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

Wolfson, Stacey; Kim, Eric; Plaunova, Anastasia; Bukhman, Rita; Sarmiento, Ruth D; Samreen, Naziya; Awal, Divya; Sheth, Monica M; Toth, Hildegard B; Moy, Linda; Reig, Beatriu
PMCID:8855316
PMID: 35133198
ISSN: 1527-1315
CID: 5156732

2021 Top Images in Radiology: Radiology In Training Editors' Choices [Editorial]

Deng, Francis; Kim, Eric; Trofimova, Anna V; Lee, Susanna I
PMID: 34846205
ISSN: 1527-1315
CID: 5065512

Radiology In Training: The Inaugural Year Amidst a Pandemic [Editorial]

Kim, Eric; Trofimova, Anna; Deng, Francis; Lee, Susanna I
PMID: 34313472
ISSN: 1527-1315
CID: 5043402

Lessons from the first DBTex Challenge

Park, Jungkyu; Shoshan, Yoel; Marti, Robert; Gómez del Campo, Pablo; Ratner, Vadim; Khapun, Daniel; Zlotnick, Aviad; Barkan, Ella; Gilboa-Solomon, Flora; ChÅ‚Ä™dowski, Jakub; Witowski, Jan; Millet, Alexandra; Kim, Eric; Lewin, Alana; Pysarenko, Kristine; Chen, Sardius; Goldberg, Julia; Patel, Shalin; Plaunova, Anastasia; Wegener, Melanie; Wolfson, Stacey; Lee, Jiyon; Hava, Sana; Murthy, Sindhoora; Du, Linda; Gaddam, Sushma; Parikh, Ujas; Heacock, Laura; Moy, Linda; Reig, Beatriu; Rosen-Zvi, Michal; Geras, Krzysztof J.
SCOPUS:85111105102
ISSN: 2522-5839
CID: 5000532

2020 Top Images in Radiology: Radiology In Training Editors' Choices [Editorial]

Trofimova, Anna V; Kim, Eric; Lee, Susanna I
PMID: 33258749
ISSN: 1527-1315
CID: 4709862

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Wu, Nan; Phang, Jason; Park, Jungkyu; Shen, Yiqiu; Huang, Zhe; Zorin, Masha; Jastrzebski, Stanislaw; Fevry, Thibault; Katsnelson, Joe; Kim, Eric; Wolfson, Stacey; Parikh, Ujas; Gaddam, Sushma; Lin, Leng Leng Young; Ho, Kara; Weinstein, Joshua D; Reig, Beatriu; Gao, Yiming; Pysarenko, Hildegard Toth Kristine; Lewin, Alana; Lee, Jiyon; Airola, Krystal; Mema, Eralda; Chung, Stephanie; Hwang, Esther; Samreen, Naziya; Kim, S Gene; Heacock, Laura; Moy, Linda; Cho, Kyunghyun; Geras, Krzysztof J
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.
PMID: 31603772
ISSN: 1558-254x
CID: 4130202

Preliminary study: Breast cancers can be well seen on 3T breast MRI with a half-dose of gadobutrol

Melsaether, Amy N; Kim, Eric; Mema, Eralda; Babb, James; Kim, Sungheon Gene
BACKGROUND:Dynamic contrast enhanced (DCE) breast MRI is highly sensitive for breast cancer and requires gadolinium-based contrast agents (GBCA)s, which have potential safety concerns. PURPOSE/OBJECTIVE:Test whether breast cancers imaged by 3T DCE breast MRI with 0.05 mmol/kg of gadobutrol are detectable. METHODS:Analysis of 3T DCE breast MRIs with half dose of gadobutrol from patients included in an IRB-approved and HIPPA-compliant prospective study of breast PET/MRI. Between 11/7/2014 and 3/2/2018, 41 consecutive women with biopsy-proven breast cancer that was at least 2 cm, multi-focal or multi-centric, had axillary metastasis, or had skin involvement who gave informed consent were included. Two breast radiologists independently recorded lesion conspicuity on a 4-point scale (0 = not seen, 1 = questionably seen, 2 = adequately seen, 3 = certainly seen), and measured the lesion. Size was compared between radiologists and with size on available mammogram, ultrasound, MRI, and surgical pathology. Inter-reader agreement was assessed by kappa coefficient for conspicuity. Lesion size comparisons were assessed using the Spearman rank correlation. RESULTS:In 40 patients (ages 28.4-80.5, 51.9 years), there were 49 cancers. 10.1% of lesions were 1 cm or less and 26.5% of lesions were 2 cm or less. Each reader detected 49/49 cancers. Conspicuity scores ranged from 2 to 3, mean 2.9/3 for both readers (p = 0.47). Size on half-dose 3T DCE-MRI correlated with size on surgical pathology (r = 0.6, p = 0.03) while size on mammogram and ultrasound did not (r = 0.25, p = 0.46; r = 0.25, p = 0.42). CONCLUSION/CONCLUSIONS:All breast cancers in this cohort, as small as 0.4 cm, were seen on 3T DCE breast MRI with 0.05 mmol/kg dose of gadobutrol.
PMID: 31279989
ISSN: 1873-4499
CID: 3976302

PET/MRI in Breast Cancer

Pujara, Akshat C; Kim, Eric; Axelrod, Deborah; Melsaether, Amy N
Positron emission tomography / magnetic resonance imaging (PET/MRI) is an emerging imaging technology that allows for the acquisition of multiple MRI parameters simultaneously with PET data. In this review, we address the technical requirements of PET/MRI including protocols and tracers, the potential of integrated localized breast PET/MRI exams, and possible applications of whole-body PET/MRI in breast cancer patients. Currently, PET/MRI can be performed on sequential and integrated PET/MRI scanners but, as not all practices can access these dedicated machines, several studies look at PET and MRI exams that are performed separately on separate scanners within a short time frame. This practice likely provides similar clinical data, although exact colocalization for iso-voxel analysis, currently performed only in research, is not possible. In PET/MRI, the MRI sequences are flexible and can be customized according to the aim of the exam. The most commonly used radiotracer is 18 F-FDG; however, tracers that image hypoxia and drug targets such as estrogen receptors and HER2 are in development and may increase the utility of PET/MRI. For dedicated breast PET/MRI, a potential advantage over standard breast MRI alone may be the complementary sensitivities of MRI for extent of disease within the breast and PET for axillary and internal mammary nodal metastases. Moreover, layers of multiparametric MRI and PET metrics derived from the index lesion are being investigated as predictors of response to neoadjuvant therapy. These data may eventually be able to be quantified and mined in a way that furthers radiomics and also precision medicine. Finally, in whole-body imaging of breast cancer patients, single-institution studies have found that PET/MRI detects more metastases than PET at about half the radiation dose, although a survival benefit has not been shown. For now, whole-body PET/MRI in breast cancer patients may be most relevant for young patients who may undergo serial surveillance exams.
PMID: 30291656
ISSN: 1522-2586
CID: 3329372

Preliminary analysis: Background parenchymal 18F-FDG uptake in breast cancer patients appears to correlate with background parenchymal enhancement and to vary by distance from the index cancer

Kim, Eric; Mema, Eralda; Axelrod, Deborah; Sigmund, Eric; Kim, Sungheon Gene; Babb, James; Melsaether, Amy N
PURPOSE/OBJECTIVE:To investigate how breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography/ magnetic resonance imaging (PET/MRI) in patients with breast cancer is related to background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT), and age, as well as whether BPU varies as a function of distance from the primary breast cancer. MATERIALS AND METHODS/METHODS:volume of interest 1) in the same quadrant of the ipsilateral breast, 5 mm from the index lesion; 2) in the opposite quadrant of the ipsilateral breast; and 3) in contralateral breast, quadrant matched to the opposite quadrant of the ipsilateral breast. The maximum standardized uptake value (SUVmax) of the index cancer was measured using a VOI that included the entire volume of the index lesion. Bleed from the primary tumor was corrected for (PET edge, MIM). FGT and BPE was assessed by 2 readers on a 4-point scale in accordance with BI-RADS lexicon. The Wilcoxon signed rank test and the Spearman rank correlation test were performed. RESULTS:BPU was significantly greater in the same quadrant as the breast cancer as compared with the opposite quadrant of the same breast (p < 0.001 for both readers) and was significantly greater in the opposite quadrant of the same breast compared to the matched quadrant of the contralateral breast (p = 0.002 for reader 1 and <0.001 for reader 2). While the FGT SUVmax in the same quadrant as the cancer correlated significantly with SUVmax of the index lesion, the FGT SUVmax in the opposite quadrant of the same breast and in the matched quadrant of the contralateral breast did not. The FGT SUVmax in the contralateral breast positively correlated with the degree of BPE and negatively correlated with age, but did not show a significant correlation with the amount of FGT for either reader. CONCLUSION/CONCLUSIONS:There appears to be an inverse correlation between metabolic activity of normal breast parenchyma and distance from the index cancer. BPU significantly correlates with BPE.
PMID: 30599855
ISSN: 1872-7727
CID: 3562812

Breast density classification with deep convolutional neural networks

Wu, Nan; J.Geras, Krzysztof; Shen, Yiqiu, Su, Jingyi; Kim, S.Gene; Kim, Eric; Wolfson, Stacey, Moy, Linda; Cho, Kyunghyun
ORIGINAL:0017085
ISSN: 2379-190x
CID: 5573552