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Exploratory Radiomics in Computed Tomography Perfusion of Prostate Cancer


Tanadini-Lang, Stephanie; Bogowicz, Marta; Veit-Haibach, Patrick; Huellner, Martin; Pauli, Chantal; Shukla, Vyoma; Guckenberger, Matthias; Riesterer, Oliver (2018). Exploratory Radiomics in Computed Tomography Perfusion of Prostate Cancer. Anticancer Research, 38(2):685-690.

Abstract

BACKGROUND/AIM An evaluation if radiomic features of CT perfusion (CTP) can predict tumor grade and aggressiveness in prostate cancer was performed. MATERIALS AND METHODS Forty-seven patients had biopsy-confirmed prostate cancer, and received a CTP. Blood volume (BV), blood flow (BF) and mean transit time (MTT) maps were derived and 1,701 radiomic features were determined per patient. Regression models were built to estimate post-surgical Gleason score (GS), microvessel density (MVD) and distinguish between the different risk groups. RESULTS Six out of the 47 patients had to be excluded from further analysis. A weak relationship between postsurgical GS and one radiomic parameter was found (R2=0.21, p=0.01). The same parameter combined with MTT inter-quartile range was prognostic for the risk group categorisation (AUC=0.81). Two different radiomic parameters were able to distinguish between low-intermediate risk and high-intermediate risk (AUC=0.77). Four parameters correlated with MVD (R2=0.53, p<0.02). CONCLUSION This exploratory study shows the potential of radiomics to classify prostate cancer.

Abstract

BACKGROUND/AIM An evaluation if radiomic features of CT perfusion (CTP) can predict tumor grade and aggressiveness in prostate cancer was performed. MATERIALS AND METHODS Forty-seven patients had biopsy-confirmed prostate cancer, and received a CTP. Blood volume (BV), blood flow (BF) and mean transit time (MTT) maps were derived and 1,701 radiomic features were determined per patient. Regression models were built to estimate post-surgical Gleason score (GS), microvessel density (MVD) and distinguish between the different risk groups. RESULTS Six out of the 47 patients had to be excluded from further analysis. A weak relationship between postsurgical GS and one radiomic parameter was found (R2=0.21, p=0.01). The same parameter combined with MTT inter-quartile range was prognostic for the risk group categorisation (AUC=0.81). Two different radiomic parameters were able to distinguish between low-intermediate risk and high-intermediate risk (AUC=0.77). Four parameters correlated with MVD (R2=0.53, p<0.02). CONCLUSION This exploratory study shows the potential of radiomics to classify prostate cancer.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Radiation Oncology
04 Faculty of Medicine > University Hospital Zurich > Clinic for Nuclear Medicine
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:February 2018
Deposited On:09 Feb 2018 14:27
Last Modified:19 Mar 2018 12:25
Publisher:International Institute of Anticancer Research
ISSN:0250-7005
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.21873/anticanres.12273
PubMed ID:29374691

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