Quantitative DCE Dynamics on Transformed MR Imaging Discriminates Clinically Significant Prostate Cancer.

Autor:	Wei Z; Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA., Iluppangama M; Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.; Department of Mathematics and Statistics, University of South Florida, FL, USA., Qi J; Department of Cancer Physiology, Moffitt Cancer Center, Tampa, FL, USA., Choi JW; Department of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, USA., Yu A; Department of Genitourinary Cancer, Moffitt Cancer Center, Tampa, FL, USA., Gage K; Department of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, USA., Chumbalkar V; Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA., Dhilon J; Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA., Balaji KC; Department of Urology, University of Florida, Jacksonville, FL, USA., Venkataperumal S; Department of Radiology, James A Haley Veterans Hospital, Tampa, FL, USA., Hernandez DJ; Department of Urology, University of South Florida Health, Tampa, FL, USA., Park J; Department of Population Sciences, Moffitt Cancer Center, Tampa, FL, USA., Yedjou C; Department of Biology, College of Science and Technology, Florida A&M University, Tallahassee, FL, USA., Alo R; Department of Biology, College of Science and Technology, Florida A&M University, Tallahassee, FL, USA., Gatenby RA; Department of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, USA., Pow-Sang J; Department of Genitourinary Cancer, Moffitt Cancer Center, Tampa, FL, USA., Balagurunanthan Y; Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.; Department of Diagnostic & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, USA.; Department of Genitourinary Cancer, Moffitt Cancer Center, Tampa, FL, USA.
Jazyk:	angličtina
Zdroj:	Cancer control : journal of the Moffitt Cancer Center [Cancer Control] 2024 Jan-Dec; Vol. 31, pp. 10732748241298539.
DOI:	10.1177/10732748241298539
Abstrakt:	Dynamic contrast enhancement (DCE) imaging is a valuable sequence of multiparametric magnetic resonance imaging (mpMRI). A DCE sequence enhances the vasculature and complements T2-weighted (T2W) and Diffusion-weighted imaging (DWI), allowing early detection of prostate cancer. However, DCE assessment has remained primarily qualitative. The study proposes quantifying DCE characteristics (T1W sequences) using six time-dependent metrics computed on feature transformations (306 radiomic features) of abnormal image regions observed over time. We applied our methodology to prostate cancer patients with the DCE MRI images (n = 25) who underwent prostatectomy with confirmed pathological assessment of the disease using Gleason Score. Regions of abnormality were assessed on the T2W MRI, guided using the whole mount pathology. Preliminary analysis finds over six temporal DCE imaging features obtained on different transformations on the imaging regions showed significant differences compared to the indolent counterpart ( P ≤ 0.05, q ≤ 0.01). We find classifier models using logistic regression formed on DCE features after feature-based transformation (Centre of Mass) had an AUC of 0.89-0.94. While using mean feature-based transformation, the AUC was in the range of 0.71-0.76, estimated using the 0.632 bootstrap cross-validation method and after applying sample balancing using the synthetic minority oversampling technique (SMOTE). Our study finds, radiomic transformation of DCE images (T1 sequences) provides better signal standardization. Their temporal characteristics allow improved discrimination of aggressive disease. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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