Dataset Information

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.

ABSTRACT: 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.

SUBMITTER: Chitalia R

PROVIDER: S-EPMC9308769 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.

Chitalia Rhea R Pati Sarthak S Bhalerao Megh M Thakur Siddhesh Pravin SP Jahani Nariman N Belenky Vivian V McDonald Elizabeth S ES Gibbs Jessica J Newitt David C DC Hylton Nola M NM Kontos Despina D Bakas Spyridon S

Scientific data 20220723 1

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 limita ...[more]

PMID: 35871247

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Project description:BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide biomarkers of the outcome of locally-advanced cervical carcinoma (LACC). There is, however, no agreement on how DCE-MR recordings should be analyzed. Previously, we have analyzed DCE-MRI data of LACC using non-model-based strategies. In the current study, we analyzed DCE-MRI data of LACC using the Tofts pharmacokinetic model, and the biomarkers derived from this analysis were compared with those derived from the non-model-based analyses.MethodsEighty LACC patients given cisplatin-based chemoradiotherapy with curative intent were included in the study. Treatment outcome was recorded as disease-free survival (DFS) and overall survival (OS). DCE-MRI series were analyzed voxelwise to produce Ktrans and ve frequency distributions, and ROC analysis was used to identify the parameters of the frequency distributions having the greatest potential as biomarkers. The prognostic power of these parameters was compared with that of the non-model-based parameters LETV (low-enhancing tumor volume) and TVIS (tumor volume with increasing signal).ResultsPoor DFS and OS were associated with low values of Ktrans, whereas there was no association between treatment outcome and ve. The Ktrans parameters having the greatest prognostic value were p35-Ktrans (the Ktrans value at the 35 percentile of a frequency distribution) and RV-Ktrans (the tumor subvolume with Ktrans values below 0.13?min-?1). Multivariate analysis including clinical parameters and p35-Ktrans or RV-Ktrans revealed that RV-Ktrans was the only independent prognostic factor of DFS and OS. There were significant correlations between RV-Ktrans and LETV and between RV-Ktrans and TVIS, and the prognostic power of RV-Ktrans was similar to that of LETV and TVIS.ConclusionsBiomarkers of the outcome of LACC can be provided by analyzing DCE-MRI series using the Tofts pharmacokinetic model. However, these biomarkers do not appear to have greater prognostic value than biomarkers determined by non-model-based analyses.

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Dataset Information

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.

Publications

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.

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