A Novel Assessment of Whole-Mount Gleason Grading in Prostate Cancer To Identify Candidates for Radical Prostatectomy: A Machine Learning-Based Multiomics Study

Authors

Jing Ning
Clemens P Spielvogel
David Haberl
Karolina Trachtova
Stefan Stoiber
Sazan Rasul
Vojtech Bystry
Gabriel Wasinger
Pascal Baltzer
Elisabeth Gurnhofer
Gerald Timelthaler
Michaela Schlederer
Laszlo Papp
Helga Schachner
Thomas Helbich
Markus Hartenbach
Bernhard Grubmüller
Shahrokh F Shariat
Marcus Hacker
Alexander Haug
Lukas Kenner

Publication Date

1-1-2024

Journal

Theranostics

Abstract

Purpose: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment.

Materials and Methods: A total of 146 patients with PCa recruited in a pilot study of a prospective clinical trial (NCT02659527) were retrospectively included in the side study, all of whom underwent ⁶⁸Ga-PSMA-11 integrated positron emission tomography (PET) / magnetic resonance (MR) before radical prostatectomy (RP) between May 2014 and April 2020. To establish a multiomics ML model, we quantified PET radiomics features, pathway-level genomics features from whole exome sequencing, and pathomics features derived from immunohistochemical staining of 11 biomarkers. Based on the multiomics dataset, five ML models were established and validated using 100-fold Monte Carlo cross-validation.

Results: Among five ML models, the random forest (RF) model performed best in terms of the area under the curve (AUC). Compared to bxGG assessment alone, the RF model was superior in terms of AUC (0.87 vs 0.75), specificity (0.72 vs 0.61), positive predictive value (0.79 vs 0.75), and accuracy (0.78 vs 0.77) and showed slightly decreased sensitivity (0.83 vs 0.89) and negative predictive value (0.80 vs 0.81). Among the feature categories, bxGG was identified as the most important feature, followed by pathomics, clinical, radiomics and genomics features. The three important individual features were bxGG, PSA staining and one intensity-related radiomics feature.

Conclusion: The findings demonstrate a superior assessment of the developed multiomics-based ML model in whole-mount GG compared to the current clinical baseline of bxGG. This enables personalized patient management by identifying high-risk PCa patients for RP.

Keywords

Humans, Male, Prostatic Neoplasms, Machine Learning, Prostatectomy, Neoplasm Grading, Aged, Middle Aged, Retrospective Studies, Prospective Studies, Pilot Projects, Positron-Emission Tomography, Magnetic Resonance Imaging, Genomics, Multiomics, prostate cancer, PSMA, Gleason grading, machine learning, multiomics

Comments

Supplementary Materials

PMID: 39239512