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Ko A, Vo AM, Miller N, Liang A, Baumbach M, Riley Argue J, Manche N, Gonzalez L, Austin N, Carver P, Procell J, Elzein H, Pan M, Zeidan N, Kasper W, Speer S, Liang Y, Pettus BJ. The Use of Breast-specific Gamma Imaging as a Low-Cost Problem-Solving Strategy for Avoiding Biopsies in Patients With Inconclusive Imaging Findings on Mammography and Ultrasonography. JOURNAL OF BREAST IMAGING 2024; 6:502-512. [PMID: 39162574 DOI: 10.1093/jbi/wbae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Indexed: 08/21/2024]
Abstract
OBJECTIVE To evaluate the clinical performance and financial costs of breast-specific gamma imaging (BSGI) as a biopsy-reducing problem-solving strategy in patients with inconclusive diagnostic imaging findings. METHODS A retrospective analysis of all patients for whom BSGI was utilized for inconclusive imaging findings following complete diagnostic mammographic and sonographic evaluation between January 2013 and December 2018 was performed. Positive BSGI findings were correlated and biopsied with either US or stereotactic technique with confirmation by clip location and pathology. After a negative BSGI result, patients were followed for a minimum of 24 months or considered lost to follow-up and excluded (22 patients). Results of further imaging studies, biopsies, and pathology results were analyzed. Net savings of avoided biopsies were calculated based on average Medicare charges. RESULTS Four hundred and forty female patients from 30 to 95 years (mean 55 years) of age were included in our study. BSGI demonstrated a negative predictive value (NPV) of 98.4% (314/319) and a positive predictive value for biopsy of 35.5% (43/121). The overall sensitivity was 89.6% (43/48), and the specificity was 80.1% (314/392). In total, 78 false positive but only 5 false negative BSGI findings were identified. Six hundred and twenty-one inconclusive imaging findings were analyzed with BSGI and a total of 309 biopsies were avoided. Estimated net financial savings from avoided biopsies were $646 897. CONCLUSION In the management of patients with inconclusive imaging findings on mammography or ultrasonography, BSGI is a problem-solving imaging modality with high NPV that helps avoid costs of image-guided biopsies.
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Affiliation(s)
- Andrew Ko
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
| | - Alexander M Vo
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, Santa Clara Valley Medical Center, San Jose, CA, USA
| | - Nathaniel Miller
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
| | - Annie Liang
- Brown University School of Public Health, Providence, RI, USA
| | - Maia Baumbach
- Department of Biomedical Engineering, Georgia Tech, Atlanta, GA, USA
| | - Jay Riley Argue
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Nathaniel Manche
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Luis Gonzalez
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, University of Florida, Gainesville, FL, USA
| | - Nicholas Austin
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, Cleveland Clinic, Cleveland, OH, USA
| | - Philip Carver
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiological Sciences, Drexel University, Philadelphia, PA, USA
| | - Joseph Procell
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Imaging, University of Rochester, Rochester, NY, USA
| | - Hassan Elzein
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, Virginia Commonwealth University, Richmond, VA, USA
| | - Margaret Pan
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Nadine Zeidan
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, University of Texas Southwestern, Dallas, TX, USA
| | - William Kasper
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Radiology, Temple University, Philadelphia, PA, USA
| | - Samuel Speer
- Department of Medical Education, Riverside Regional Medical Center, Newport News, VA, USA
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR, USA
| | - Yizhi Liang
- Peninsula Radiological Associates, Newport News, VA, USA
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Ashoor M, Khorshidi A. Improving signal-to-noise ratio by maximal convolution of longitudinal and transverse magnetization components in MRI: application to the breast cancer detection. Med Biol Eng Comput 2024; 62:941-954. [PMID: 38100039 DOI: 10.1007/s11517-023-02994-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/07/2023] [Indexed: 02/22/2024]
Abstract
PURPOSE The extraction of information from images provided by medical imaging systems may be employed to obtain the specific objectives in the various fields. The quantity of signal to noise ratio (SNR) plays a crucial role in displaying the image details. The higher the SNR value, the more the information is available. METHODS In this study, a new function has been formulated using the appropriate suggestions on convolutional combination of the longitudinal and transverse magnetization components related to the relaxation times of T1 and T2 in MRI, where by introducing the distinct index on the maximum value of this function, the new maps are constructed toward the best SNR. Proposed functions were analytically simulated using Matlab software and evaluated with respect to various relaxation times. This proposed method can be applied to any medical images. For instance, the T1- and T2-weighted images of the breast indicated in the reference [35] were selected for modelling and construction of the full width at x maximum (FWxM) map at the different values of x-parameter from 0.01 to 0.955 at 0.035 and 0.015 intervals. The range of x-parameter is between zero and one. To determine the maximum value of the derived SNR, these intervals have been first chosen arbitrarily. However, the smaller this interval, the more precise the value of the x-parameter at which the signal to noise is maximum. RESULTS The results showed that at an index value of x = 0.325, the new map of FWxM (0.325) will be constructed with a maximum derived SNR of 22.7 compared to the SNR values of T1- and T2-maps by 14.53 and 17.47, respectively. CONCLUSION By convolving two orthogonal magnetization vectors, the qualified images with higher new SNR were created, which included the image with the best SNR. In other words, to optimize the adoption of MRI technique and enable the possibility of wider use, an optimal and cost-effective examination has been suggested. Our proposal aims to shorten the MRI examination to further reduce interpretation times while maintaining primary sensitivity. SIGNIFICANCE Our findings may help to quantitatively identify the primary sources of each type of solid and sequential cancer.
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Affiliation(s)
- Mansour Ashoor
- Radiation Applications Research School, Nuclear Science and Technology Research Institute, Tehran, Iran
| | - Abdollah Khorshidi
- Radiation Applications Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
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Baughan N, Douglas L, Giger ML. Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening. JOURNAL OF BREAST IMAGING 2022; 4:451-459. [PMID: 38416954 DOI: 10.1093/jbi/wbac052] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Indexed: 03/01/2024]
Abstract
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current state, and future of AI in breast cancer screening and diagnosis along with challenges involved in the development of AI systems. Although AI has been developing for interpretation tasks associated with breast cancer screening for decades, its potential to combat the subjective nature and improve the efficiency of human image interpretation is always expanding. The rapid advancement of computational power and deep learning has increased greatly in AI research, with promising performance in detection and classification tasks across imaging modalities. Most AI systems, based on human-engineered or deep learning methods, serve as concurrent or secondary readers, that is, as aids to radiologists for a specific, well-defined task. In the future, AI may be able to perform multiple integrated tasks, making decisions at the level of or surpassing the ability of humans. Artificial intelligence may also serve as a partial primary reader to streamline ancillary tasks, triaging cases or ruling out obvious normal cases. However, before AI is used as an independent, autonomous reader, various challenges need to be addressed, including explainability and interpretability, in addition to repeatability and generalizability, to ensure that AI will provide a significant clinical benefit to breast cancer screening across all populations.
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Affiliation(s)
- Natalie Baughan
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Lindsay Douglas
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Maryellen L Giger
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
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Mendez AM, Fang LK, Meriwether CH, Batasin SJ, Loubrie S, Rodríguez-Soto AE, Rakow-Penner RA. Diffusion Breast MRI: Current Standard and Emerging Techniques. Front Oncol 2022; 12:844790. [PMID: 35880168 PMCID: PMC9307963 DOI: 10.3389/fonc.2022.844790] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.
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Affiliation(s)
- Ashley M. Mendez
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Lauren K. Fang
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Claire H. Meriwether
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Summer J. Batasin
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Stéphane Loubrie
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Ana E. Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, CA, United States
| | - Rebecca A. Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, CA, United States,Department of Bioengineering, University of California San Diego, La Jolla, CA, United States,*Correspondence: Rebecca A. Rakow-Penner,
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