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Temporal assessment of radiomic features on clinical mammography in a high-risk population

Publication ,  Conference
Mendel, KR; Li, H; Lan, L; Chan, CW; King, LM; Tayob, N; Whitman, G; El-Zein, R; Bedrosian, I; Giger, ML
Published in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
January 1, 2018

Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment, diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects (23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis, the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance evaluation served well in our investigation on the role of temporal radiomics in risk assessment.

Duke Scholars

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510616394

Publication Date

January 1, 2018

Volume

10575
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Mendel, K. R., Li, H., Lan, L., Chan, C. W., King, L. M., Tayob, N., … Giger, M. L. (2018). Temporal assessment of radiomic features on clinical mammography in a high-risk population. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 10575). https://doi.org/10.1117/12.2293368
Mendel, K. R., H. Li, L. Lan, C. W. Chan, L. M. King, N. Tayob, G. Whitman, R. El-Zein, I. Bedrosian, and M. L. Giger. “Temporal assessment of radiomic features on clinical mammography in a high-risk population.” In Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 10575, 2018. https://doi.org/10.1117/12.2293368.
Mendel KR, Li H, Lan L, Chan CW, King LM, Tayob N, et al. Temporal assessment of radiomic features on clinical mammography in a high-risk population. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Mendel, K. R., et al. “Temporal assessment of radiomic features on clinical mammography in a high-risk population.” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10575, 2018. Scopus, doi:10.1117/12.2293368.
Mendel KR, Li H, Lan L, Chan CW, King LM, Tayob N, Whitman G, El-Zein R, Bedrosian I, Giger ML. Temporal assessment of radiomic features on clinical mammography in a high-risk population. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510616394

Publication Date

January 1, 2018

Volume

10575