Radiogenomic analysis of breast cancer: dynamic contrast enhanced - magnetic resonance imaging based features are associated with molecular subtypes

Wang, Hongtao; Fan, Ming; Zhang, Juan; Wang, Xiaojia; Li, Lihua

doi:10.1117/12.2217658

Cited by 3 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Quantitative imaging features were not only linked to specific mutations but also genomics-derived molecular subtypes [125,126]. Wang et al were able to further broaden the molecular subtypes that were predicted by imaging features: luminal A (p = 0.00473), HER2-enriched (p = 0.00277), and basal-like (p = 0.0117) [127]. Machine learning models capable of discerning luminal A molecular subtype (AUC = 0.697), triple-negative breast cancer (AUC = 0.654), estrogen receptor (ER) mutational status (AUC = 0.649), and progesterone receptor (PR) status (AUC = 0.622) were subsequently built [128].…”

Section: Breast and Ovariesmentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

View full text Add to dashboard Cite

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.

show abstract

Section: Breast and Ovariesmentioning

confidence: 99%

Radiogenomics: bridging imaging and genomics

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Medical Imaging 2016 “PACS and Imaging Informatics: Next Generation and Innovations” was chaired by Jianguo Zhang and Tessa S. Cook and held in San Diego, CA. Radiomics was the topic of several papers for classification of breast cancer 609 and lung nodules. 610 3D printing for cardiac procedure planning was a topic, 611 as was smartphone use for clinical trials.…”

Section: S Pacs Conferencesmentioning

confidence: 99%

SPIE Medical Imaging 50th anniversary: history of the Picture Archiving and Communication Systems Conference

Andriole

2022

J. Med. Imag.

View full text Add to dashboard Cite

show abstract

The use of radiomics in magnetic resonance imaging for the pre‐treatment characterisation of breast cancers: A scoping review

Campaña

Gandomkar

Giannotti

et al. 2023

J of Medical Radiation Sci

View full text Add to dashboard Cite

Radiomics is an emerging field that aims to extract and analyse a comprehensive set of quantitative features from medical images. This scoping review is focused on MRI‐based radiomic features for the molecular profiling of breast tumours and the implications of this work for predicting patient outcomes. A thorough systematic literature search and outcome extraction were performed to identify relevant studies published in MEDLINE/PubMed (National Centre for Biotechnology Information), EMBASE and Scopus from 2015 onwards. The following information was retrieved from each article: study purpose, study design, extracted radiomic features, machine learning technique(s), sample size/characteristics, statistical result(s) and implications on patient outcomes. Based on the study purpose, four key themes were identified in the included 63 studies: tumour subtype classification (n = 37), pathologically complete response (pCR) prediction (n = 15), lymph node metastasis (LNM) detection (n = 7) and recurrence rate prediction (n = 6). In all four themes, reported accuracies widely varied among the studies, for example, area under receiver characteristics curve (AUC) for detecting LNM ranged from 0.72 to 0.91 and the AUC for predicting pCR ranged from 0.71 to 0.99. In all four themes, combining radiomic features with clinical data improved the predictive models. Preliminary results of this study showed radiomics potential to characterise the whole tumour heterogeneity, with clear implications for individual‐targeted treatment. However, radiomics is still in the pre‐clinical phase, currently with an insufficient number of large multicentre studies and those existing studies are often limited by insufficient methodological transparency and standardised workflow. Consequently, the clinical translation of existing studies is currently limited.

show abstract

Radiogenomic analysis of breast cancer: dynamic contrast enhanced - magnetic resonance imaging based features are associated with molecular subtypes

Cited by 3 publications

References 5 publications

Radiogenomics: bridging imaging and genomics

Radiogenomics: bridging imaging and genomics

SPIE Medical Imaging 50th anniversary: history of the Picture Archiving and Communication Systems Conference

The use of radiomics in magnetic resonance imaging for the pre‐treatment characterisation of breast cancers: A scoping review

Contact Info

Product

Resources

About