Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
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Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.
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The breast phenotype group in TCIA defined a total of 38 radiomics features driving reproducible radiogenomics research hypothesis testing.
“In this sequence, the breast phenotype group defined a total of 38 radiomics features driving reproducible radiogenomics research hypothesis testing [234].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [234]
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- Hui Li has been working on quantitative imaging analysis at the University of Chicago since 2001. His research interests include breast cancer risk assessment and computer-aided diagnosis on Journal of Medical Imaging 041007-11 Oct-Dec 2015 • Vol. 2(
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- Authors
- Guo, W., Li, H., Zhu, Y., Lan, L., Yang, S., Drukker, K., Morris, E. A., Burnside, E. S., Whitman, G. J., Giger, M. L., Ji, Y., & Research Group, T. B. P.
- Affiliations
- Fudan University (CN)
- University of Chicago (US)
- Endeavor Health (US)
- The Univ. of Chicago
- Memorial Sloan Kettering Cancer Center (US)
- University of Wisconsin–Madison (US)
- The University of Texas MD Anderson Cancer Center (US)
- http://www.cancerimagingarchive.net/
- ORCID
- Venue
- Journal of Medical Imaging
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2015-09-23
- Volume
- 2
- Issue
- 04
- Pages
- 1-12
- ISSN
- 2329-4302 (ISSN-L)
- 2329-4310
- PMC
- PMC4718467
- OpenAlex
- W1687542094
- Cited by
- 148
- References
- 38
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 6 · 2023: 14 · 2022: 15 · 2021: 18 · 2020: 22 · 2019: 17 · 2018: 31 · 2017: 17 · 2016: 4
- FWCI
- 14.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of medical imaging (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Fudan University
- National Institutes of Health
- National Cancer Institute (U01CA195564, K24CA194251, R01CA132897, R01CA127379, P30CA014520)
- National Institute of Allergy and Infectious Diseases (U54AI117924)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Keywords
- invasive breast cancer, radiogenomics, radiomics, genomic data integration, clinical phenotype prediction, pathological stage, lymph node metastasis, hormone receptor status, HER2 status, breast cancer prognosis
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