Can MRI of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour

Official title

Can Magnetic Resonance Imaging of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour (Can MRI-PREDICT)

Overview

Prostate cancer is the most common cancer diagnosed in men in Canada. Magnetic resonance imaging (MRI) may become a valuable tool to non-invasively identify prostate cancer and assess its biological aggressiveness, which in turn will help doctors make better decisions about how to treat an individual patient's prostate cancer. Despite the promise of MRI for detecting and characterizing prostate cancer, there are several recognized limitations and challenges. These include lack of standardized interpretation and reporting of prostate MRI exams. The investigators propose to validate and improve a computer program computerized prediction tool that will use information from MR images to inform us how aggressive a prostate cancer is. The hypothesis is that this computer-aided approach will increase the reproducibility and accuracy of MRI in predicting the tumor biology information about the imaged prostate cancer.

Detailed description

Prostate biopsies are the gold standard assessment of how prostate cancer is diagnosed and how low risk prostate cancers are surveilled. The investigators have produced a machine-learning based algorithm which uses MRI characteristics (radiomic features or textures) to predict the results of a prostate biopsy. The field has numerous concerns that such radiomic based predictions will not be reproducible, as there as so many subtle changes between MRI scans of different patients.

The interventions are the use of the MRT and the use of a second MRI of the prostate (MRI-P).

Two primary outcomes will be investigated. First, the existing radiomics predictive model, labeled as the MRI-P based Radiomics Tool (MRT) will predict the Grade Group (GG) and compare it to the gold standard, pathologist's evaluation of the Grade Group (GG). Second, the stability of the predicted GG between two shortly spaced MRI-Ps will be compared.

Patients with a detectable prostate nodule on MRI-P which localizes to a biopsy confirmed prostate cancer will be approached for enrollment. If enrolled, participants will attend for a subsequent MRI-P in a brief time frame relative to the acquisition of the first MRI-P. Attempts will be made to obtain participants that allow for even distribution among all GGs.

Interventions

• Diagnostic test MRT Accuracy
  Predicted Grade Group (GG) by the MRI-based Radiomics Tool (MRT) at each Magnetic Resonance Imaging of the Prostate (MRI-P)
• Diagnostic test MRT Stability
  MRT's predicted GG at second MRI-P.

Primary outcome measures

• MRT Classification Change [Time frame: Baseline, 8 weeks]
• MRT Classification: Baseline [Time frame: Baseline]
• MRT Classification: Week 8 [Time frame: 8 weeks]

Secondary outcome measures (1)

• Model optmization with novel radiomic features and clinical covariates [Time frame: At study completion, 2 years.]

Eligibility criteria

Inclusion criteria

An appropriate diagnostic MRI-P, defined as:

• Being performed on 3T MRI at the Halifax Infirmary Building
• Taken place within 5 weeks of study enrolment
• Having a detectable nodule which anatomically localizes to prostate cancer (PCa) identified in diagnostic biopsy specimen
• Acquired T1+contrast, T2, and attenuated diffusion coefficient (ADC) series axial images of the prostate

An appropriate diagnostic biopsy, defined as:

• Taken place within 2 months of the participant's MRI-P 1
• Taken place within 3 months of participant's study enrolment
• Reports diagnosis of PCa
• Reports a systematic assessment of the biopsy, assessing at least 12 cores
• Reports at least on core involved with PCa and this core must anatomically localise to a nodule seen on MRI-P 1

Exclusion criteria

• Past prostatic interventions which would influence the prostate's structure
• Alterations to physiological testosterone levels
• Inability to position one's self in a reproducible fashion for an MRI-P
• Patient factors reported to produce significant artifact on MRI-P 1

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

N/A

Model

Single group

Masking

Open label

Primary purpose

Diagnostic

Study locations

Canada · 1 center

• Victoria General Hospital — Halifax

Publications

• Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA; Grading Committee. The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma: Definition of Grading Patterns and Proposal for a New Grading System. Am J Surg Pathol. 2016 Feb;40(2):244-52. doi: 10.1097/PAS.0000000000000530. PMID 26492179
• Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, Margolis D, Schnall MD, Shtern F, Tempany CM, Thoeny HC, Verma S. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur Urol. 2016 Jan;69(1):16-40. doi: 10.1016/j.eururo.2015.08.052. Epub 2015 Oct 1. PMID 26427566
• Westphalen AC, McCulloch CE, Anaokar JM, Arora S, Barashi NS, Barentsz JO, Bathala TK, Bittencourt LK, Booker MT, Braxton VG, Carroll PR, Casalino DD, Chang SD, Coakley FV, Dhatt R, Eberhardt SC, Foster BR, Froemming AT, Futterer JJ, Ganeshan DM, Gertner MR, Mankowski Gettle L, Ghai S, Gupta RT, Hahn ME, Houshyar R, Kim C, Kim CK, Lall C, Margolis DJA, McRae SE, Oto A, Parsons RB, Patel NU, Pinto PMID 32315265
• Chaddad A, Kucharczyk MJ, Niazi T. Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer. Cancers (Basel). 2018 Jul 28;10(8):249. doi: 10.3390/cancers10080249. PMID 30060575
• T JMC, Arif M, Niessen WJ, Schoots IG, Veenland JF. Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications. Cancers (Basel). 2020 Jun 17;12(6):1606. doi: 10.3390/cancers12061606. PMID 32560558
• Schwier M, van Griethuysen J, Vangel MG, Pieper S, Peled S, Tempany C, Aerts HJWL, Kikinis R, Fennessy FM, Fedorov A. Repeatability of Multiparametric Prostate MRI Radiomics Features. Sci Rep. 2019 Jul 1;9(1):9441. doi: 10.1038/s41598-019-45766-z. PMID 31263116
• Lu H, Parra NA, Qi J, Gage K, Li Q, Fan S, Feuerlein S, Pow-Sang J, Gillies R, Choi JW, Balagurunathan Y. Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging. Front Oncol. 2020 May 7;10:551. doi: 10.3389/fonc.2020.00551. eCollection 2020. PMID 32457827
• Merisaari H, Taimen P, Shiradkar R, Ettala O, Pesola M, Saunavaara J, Bostrom PJ, Madabhushi A, Aronen HJ, Jambor I. Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magn Reson Med. 2020 Jun;83(6):2293-2309. doi: 10.1002/mrm.28058. Epub 2019 Nov 8. PMID 31703155

Identifiers

NCT: NCT05024162 · 1026856

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗