Faculty Bibliography

PURPOSE: To compare a novel multicoil compressed sensing technique with flexible temporal resolution, golden-angle radial sparse parallel (GRASP), to conventional fat-suppressed spoiled three-dimensional (3D) gradient-echo (volumetric interpolated breath-hold examination, VIBE) MRI in evaluating the conspicuity of benign and malignant breast lesions. MATERIALS AND METHODS: Between March and August 2015, 121 women (24-84 years; mean, 49.7 years) with 180 biopsy-proven benign and malignant lesions were imaged consecutively at 3.0 Tesla in a dynamic contrast-enhanced (DCE) MRI exam using sagittal T1-weighted fat-suppressed 3D VIBE in this Health Insurance Portability and Accountability Act-compliant, retrospective study. Subjects underwent MRI-guided breast biopsy (mean, 13 days [1-95 days]) using GRASP DCE-MRI, a fat-suppressed radial "stack-of-stars" 3D FLASH sequence with golden-angle ordering. Three readers independently evaluated breast lesions on both sequences. Statistical analysis included mixed models with generalized estimating equations, kappa-weighted coefficients and Fisher's exact test. RESULTS: All lesions demonstrated good conspicuity on VIBE and GRASP sequences (4.28 +/- 0.81 versus 3.65 +/- 1.22), with no significant difference in lesion detection (P = 0.248). VIBE had slightly higher lesion conspicuity than GRASP for all lesions, with VIBE 12.6% (0.63/5.0) more conspicuous (P < 0.001). Masses and nonmass enhancement (NME) were more conspicuous on VIBE (P < 0.001), with a larger difference for NME (14.2% versus 9.4% more conspicuous). Malignant lesions were more conspicuous than benign lesions (P < 0.001) on both sequences. CONCLUSION: GRASP DCE-MRI, a multicoil compressed sensing technique with high spatial resolution and flexible temporal resolution, has near-comparable performance to conventional VIBE imaging for breast lesion evaluation. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2016.

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. (i) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. (ii) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. (iii) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. (iv) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breastcancerclassifier.

Invasive lobular carcinoma (ILC) is the second-most common histologic subtype of breast cancer, constituting 5% to 15% of all breast cancers. It is characterized by an infiltrating growth pattern that may decrease detectability on mammography and US. The use of digital breast tomosynthesis (DBT) improves conspicuity of ILC, and sensitivity is 80% to 88% for ILC. Sensitivity of mammography is lower in dense breasts, and breast tomosynthesis has better sensitivity for ILC in dense breasts compared with digital mammography (DM). Screening US identifies additional ILCs even after DBT, with a supplemental cancer detection rate of 0 to 1.2 ILC per 1000 examinations. Thirteen percent of incremental cancers found by screening US are ILCs. Breast MRI has a sensitivity of 93% for ILC. Abbreviated breast MRI also has high sensitivity but may be limited due to delayed enhancement in ILC. Contrast-enhanced mammography has improved sensitivity for ILC compared with DM, with higher specificity than breast MRI. In summary, supplemental screening modalities increase detection of ILC, with MRI demonstrating the highest sensitivity.

The fastMRI breast dataset is the first large-scale dataset of radial k-space and Digital Imaging and Communications in Medicine data for breast dynamic contrast-enhanced MRI with case-level labels, and its public availability aims to advance fast and quantitative machine learning research.

Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most existing literature reviews focusing solely on LLMs, this work examines both LLMs and MLLMs, highlighting their potential to support radiology workflows such as report generation, image interpretation, EHR summarization, differential diagnosis generation, and patient education. By streamlining these tasks, LLMs and MLLMs could reduce radiologist workload, improve diagnostic accuracy, support interdisciplinary collaboration, and ultimately enhance patient care. We also discuss key limitations, such as the limited capacity of current MLLMs to interpret 3D medical images and to integrate information from both image and text data, as well as the lack of effective evaluation methods. Ongoing efforts to address these challenges are introduced.

PURPOSE/OBJECTIVE:To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. METHODS: RESULTS: CONCLUSION/CONCLUSIONS:We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

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).

Breast MRI has high sensitivity and negative predictive value, making it well suited to problem solving when other imaging modalities or physical examinations yield results that are inconclusive for the presence of breast cancer. Indications for problem-solving MRI include equivocal or uncertain imaging findings at mammography and/or US; suspicious nipple discharge or skin changes suspected to represent an abnormality when conventional imaging results are negative for cancer; lesions categorized as Breast Imaging Reporting and Data System 4, which are not amenable to biopsy; and discordant radiologic-pathologic findings after biopsy. MRI should not precede or replace careful diagnostic workup with mammography and US and should not be used when a biopsy can be safely performed. The role of MRI in characterizing calcifications is controversial, and management of calcifications should depend on their mammographic appearance because ductal carcinoma in situ may not appear enhancing on MR images. In addition, ductal carcinoma in situ detected solely with MRI is not associated with a higher likelihood of an upgrade to invasive cancer compared with ductal carcinoma in situ detected with other modalities. MRI for triage of high-risk lesions is a subject of ongoing investigation, with a possible future role for MRI in decreasing excisional biopsies. The accuracy of MRI is likely to increase with the use of advanced techniques such as deep learning, which will likely expand the indications for problem-solving MRI. ^©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

OBJECTIVE:To test the performance of a novel machine learning-based breast density tool. The tool utilizes a convolutional neural network to predict the BI-RADS based density assessment of a study. The clinical density assessments of 33,000 mammographic examinations (164,000 images) from one academic medical center (Site A) were used for training. MATERIALS AND METHODS/METHODS:This was an IRB approved HIPAA compliant study performed at two academic medical centers. The validation data set was composed of 500 studies from one site (Site A) and 700 from another (Site B). At Site A, each study was assessed by three breast radiologists and the majority (consensus) assessment was used as truth. At Site B, if the tool agreed with the clinical reading, then it was considered to have correctly predicted the clinical reading. In cases where the tool and the clinical reading disagreed, then the study was evaluated by three radiologists and the consensus reading was used as the clinical reading. RESULTS:For the classification into the four categories of the Breast Imaging Reporting and Data System (BI-RADS®), the AI classifier had an accuracy of 84.6% at Site A and 89.7% at Site B. For binary classification (dense vs. non-dense), the AI classifier had an accuracy of 94.4% at Site A and 97.4% at Site B. In no case did the classifier disagree with the consensus reading by more than one category. CONCLUSIONS:The automated breast density tool showed high agreement with radiologists' assessments of breast density.