Radiogenomics-based cancer prognosis in colorectal cancer.
Bogdan Badic,Mathieu Hatt,Stéphanie Durand,Catherine Le Jossic-Corcos,Brigitte Simon,Dimitris Visvikis,Laurent Corcos +6 more
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TLDR
Combining CE-CT radiomics with gene expression analysis and histopathological examination of primary CRC could provide higher prognostic stratification power, leading to improved patient management.Abstract:
Radiogenomics aims at investigating the relationship between imaging radiomic features and gene expression alterations. This study addressed the potential prognostic complementary value of contrast enhanced computed tomography (CE-CT) radiomic features and gene expression data in primary colorectal cancers (CRC). Sixty-four patients underwent CT scans and radiomic features were extracted from the delineated tumor volume. Gene expression analysis of a small set of genes, previously identified as relevant for CRC, was conducted on surgical samples from the same tumors. The relationships between radiomic and gene expression data was assessed using the Kruskal–Wallis test. Multiple testing was not performed, as this was a pilot study. Cox regression was used to identify variables related to overall survival (OS) and progression free survival (PFS). ABCC2 gene expression was correlated with N (p = 0.016) and M stages (p = 0.022). Expression changes of ABCC2, CD166, CDKNV1 and INHBB genes exhibited significant correlations with some radiomic features. OS was associated with Ratio 3D Surface/volume (p = 0.022) and ALDH1A1 expression (p = 0.042), whereas clinical stage (p = 0.004), ABCC2 expression (p = 0.035), and EntropyGLCM_E (p = 0.0031), were prognostic factors for PFS. Combining CE-CT radiomics with gene expression analysis and histopathological examination of primary CRC could provide higher prognostic stratification power, leading to improved patient management.read more
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Journal ArticleDOI
Radiomics: from qualitative to quantitative imaging
William Rogers,Sithin Thulasi Seetha,Relinde I Y Lieverse,Renée W Y Granzier,Abdalla Ibrahim,Simon Keek,Sebastian Sanduleanu,Sergey Primakov,Manon Beuque,Damiënne Marcus,Alexander M A van der Wiel,Fadila Zerka,Cary Oberije,Janita E. van Timmeren,Janita E. van Timmeren,Henry C. Woodruff,Philippe Lambin +16 more
TL;DR: This review describes the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics.
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Biomarkers in Colorectal Cancer: Current Research and Future Prospects
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Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review.
Femke C.R. Staal,Denise J. van der Reijd,Marjaneh Taghavi,Doenja M. J. Lambregts,Regina G. H. Beets-Tan,Monique Maas +5 more
TL;DR: The most evidence is available for MRI-based radiomics in rectal cancer, and future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.
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TL;DR: Associations between inherited cellular transport gene variants and risk of EOC histologic subtypes are revealed on a large cohort of women.
Journal ArticleDOI
Robust Radiomics feature quantification using semiautomatic volumetric segmentation.
Chintan Parmar,Chintan Parmar,Chintan Parmar,Emmanuel Rios Velazquez,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Mohammed Jermoumi,Mohammed Jermoumi,Sara Carvalho,Raymond H. Mak,Sushmita Mitra,B. Uma Shankar,Ron Kikinis,Benjamin Haibe-Kains,Philippe Lambin,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +16 more
TL;DR: 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors and can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.
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