@inproceedings{f15f0441fa3c4afba9869ed95ea0f7e5,
title = "Temporal assessment of radiomic features on clinical mammography in a high-risk population",
abstract = "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.",
keywords = "Breast cancer, mammogram, radiomics, risk assessment, texture",
author = "Mendel, {Kayla R.} and Hui Li and Li Lan and Chan, {Chun Wai} and King, {Lauren M.} and Nabihah Tayob and Gary Whitman and Randa El-Zein and Isabelle Bedrosian and Giger, {Maryellen L.}",
note = "Funding Information: Supported, in part, by the NIBIB of the NIH under grant number T32 EB002103, the NCI of the NIH under grant number U01 189240 and NIH QIN U01 195564. M.L.G. is a stockholder in R2 Technology/Hologic and a cofounder and shareholder in Quantitative Insights. M.L.G. and H.L receive royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. It is the University of Chicago Conflict of Interest Policy that investigators disclose publicly actual or potential significant financial interest that would reasonably appear to be directly and significantly affected by the research activities. Publisher Copyright: {\textcopyright} 2018 SPIE.; Medical Imaging 2018: Computer-Aided Diagnosis ; Conference date: 12-02-2018 Through 15-02-2018",
year = "2018",
doi = "10.1117/12.2293368",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Kensaku Mori and Nicholas Petrick",
booktitle = "Medical Imaging 2018",
address = "United States",
}