1
|
Grover H, Grover SB, Leah Forman F, Khanna G, Margolies L. Multiple bilateral breast masses due to lymphoma metastases: A report of 2 cases highlighting the mammographic and sonographic features. Radiol Case Rep 2024; 19:357-369. [PMID: 38033677 PMCID: PMC10682527 DOI: 10.1016/j.radcr.2023.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/01/2023] [Accepted: 10/08/2023] [Indexed: 12/02/2023] Open
Abstract
Metastases within breast usually occur due to a primary malignancy in the contralateral breast. Breast metastases from extra mammary malignancies are known to be very rare and the primary tumors are reported to be melanoma, lung cancer, gastro-intestinal primary tumors, neuroendocrine tumors, sarcomas, ovarian tumors and lymphomas. Breast lymphomas comprise 0.04%-0.7% of all cases of breast malignancies and may manifest either as a primary or a secondary variety. A primary breast lymphoma is known to be more infrequent than a secondary breast lymphoma. In patients with breast metastases the usual clinical presentation is with multiple palpable masses and imaging evaluation is the mainstay for initial diagnosis. We report the imaging features seen in 2 almost identical cases of secondary breast lymphoma. At mammography, multiple, round to elliptical, sharply circumscribed, high-density masses were seen, in which spiculation, calcification and architectural distortion were conspicuously absent. On sonography, these round /oval masses were homogenously hypoechoic, sharply circumscribed, showed a thin echogenic rim with posterior acoustic enhancement and were moderately to profusely vascular on color Doppler examination. These imaging features should suggest the possible diagnosis of metastases from a hematogenous malignancy and an ultrasound guided biopsy should be performed. Once the etiology of lymphoma is confirmed, a rigorous multi- modality imaging work up to identify the primary site, stage the disease and document other sites of dissemination is warranted.
Collapse
Affiliation(s)
- Hemal Grover
- Department of Radiology, Icahn School of Medicine Mount Sinai, New York, NY, USA
| | - Shabnam Bhandari Grover
- Department of Radiology, School of Medical Sciences and Research, Sharda Hospital, Sharda University, Greater Noida, 201306, Uttar Pradesh, India
- Department of Radiology, VMMC and Safdarjung Hospital, New Delhi 110029, India
| | - Frimmit Leah Forman
- Department of Radiology, Icahn School of Medicine Mount Sinai, New York, NY, USA
| | - Geetika Khanna
- Department of Pathology, VMMC and Safdarjung Hospital, New Delhi 110029, India
| | - Laurie Margolies
- Department of Radiology, Icahn School of Medicine Mount Sinai, New York, NY, USA
| |
Collapse
|
2
|
Nguyen DL, Myers KS, Oluyemi E, Mullen LA, Panigrahi B, Rossi J, Ambinder EB. BI-RADS 3 Assessment on MRI: A Lesion-Based Review for Breast Radiologists. JOURNAL OF BREAST IMAGING 2022; 4:460-473. [DOI: 10.1093/jbi/wbac032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Indexed: 12/15/2022]
Abstract
Abstract
Unlike mammography and US, limited data exist to establish well-defined criteria for MRI findings that have a ≤2% likelihood of malignancy. Therefore, determining which findings are appropriate for a BI-RADS 3 assessment on MRI remains challenging and variable among breast radiologists. Emerging data suggest that BI-RADS 3 should be limited to baseline MRI examinations (or examinations with less than two years of prior comparisons) performed for high-risk screening and only used for masses with all of the typical morphological and kinetic features suggestive of a fibroadenoma or dominant enhancing T2 hypointense foci that is distinct from background parenchymal enhancement and without suspicious kinetics. This article presents an updated discussion of BI-RADS 3 assessment (probably benign) for breast MRI using current evidence.
Collapse
Affiliation(s)
- Derek L Nguyen
- Johns Hopkins Medicine, Russell H. Morgan Department of Radiology and Radiological Science , Baltimore, MD , USA
| | - Kelly S Myers
- Johns Hopkins Medicine, Russell H. Morgan Department of Radiology and Radiological Science , Baltimore, MD , USA
| | - Eniola Oluyemi
- Johns Hopkins Medicine, Russell H. Morgan Department of Radiology and Radiological Science , Baltimore, MD , USA
| | - Lisa A Mullen
- Johns Hopkins Medicine, Russell H. Morgan Department of Radiology and Radiological Science , Baltimore, MD , USA
| | - Babita Panigrahi
- Johns Hopkins Medicine, Russell H. Morgan Department of Radiology and Radiological Science , Baltimore, MD , USA
| | - Joanna Rossi
- Johns Hopkins Medicine, Russell H. Morgan Department of Radiology and Radiological Science , Baltimore, MD , USA
| | - Emily B Ambinder
- Johns Hopkins Medicine, Russell H. Morgan Department of Radiology and Radiological Science , Baltimore, MD , USA
| |
Collapse
|
3
|
Wu N, Huang Z, Shen Y, Park J, Phang J, Makino T, Gene Kim S, Cho K, Heacock L, Moy L, Geras KJ. Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms. J Digit Imaging 2021; 34:1414-1423. [PMID: 34731338 PMCID: PMC8669066 DOI: 10.1007/s10278-021-00530-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/12/2021] [Accepted: 10/15/2021] [Indexed: 10/19/2022] Open
Abstract
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false-positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography examinations from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.
Collapse
Affiliation(s)
- Nan Wu
- Center for Data Science, New York University, New York City, USA.
| | - Zhe Huang
- Center for Data Science, New York University, New York City, USA
| | - Yiqiu Shen
- Center for Data Science, New York University, New York City, USA
| | - Jungkyu Park
- Center for Data Science, New York University, New York City, USA
| | - Jason Phang
- Center for Data Science, New York University, New York City, USA
| | - Taro Makino
- Center for Data Science, New York University, New York City, USA
| | - S Gene Kim
- Department of Radiology, New York University School of Medicine, New York City, USA
- Perlmutter Cancer Center, NYU Langone Health, New York City, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York City, USA
| | - Kyunghyun Cho
- Center for Data Science, New York University, New York City, USA
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York City, USA
- CIFAR Associate Fellow, New York City, USA
| | - Laura Heacock
- Department of Radiology, New York University School of Medicine, New York City, USA
| | - Linda Moy
- Department of Radiology, New York University School of Medicine, New York City, USA
- Perlmutter Cancer Center, NYU Langone Health, New York City, USA
| | - Krzysztof J Geras
- Center for Data Science, New York University, New York City, USA
- Department of Radiology, New York University School of Medicine, New York City, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York City, USA
| |
Collapse
|