A review of artificial intelligence in mammography

Ha, Richard; Jairam, Meghan P.

doi:10.1016/j.clinimag.2022.05.005

Cited by 12 publications

(6 citation statements)

References 51 publications

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“…The AI CAD was successful at detecting masses with spiculated margins, suggesting that it is well suited to detect the subtle features of ILC in mammography. Many countries engage in double reading of mammograms to improve the sensitivity of detecting cancer [ 20 ]. Given the AI CAD’s sensitivity for ILC detection, it is possible that this technology could be used as a second reader or as part of a radiologist’s toolkit.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography

Arce,

Vijay,

Yim

et al. 2023

Cureus

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Introduction Early breast cancer detection with screening mammography has been shown to reduce mortality and improve breast cancer survival. This study aims to evaluate the ability of an artificial intelligence computer-aided detection (AI CAD) system to detect biopsy-proven invasive lobular carcinoma (ILC) on digital mammography. Methods This retrospective study reviewed mammograms of patients who were diagnosed with biopsy-proved ILC between January 1, 2017, and January 1, 2022. All mammograms were analyzed using cmAssist ® (CureMetrix, San Diego, California, United States), which is an AI CAD for mammography. The AI CAD sensitivity for detecting ILC on mammography was calculated and further subdivided by lesion type, mass shape, and mass margins. To account for the within-subject correlation, generalized linear mixed models were implemented to investigate the association between age, family history, and breast density and whether the AI detected a false positive or true positive. Odds ratios, 95% confidence intervals, and p-values were also calculated. Results A total of 124 patients with 153 biopsy-proven ILC lesions were included. The AI CAD detected ILC on mammography with a sensitivity of 80%. The AI CAD had the highest sensitivity for detecting calcifications (100%), masses with irregular shape (82%), and masses with spiculated margins (86%). However, 88% of mammograms had at least one false positive mark with an average number of 3.9 false positive marks per mammogram. Conclusion The AI CAD system evaluated was successful in marking the malignancy in digital mammography. However, the numerous annotations confounded the ability to determine its overall accuracy and this reduces its potential use in real-life practice.

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Section: Discussionmentioning

confidence: 99%

Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography

Arce,

Vijay,

Yim

et al. 2023

Cureus

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“…Currently, there is an enormous focus on deep learning techniques in medical imaging research (Yi et al 2019, Jairam and Ha 2022, Xun et al 2022. These methods have also been used to generate synthetic data.…”

Section: Use Of Artificial Intelligencementioning

confidence: 99%

The creation of breast lesion models for mammographic virtual clinical trials: a topical review

et al. 2023

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Simulated breast lesion models, including microcalcification clusters and masses, have been used in several studies. Realistic lesions are required for virtual clinical trials (VCT) to be representative of clinical performance. Lesions can be obtained using mathematical methods or patient data. Various approaches exist to embed these lesions into breast structures to create cancer cases. This article provides a review of the literature available on the development of lesion models, simulation methods to insert these lesions into background structures and their applications.

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“…Increased processing speeds made BTUS scanning and image processing times a clinical reality that launched DMT in 2010. The current SoftVue unit contains eight GPUs and could also facilitate the rapidly advancing field of artificial intelligence (AI), which has demonstrated improved mammographic mass detection and tissue differentiation that are beyond that of breast radiologists [ 4 , 5 ]. The new quantitative transmission US parameters of sound, speed, and attenuation emphasized tissue characterization, and included its theranostic uses in a 2003 patent submission, awarded in 2013 [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Breast Tomographic Ultrasound: The Spectrum from Current Dense Breast Cancer Screenings to Future Theranostic Treatments

Littrup,

Mehrmohammadi,

Duric

2024

Tomography

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This review provides unique insights to the scientific scope and clinical visions of the inventors and pioneers of the SoftVue breast tomographic ultrasound (BTUS). Their >20-year collaboration produced extensive basic research and technology developments, culminating in SoftVue, which recently received the Food and Drug Administration’s approval as an adjunct to breast cancer screening in women with dense breasts. SoftVue’s multi-center trial confirmed the diagnostic goals of the tissue characterization and localization of quantitative acoustic tissue differences in 2D and 3D coronal image sequences. SoftVue mass characterizations are also reviewed within the standard cancer risk categories of the Breast Imaging Reporting and Data System. As a quantitative diagnostic modality, SoftVue can also function as a cost-effective platform for artificial intelligence-assisted breast cancer identification. Finally, SoftVue’s quantitative acoustic maps facilitate noninvasive temperature monitoring and a unique form of time-reversed, focused US in a single theranostic device that actually focuses acoustic energy better within the highly scattering breast tissues, allowing for localized hyperthermia, drug delivery, and/or ablation. Women also prefer the comfort of SoftVue over mammograms and will continue to seek out less-invasive breast care, from diagnosis to treatment.

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A review of artificial intelligence in mammography

Cited by 12 publications

References 51 publications

Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography

Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography

The creation of breast lesion models for mammographic virtual clinical trials: a topical review

Breast Tomographic Ultrasound: The Spectrum from Current Dense Breast Cancer Screenings to Future Theranostic Treatments

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