Prostate cancer classification using radiomics and machine learning on mp-MRI validated using co-registered histology

Ryan Alfano; Glenn S Bauman; Jose A Gomez; Mena Gaed; Madeleine Moussa; Joseph Chin; Stephen Pautler; Aaron D Ward

doi:10.1016/j.ejrad.2022.110494

Prostate cancer classification using radiomics and machine learning on mp-MRI validated using co-registered histology

Eur J Radiol. 2022 Nov:156:110494. doi: 10.1016/j.ejrad.2022.110494. Epub 2022 Aug 19.

Authors

Ryan Alfano¹, Glenn S Bauman², Jose A Gomez³, Mena Gaed⁴, Madeleine Moussa⁵, Joseph Chin⁶, Stephen Pautler⁷, Aaron D Ward⁸

Affiliations

¹ Baines Imaging Research Laboratory, 790 Commissioners Rd E, London, ON N6A 5W9, Canada; Lawson Health Research Institute, 750 Base Line Rd E, London, ON N6C 2R5, Canada; Western University, Department of Medical Biophysics, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: ralfano2@uwo.ca.
² Western University, Department of Medical Biophysics, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: glenn.bauman@lhsc.on.ca.
³ Western University, Department of Pathology and Laboratory Medicine, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: jose.gomezlemus@lhsc.on.ca.
⁴ Western University, Department of Pathology and Laboratory Medicine, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: mgaed@uwo.ca.
⁵ Western University, Department of Pathology and Laboratory Medicine, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: madeleine.moussa@lhsc.on.ca.
⁶ Western University, Department of Surgery, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: joseph.chin@lhsc.on.ca.
⁷ Western University, Department of Surgery, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: stephen.pautler@sjhc.london.on.ca.
⁸ Baines Imaging Research Laboratory, 790 Commissioners Rd E, London, ON N6A 5W9, Canada; Lawson Health Research Institute, 750 Base Line Rd E, London, ON N6C 2R5, Canada; Western University, Department of Medical Biophysics, 1151 Richmond St., London, ON N6A 3K7, Canada; Western University, Department of Oncology, 1151 Richmond St., London, ON N6A 3K7, Canada. Electronic address: aaron.ward@uwo.ca.

PMID: 36095953
DOI: 10.1016/j.ejrad.2022.110494

Abstract

Background: Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for prostate cancer (PCa) detection but currently has unaddressed limitations. Computer aided diagnosis (CAD) systems have been developed to address these needs, but many approaches used to generate and validate the models have inherent biases.

Method: All clinically significant PCa on histology was mapped to mp-MRI using a previously validated registration algorithm. Shape and size matched non-PCa regions were selected using a proposed sampling algorithm to eliminate biases towards shape and size. Further analysis was performed to assess biases regarding inter-zonal variability.

Results: A 5-feature Naïve-Bayes classifier produced an area under the receiver operating characteristic curve (AUC) of 0.80 validated using leave-one-patient-out cross-validation. As mean inter-class area mismatch increased, median AUC trended towards positively biasing classifiers to producing higher AUCs. Classifiers were invariant to differences in shape between PCa and non-PCa lesions (AUC: 0.82 vs 0.82). Performance for models trained and tested only in the peripheral zone was found to be lower than in the central gland (AUC: 0.75 vs 0.95).

Conclusion: We developed a radiomics based machine learning system to classify PCa vs non-PCa tissue on mp-MRI validated on accurately co-registered mid-gland histology with a measured target registration error. Potential biases involved in model development were interrogated to provide considerations for future work in this area.

Keywords: Machine learning; Multiparametric MRI; Prostate cancer; Radiomics.