Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive                     Breast Cancer

Chung, Maggie; Calabrese, Evan; Mongan, John; Ray, Kimberly M.; Hayward, Jessica H.; Kelil, Tatiana; Sieberg, Ryan T.; Hylton, Nola M.; Joe, Bonnie N.; Lee, Amie Y.

doi:10.1148/radiol.213199

Cited by 21 publications

(15 citation statements)

References 20 publications

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“…Using non-enhanced MRI acquisitions for deriving vCE images is an emerging field of research in breast MRI (20–25). Different approaches have been suggested, with many studies focusing on morphologic acquisitions during neural network training.…”

Section: Discussionmentioning

confidence: 99%

“…Patient characteristics might additively influence the results and limit the generalizability of vCE image generation models. Chung et al (25) exclusively included patients with biopsy-proven invasive breast cancer with a mean lesion size of 24 mm. This cohort matches that in the study by Zhang et al(26), but who did not disclose the lesion size in detail.…”

Section: Discussionmentioning

confidence: 99%

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Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks

Liebert,

Schreiter,

Kapsner

et al. 2024

Preprint

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Background: Virtual contrast-enhanced (vCE) imaging techniques are an emerging topic of research in breast MRI. Purpose: To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of vCE breast MRI. Materials and Methods: The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age:52 ±12 years) obtained from 2017-2020 (single site, two 3T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value=50-1500s/mm2). Three readers evaluated the vCE images with regards to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise-ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings. Results: The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p<.05). Non-significant effects (p>.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance. Conclusion: vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when a multi b-value DWI is added to morphologic T1w-/T2w-sequences as input for model training.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks

Liebert,

Schreiter,

Kapsner

et al. 2024

Preprint

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“…However, other factors are also contributing to scan efficiency, such as the planning time, and the table switch time, thus optimizing logistics also seem important 8 . Further optimization may also include low(er) GBCA-concentration protocols 43 and GBCA-free approaches 44,45 . These are currently studied due to concerns of gadolinium deposition within the cerebrum 46 and for during pregnancy 47 …”

Section: Discussionmentioning

confidence: 99%

“…8 Further optimization may also include low(er) GBCA-concentration protocols 43 and GBCA-free approaches. 44,45 These are currently studied due to concerns of gadolinium deposition within the cerebrum 46 and for during pregnancy. 47 Our study had some limitations.…”

Section: Discussionmentioning

confidence: 99%

Breast DWI Analyzed Before and After Gadolinium Contrast Administration—An Intrapatient Analysis on 1.5 T and 3.0 T

et al. 2023

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Objectives Diffusion-weighted magnetic resonance imaging (MRI) is gaining popularity as an addition to standard dynamic contrast-enhanced breast MRI. Although adding diffusion-weighted imaging (DWI) to the standard protocol design would require increased scanning-time, implementation during the contrast-enhanced phase could offer a multiparametric MRI protocol without any additional scanning time. However, gadolinium within a region of interest (ROI) might affect assessments of DWI. This study aims to determine if acquiring DWI postcontrast, incorporated in an abbreviated MRI protocol, would statistically significantly affect lesion classification. In addition, the effect of postcontrast DWI on breast parenchyma was studied. Materials and Methods Screening or preoperative MRIs (1.5 T/3 T) were included for this study. Diffusion-weighted imaging was acquired with single-shot spin echo–echo planar imaging before and at approximately 2 minutes after gadoterate meglumine injection. Apparent diffusion coefficients (ADCs) based on 2-dimensional ROIs of fibroglandular tissue, as well as benign and malignant lesions at 1.5 T/3.0 T, were compared with a Wilcoxon signed rank test. Diffusivity levels were compared between precontrast and postcontrast DWI with weighted κ. An overall P ≤ 0.05 was considered statistically significant. Results No significant changes were observed in ADCmean after contrast administration in 21 patients with 37 ROI of healthy fibroglandular tissue and in the 93 patients with 93 (malignant and benign) lesions. This effect remained after stratification on B0. In 18% of all lesions, a diffusion level shift was observed, with an overall weighted κ of 0.75. Conclusions This study supports incorporating DWI at 2 minutes postcontrast when ADC is calculated based on b150-b800 with 15 mL 0.5 M gadoterate meglumine in an abbreviated multiparametric MRI protocol without requiring extra scan time.

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“…A recent study demonstrated an AI algorithm that was developed using a data set of breast MRI images with and without contrast. This AI model was then given inputs consisting only of non-contrast images from breast MRI studies and was able to generate simulated contrast-enhanced breast MRI images [65]. These simulated images were felt to be quantitatively similar to, and demonstrated high level of tumor overlap with, the true contrast-enhanced breast MRI images, with 95% of images found to be of diagnostic quality by the study radiologists.…”

Section: Image Enhancementmentioning

confidence: 99%

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

et al. 2023

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Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.

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Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer

Cited by 21 publications

References 20 publications

Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks

Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks

Breast DWI Analyzed Before and After Gadolinium Contrast Administration—An Intrapatient Analysis on 1.5 T and 3.0 T

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

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