Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions

Ying Hwey Nai, Dennis Lai Hong Cheong, Sharmili Roy, Trina Kok, Mary C. Stephenson, Josh Schaefferkoetter, John J. Totman, Maurizio Conti, Lars Eriksson, Edward G. Robins, Ziting Wang, Wynne Yuru Chua, Bertrand Wei Leng Ang, Arvind Kumar Singha, Thomas Paulraj Thamboo, Edmund Chiong, Anthonin Reilhac

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Introduction: The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. Methods: 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). Results: SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. Conclusions: ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.

Original languageEnglish
Pages (from-to)64-72
Number of pages9
JournalMagnetic Resonance Imaging
Volume100
DOIs
Publication statusPublished or Issued - Jul 2023
Externally publishedYes

Keywords

  • Gleason score (GS)
  • Machine learning (ML)
  • Multiparametric magnetic resonance imaging (mpMRI)
  • Positron emission tomography (PET)
  • Quantitative parameters
  • Radiomics

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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