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Erschienen in: European Radiology 9/2017

13.03.2017 | Oncology

A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome

verfasst von: Hebert Alberto Vargas, Harini Veeraraghavan, Maura Micco, Stephanie Nougaret, Yulia Lakhman, Andreas A. Meier, Ramon Sosa, Robert A. Soslow, Douglas A. Levine, Britta Weigelt, Carol Aghajanian, Hedvig Hricak, Joseph Deasy, Alexandra Snyder, Evis Sala

Erschienen in: European Radiology | Ausgabe 9/2017

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Abstract

Purpose

To evaluate the associations between clinical outcomes and radiomics-derived inter-site spatial heterogeneity metrics across multiple metastatic lesions on CT in patients with high-grade serous ovarian cancer (HGSOC).

Methods

IRB-approved retrospective study of 38 HGSOC patients. All sites of suspected HGSOC involvement on preoperative CT were manually segmented. Gray-level correlation matrix-based textures were computed from each tumour site, and grouped into five clusters using a Gaussian Mixture Model. Pairwise inter-site similarities were computed, generating an inter-site similarity matrix (ISM). Inter-site texture heterogeneity metrics were computed from the ISM and compared to clinical outcomes.

Results

Of the 12 inter-site texture heterogeneity metrics evaluated, those capturing the differences in texture similarities across sites were associated with shorter overall survival (inter-site similarity entropy, similarity level cluster shade, and inter-site similarity level cluster prominence; p ≤ 0.05) and incomplete surgical resection (similarity level cluster shade, inter-site similarity level cluster prominence and inter-site cluster variance; p ≤ 0.05). Neither the total number of disease sites per patient nor the overall tumour volume per patient was associated with overall survival. Amplification of 19q12 involving cyclin E1 gene (CCNE1) predominantly occurred in patients with more heterogeneous inter-site textures.

Conclusion

Quantitative metrics non-invasively capturing spatial inter-site heterogeneity may predict outcomes in patients with HGSOC.

Key Points

Calculating inter-site texture-based heterogeneity metrics was feasible
Metrics capturing texture similarities across HGSOC sites were associated with overall survival
Heterogeneity metrics were also associated with incomplete surgical resection of HGSOC.
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Metadaten
Titel
A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome
verfasst von
Hebert Alberto Vargas
Harini Veeraraghavan
Maura Micco
Stephanie Nougaret
Yulia Lakhman
Andreas A. Meier
Ramon Sosa
Robert A. Soslow
Douglas A. Levine
Britta Weigelt
Carol Aghajanian
Hedvig Hricak
Joseph Deasy
Alexandra Snyder
Evis Sala
Publikationsdatum
13.03.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 9/2017
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-017-4779-y

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