Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

Mendel, Kayla R.; Li, Hui; Giger, Maryellen L.

doi:10.1117/12.2217775

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…In applications for breast cancer risk assessment, radiomic features have emerged as surrogate measures for breast parenchymal patterns in mammographic and tomographic images . With the expanding use of DCE‐MRI for breast cancer screening, recent studies have suggested associations between radiomic characterizations of BPE and breast cancer risk . Specifically, in each study radiomic features characterizing the morphology, texture, and kinetics of BPE, as well as features quantifying breast density, demonstrated a high correlation with qualitative phenotypic characteristics determined by a radiologist, emphasizing the complementary value of radiomics for breast cancer risk assessment.…”

Section: Radiomic Analysis Of Bpementioning

confidence: 99%

Background parenchymal enhancement on breast MRI: A comprehensive review

Liao

Bancroft

Strigel

et al. 2019

Magnetic Resonance Imaging

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The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrastenhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5

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Section: Radiomic Analysis Of Bpementioning

confidence: 99%

Background parenchymal enhancement on breast MRI: A comprehensive review

Liao

Bancroft

Strigel

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

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“…Beyond full-field digital mammograms and breast ultrasound, CAD on breast tomosynthesis was an early topic for emerging technology. [52][53][54] Other breast imaging technologies and topics with CAD applications included dynamic breast MRI, utilization of multiple views, lesion segmentation and classification, breast segmentation and density [55][56][57][58][59][60][61][62] In addition, AI methods for assessing prognosis and response to therapy have been presented. 63 Abdominal imaging with a focus on bowel and liver was a frequent topic.…”

Section: Introductionmentioning

confidence: 99%

“… 52 – 54 Other breast imaging technologies and topics with CAD applications included dynamic breast MRI, utilization of multiple views, lesion segmentation and classification, breast segmentation and density assessment, predictive models for cancer risk assessment, dedicated breast CT, 3D ultrasound, and breast cancer diagnosis with deep learning. 55 – 62 In addition, AI methods for assessing prognosis and response to therapy have been presented. 63 …”

Section: Introductionmentioning

confidence: 99%

SPIE Computer-Aided Diagnosis conference anniversary review

Summers

Giger

2022

J. Med. Imag.

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The SPIE Computer-Aided Diagnosis conference has been held for 16 consecutive years at the annual SPIE Medical Imaging symposium. The conference remains vibrant, with a core group of submitters as well as new submitters and attendees each year. Recent developments include a marked shift in submissions relating to the artificial intelligence revolution in medical image analysis. This review describes the topics and trends observed in research presented at the Computer-Aided Diagnosis conference as part of the 50th-anniversary celebration of SPIE Medical Imaging.

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Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

2016

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BackgroundThe assessment of a woman’s risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.Main textThe analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.ConclusionsThe objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

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Quantitative breast MRI radiomics for cancer risk assessment and the monitoring of high-risk populations

Cited by 3 publications

References 7 publications

Background parenchymal enhancement on breast MRI: A comprehensive review

Background parenchymal enhancement on breast MRI: A comprehensive review

SPIE Computer-Aided Diagnosis conference anniversary review

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

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