tests were studied and consensus reached. National guidelines and peer-reviewed published literature were used to recommend that women with dense breast tissue at screening mammography follow supplemental screening guidelines based on breast cancer risk assessment. The goal of developing educational materials for referring clinicians and patients was reached with the construction of an easily accessible Web site that contains information about breast density, breast cancer risk assessment, and supplementary imaging. This multi-institutional, multidisciplinary approach may be useful for organizations to frame responses as similar legislation is passed across the United States. Online supplemental material is available for this article.

Ability to visualize breast lesion vascularity and quantify the vascular heterogeneity using contrast-enhanced 3-D harmonic (HI) and subharmonic (SHI) ultrasound imaging was investigated in a clinical population. Patients (n = 134) identified with breast lesions on mammography were scanned using power Doppler imaging, contrast-enhanced 3-D HI, and 3-D SHI on a modified Logiq 9 scanner (GE Healthcare). A region of interest corresponding to ultrasound contrast agent flow was identified in 4D View (GE Medical Systems) and mapped to Author Manuscript raw slice data to generate a map of time-intensity curves for the lesion volume. Time points corresponding to baseline, peak intensity, and washout of ultrasound contrast agent were identified and used to generate and compare vascular heterogeneity plots for malignant and benign lesions. Vascularity was observed with power Doppler imaging in 84 lesions (63 benign and 21 malignant). The 3-D HI showed flow in 8 lesions (5 benign and 3 malignant), whereas 3-D SHI visualized flow in 68 lesions (49 benign and 19 malignant). Analysis of vascular heterogeneity in the 3-D SHI volumes found benign lesions having a significant difference in vascularity between central and peripheral sections (1.71 ± 0.96 vs. 1.13 ± 0.79 dB, p < 0.001, respectively), whereas malignant lesions showed no difference (1.66 ± 1.39 vs. 1.24 ± 1.14 dB, p = 0.24), indicative of more vascular coverage. These preliminary results suggest quantitative evaluation of vascular heterogeneity in breast lesions using contrast-enhanced 3-D SHI is feasible and able to detect variations in vascularity between central and peripheral sections for benign and malignant lesions. HHS Public Access