An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects.

3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D. We propose an effective alternative, a neural network that enables efficient classification of full-resolution 3D medical images. Compared to off-the-shelf convolutional neural networks, our network, 3D Globally-Aware Multiple Instance Classifier (3D-GMIC), uses 77.98%-90.05% less GPU memory and 91.23%-96.02% less computation. While it is trained only with image-level labels, without segmentation labels, it explains its predictions by providing pixel-level saliency maps. On a dataset collected at NYU Langone Health, including 85,526 patients with full-field 2D mammography (FFDM), synthetic 2D mammography, and 3D mammography, 3D-GMIC achieves an AUC of 0.831 (95% CI: 0.769-0.887) in classifying breasts with malignant findings using 3D mammography. This is comparable to the performance of GMIC on FFDM (0.816, 95% CI: 0.737-0.878) and synthetic 2D (0.826, 95% CI: 0.754-0.884), which demonstrates that 3D-GMIC successfully classified large 3D images despite focusing computation on a smaller percentage of its input compared to GMIC. Therefore, 3D-GMIC identifies and utilizes extremely small regions of interest from 3D images consisting of hundreds of millions of pixels, dramatically reducing associated computational challenges. 3D-GMIC generalizes well to BCS-DBT, an external dataset from Duke University Hospital, achieving an AUC of 0.848 (95% CI: 0.798-0.896).

Comparison of lesion detection and conspicuity between narrow-angle and wide-angle digital breast tomosynthesis for dense and non-dense breasts.

Digital breast tomosynthesis (DBT) has been shown to improve both sensitivity and specificity for breast cancer detection compared to full-field digital mammography. However, its performance could be limited for patients with dense breasts. Clinical DBT systems vary in their system designs, one of which is the acquisition angular range (AR), which leads to varied performance for different imaging tasks. In this study, we aim to compare DBT systems with different AR. We used a previously validated cascaded linear system model to investigate the dependence of in-plane breast structural noise (BSN) and detectability of masses on AR. We conducted a pilot clinical study to compare the lesion conspicuity between clinical DBT systems with the narrowest and the widest AR. Patients called back for diagnostic imaging on suspicious findings were imaged with both narrow-angle (NA) and wide-angle (WA) DBT. We analyzed the BSN for clinical images using noise power spectrum (NPS) analysis. A 5-point Likert scale was used in the reader study to compare the lesion conspicuity. Our theoretical calculation results show that increasing AR leads to reduced BSN and improved mass detectability. The NPS analysis on clinical images shows the lowest BSN for WA DBT. The WA DBT provides better lesion conspicuity for masses and asymmetries and shows a greater advantage for non-microcalcification lesions in dense breasts. The NA DBT provides better characterizations for microcalcifications. The WA DBT can downgrade false-positive findings seen on NA DBT. In conclusion, WA DBT could improve the detection of masses and asymmetries for patients with dense breasts.

Axillary Adenopathy after COVID-19 Vaccine: No Reason to Delay Screening Mammogram.

Online supplemental material is available for this article.

Impact of COVID-19 on Radiology Faculty - An Exacerbation of Gender Differences in Unpaid Home Duties and Professional Productivity.

RATIONALE AND OBJECTIVES: The COVID-19 pandemic stresses the tenuous balance between domestic obligations and academic output for women across professions. Our investigation aims to evaluate the impact of the pandemic on the home duties and workplace productivity of academic radiologists with respect to gender. MATERIALS AND METHODS: A 49-question survey was distributed to 926 members of Association of University Radiologists in October 2020. Several categories were addressed: demographics; workplace changes; stress levels and personal experiences with illness; time spent on domestic obligations; and perception of productivity during COVID-19. Statistical analyses were performed using SAS version 9.4 software (SAS Institute, Cary, NC). RESULTS: A total of 96 responses across 30 states, 53.1% male and 46.9% female were received. Women report spending more time on unpaid domestic duties than men prior to COVID-19, with men spending a median of 5-10 h/wk and women spending a median of 10-15 h/wk (p = 0.043). With pandemic onset, both genders reported that women did more of the homecare, when not split equally. Women with young children reported a significant decrease in work-from-home productivity compared to men with young children (p = 0.007). Men reported they had more time to be productive compared to women (p = 0.012). CONCLUSION: The COVID-19 pandemic threatens to disrupt the advancement of women in radiology leadership roles by creating disparate effects on productivity due to increased workloads at home for women. This could potentially lead to decreases in promotions and research productivity in years to come that far outlast the acute phases of the pandemic.

Ductal Carcinoma In Situ and Progression to Invasive Cancer: A Review of the Evidence.

Ductal carcinoma in situ (DCIS), breast cancer confined to the milk ducts, is a heterogeneous entity. The question of how and when a case of DCIS will extend beyond the ducts to become invasive breast cancer has implications for both patient prognosis and optimal treatment approaches. The natural history of DCIS has been explored through a variety of methods, from mouse models to biopsy specimen reviews to population-based screening data to modeling studies. This article will review the available evidence regarding progression pathways and will also summarize current trials designed to assess DCIS progression.

Surgery for a quadricuspid aortic valve: case report and comprehensive review of the literature.

Quadricuspid aortic valve (QAV) is a rare cardiac anomaly which can present with clinically significant regurgitation. The case is presented of a 38-year-old female patient with a regurgitant QAV managed surgically. A review of the current literature relating to QAV is also provided. The most common valve type that is operated on is type B, thus separating the surgical population from that of all QAVs, in which type A is most common. Moreover, aortic aneurysms were found to be a common and previously unrecognized significant characteristic among QAV patients. The majority of patients with a regurgitant QAV undergo replacement, although repairs have recently gained popularity. To date, the outcomes for both groups appear similar.