Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1
Rhea Chitalia, Sarthak Pati, Megh Bhalerao, Siddhesh Pravin Thakur, Nariman Jahani, Vivian Belenky, Elizabeth S McDonald, Jessica Gibbs, David C Newitt, Nola M Hylton, Despina Kontos, Spyridon Bakas
Sci Data . 2022 Jul 23;9(1):440. doi: 10.1038/s41597-022-01555-4.
Breast cancer is one of the most pervasive forms of cancer and its inherent intra- and inter-tumor heterogeneity contributes towards its poor prognosis. Multiple studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of having consistency in: a) data quality, b) quality of expert annotation of pathology, and c) availability of baseline results from computational algorithms. To address these limitations, here we propose the enhancement of the I-SPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. Specifically, the proposed dataset includes a) uniformly processed scans that are harmonized to match intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
Read Full Article Here: https://doi.org/10.1038/s41597-022-01555-4