Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Tumor Aggressiveness: A Pilot Study

Version 1 : Received: 11 May 2023 / Approved: 12 May 2023 / Online: 12 May 2023 (09:06:16 CEST)

A peer-reviewed article of this Preprint also exists.

Mayer, R.; Turkbey, B.; Choyke, P.L.; Simone, C.B., II. Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study. Diagnostics 2023, 13, 2008. Mayer, R.; Turkbey, B.; Choyke, P.L.; Simone, C.B., II. Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study. Diagnostics 2023, 13, 2008.

Abstract

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of MRI sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material, and prolongs scanning time. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasively management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx+SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade >=2 (<2) was judged as CsPCa (CiPCa). Linear and Logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R~0.17). Patients assessed with combined MRBx+SysBx and from individual MRI scanners achieved higher R (R=0.207+/-0.118) than using all patients in the fits. Conclusions: In the first study to date to apply and test hyperspectral approaches for assessing tumor aggressiveness on SRBP-MRI, high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA were comparable or better than those from artificial intelligence in other studies.

Keywords

logistic probability; prostate cancer; bi-parametric magnetic resonance imaging (BP-MRI); Gleason score (GS); signal-to-clutter ratio (SCR); regularization

Subject

Medicine and Pharmacology, Oncology and Oncogenics

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