Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.
Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.
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DOI：
10.1038/s41597-022-01555-4
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发表时间：
2022-07-23
期刊：
影响因子：
9.8
通讯作者：
Bakas, Spyridon
中科院分区：
文献类型：
--
作者：
Chitalia, Rhea;Pati, Sarthak;Bhalerao, Megh;Thakur, Siddhesh Pravin;Jahani, Nariman;Belenky, Vivian;McDonald, Elizabeth S.;Gibbs, Jessica;Newitt, David C.;Hylton, Nola M.;Kontos, Despina;Bakas, Spyridon
乳腺癌是最普遍的癌症形式之一，其固有的肿瘤内和肿瘤间异质性导致其预后不良。多项研究报告了来自私人机构数据或公开数据集的结果。然而，当前的公共数据集在以下方面具有一致性方面是有限的：a）数据质量，B）病理学的专家注释的质量，以及c）来自计算算法的基线结果的可用性。为了解决这些局限性，在这里，我们建议增强I-SPY 1数据收集，统一管理数据，肿瘤注释和定量成像特征。具体而言，所提出的数据集包括a）统一处理的扫描，其被协调以匹配强度和空间特征，便于在计算研究中立即使用，B）肿瘤区域的计算生成和手动修订的专家注释，以及c）对应于肿瘤区域的一组全面的定量成像（也称为放射组学）特征。该集合描述了我们对可重复，可再现和比较定量研究的贡献，这些研究导致了新的预测，预后和诊断评估。
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.
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影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred
影响因子：
3.8
作者：
Pati S;Verma R;Akbari H;Bilello M;Hill VB;Sako C;Correa R;Beig N;Venet L;Thakur S;Serai P;Ha SM;Blake GD;Shinohara RT;Tiwari P;Bakas S
通讯作者：
Bakas S
DOI：
10.1002/jmri.24351
发表时间：
2014-08
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
--
作者：
Jafri NF;Newitt DC;Kornak J;Esserman LJ;Joe BN;Hylton NM
通讯作者：
Hylton NM
影响因子：
2.4
作者：
Davatzikos, Christos;Rathore, Saima;Kontos, Despina
通讯作者：
Kontos, Despina
影响因子：
5.2
作者：
Castaldo, Rossana;Pane, Katia;Franzese, Monica
通讯作者：
Franzese, Monica