Faculty Bibliography

The coronavirus disease 2019 (COVID-19) pandemic is a global healthcare emergency. Although reverse transcriptase polymerase chain reaction (RT-PCR) is the reference standard method to identify patients with COVID-19 infection, chest radiographs and CT chest play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology (STR) collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations were performed by thoracic radiology subspecialists for all COVID positive thoracic CTs. The labeling schema was coordinated with other international consensus panels and COVID data annotation efforts, European Society of Medical Imaging Informatics (EUSOMII), the American College of Radiology (ACR) and the American Association of Physicists in Medicine (AAPM). Study level COVID classification labels for chest radiographs were annotated by three radiologists with majority vote adjudication by board certified radiologists. RICORD consists of 240 thoracic CT scans and 1,000 chest radiographs contributed from four international sites. We anticipate that the RICORD database will ideally lead to prediction models that can demonstrate sustained performance across populations and healthcare systems. See also the editorial by Bai and Thomasian.

Magnetic Resonance (MR) imaging is the most sensitive modality for breast cancer detection but is currently limited to screening women at high risk due to limited specificity and test accessibility. However, specificity of MR imaging improves with successive rounds of screening, and abbreviated approaches have the potential to increase access and decrease cost. There is growing evidence to support supplemental MR imaging in moderate-risk women, and current guidelines continue to evolve. Functional imaging has the potential to maximize survival benefit of screening. Leveraging MR imaging as a possible primary screening tool is therefore also being investigated in average-risk women.

Breast MR imaging is the most sensitive imaging method for the detection of breast cancer and detects more aggressive malignancies than mammography and ultrasound examination. Despite these advantages, breast MR imaging has low use rates for breast cancer screening. Abbreviated breast MR imaging, in which a limited number of breast imaging sequences are obtained, has been proposed as a way to solve cost and patient tolerance issues while preserving the high cancer detection rate of breast MR imaging. This review discusses abbreviated breast MR imaging, including protocols, multicenter clinical trial results, clinical workflow implementation challenges, and future directions.

Objective/UNASSIGNED:The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance. Methods/UNASSIGNED:-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping. Results/UNASSIGNED:= 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50-0.71) to 0.75 (95% CI: 0.65-0.84). Conclusion/UNASSIGNED:Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.

Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we propose a novel neural network model to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings. We apply the model to screening mammography interpretation: predicting the presence or absence of benign and malignant lesions. On the NYU Breast Cancer Screening Dataset, our model outperforms (AUC = 0.93) ResNet-34 and Faster R-CNN in classifying breasts with malignant findings. On the CBIS-DDSM dataset, our model achieves performance (AUC = 0.858) on par with state-of-the-art approaches. Compared to ResNet-34, our model is 4.1x faster for inference while using 78.4% less GPU memory. Furthermore, we demonstrate, in a reader study, that our model surpasses radiologist-level AUC by a margin of 0.11.

The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting response on pretreatment imaging. This article comprehensively reviews response assessment on breast MRI and highlights areas of ongoing research. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

Background The Eastern Cooperative Oncology Group and American College of Radiology Imaging Network Cancer Research Group A6702 multicenter trial helped confirm the potential of diffusion-weighted MRI for improving differential diagnosis of suspicious breast abnormalities and reducing unnecessary biopsies. A prespecified secondary objective was to explore the relative value of different approaches for quantitative assessment of lesions at diffusion-weighted MRI. Purpose To determine whether alternate calculations of apparent diffusion coefficient (ADC) can help further improve diagnostic performance versus mean ADC values alone for analysis of suspicious breast lesions at MRI. Materials and Methods This prospective trial (ClinicalTrials.gov identifier: NCT02022579) enrolled consecutive women (from March 2014 to April 2015) with a Breast Imaging Reporting and Data System category of 3, 4, or 5 at breast MRI. All study participants underwent standardized diffusion-weighted MRI (b = 0, 100, 600, and 800 sec/mm²). Centralized ADC measures were performed, including manually drawn whole-lesion and hotspot regions of interest, histogram metrics, normalized ADC, and variable b-value combinations. Diagnostic performance was estimated by using the area under the receiver operating characteristic curve (AUC). Reduction in biopsy rate (maintaining 100% sensitivity) was estimated according to thresholds for each ADC metric. Results Among 107 enrolled women, 81 lesions with outcomes (28 malignant and 53 benign) in 67 women (median age, 49 years; interquartile range, 41-60 years) were analyzed. Among ADC metrics tested, none improved diagnostic performance versus standard mean ADC (AUC, 0.59-0.79 vs AUC, 0.75; P = .02-.84), and maximum ADC had worse performance (AUC, 0.52; P < .001). The 25th-percentile ADC metric provided the best performance (AUC, 0.79; 95% CI: 0.70, 0.88), and a threshold using median ADC provided the greatest reduction in biopsy rate of 23.9% (95% CI: 14.8, 32.9; 16 of 67 BI-RADS category 4 and 5 lesions). Nonzero minimum b value (100, 600, and 800 sec/mm²) did not improve the AUC (0.74; P = .28), and several combinations of two b values (0 and 600, 100 and 600, 0 and 800, and 100 and 800 sec/mm²; AUC, 0.73-0.76) provided results similar to those seen with calculations of four b values (AUC, 0.75; P = .17-.87). Conclusion Mean apparent diffusion coefficient calculated with a two-b-value acquisition is a simple and sufficient diffusion-weighted MRI metric to augment diagnostic performance of breast MRI compared with more complex approaches to apparent diffusion coefficient measurement. © RSNA, 2020 Online supplemental material is available for this article.

Mastectomy may be performed to treat breast cancer or as a prophylactic approach in women with a high risk of developing breast cancer. In addition, mastectomies may be performed with or without reconstruction. Reconstruction approaches differ and may be autologous, involving a transfer of tissue (skin, subcutaneous fat, and muscle) from other parts of the body to the chest wall. Reconstruction may also involve implants. Implant reconstruction may occur as a single procedure or as multistep procedures with initial use of an adjustable tissue expander allowing the mastectomy tissues to be stretched without compromising blood supply. Ultimately, a full-volume implant will be placed. Reconstructions with a combination of autologous and implant reconstruction may also be performed. Other techniques such as autologous fat grafting may be used to refine both implant and flap-based reconstruction. This review of imaging in the setting of mastectomy with or without reconstruction summarizes the literature and makes recommendations based on available evidence. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.