Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

T2-weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Savannah R Duenweg
ORCID logo ,
Samuel A. Bobholz
,
Michael J. Barrett
,
Allison K. Lowman
,
Aleksandra Winiarz
,
Biprojit Nath
,
Margaret Stebbins
,
John Bukowy
,
Kenneth A. Iczkowski
,
Kenneth M. Jacobsohn
,
Stephanie Vincent-Sheldon
,
Peter S. LaViolette
* ORCID logo
Version 1 : Received: 30 June 2023 / Approved: 3 July 2023 / Online: 3 July 2023 (11:32:39 CEST)

A peer-reviewed article of this Preprint also exists.

Duenweg, S.R.; Bobholz, S.A.; Barrett, M.J.; Lowman, A.K.; Winiarz, A.; Nath, B.; Stebbins, M.; Bukowy, J.; Iczkowski, K.A.; Jacobsohn, K.M.; Vincent-Sheldon, S.; LaViolette, P.S. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers 2023, 15, 4437. Duenweg, S.R.; Bobholz, S.A.; Barrett, M.J.; Lowman, A.K.; Winiarz, A.; Nath, B.; Stebbins, M.; Bukowy, J.; Iczkowski, K.A.; Jacobsohn, K.M.; Vincent-Sheldon, S.; LaViolette, P.S. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers 2023, 15, 4437.

Abstract

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predict eventual BCR with an AUC of 0.97 and classifying cancer at an accuracy of 89.9%. This study showcases the application for a radiomics-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Keywords

prostate cancer; mp-MRI; biochemical recurrence; Gleason pattern; radiomics

Subject

Medicine and Pharmacology, Urology and Nephrology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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