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Papp L, Spielvogel CP, Grubmüller B, Grahovac M, Krajnc D, Ecsedi B, Sareshgi RAM, Mohamad D, Hamboeck M, Rausch I, Mitterhauser M, Wadsak W, Haug AR, Kenner L, Mazal P, Susani M, Hartenbach S, Baltzer P, Helbich TH, Kramer G, Shariat SF, Beyer T, Hartenbach M, Hacker M. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [ 68Ga]Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging 2021; 48:1795-1805. [PMID: 33341915 PMCID: PMC8113201 DOI: 10.1007/s00259-020-05140-y] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 11/29/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning. METHODS Fifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses. RESULTS The area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively. CONCLUSION Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.
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Affiliation(s)
- L Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - C P Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
| | - B Grubmüller
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - M Grahovac
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - D Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - B Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - R A M Sareshgi
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - D Mohamad
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - M Hamboeck
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - I Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - M Mitterhauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
| | - W Wadsak
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - A R Haug
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
| | - L Kenner
- Christian Doppler Laboratory for Applied Metabolomics, Vienna, Austria
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - P Mazal
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | - M Susani
- Clinical Institute of Pathology, Medical University of Vienna, Vienna, Austria
| | | | - P Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Common General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - T H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Common General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - G Kramer
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - S F Shariat
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - T Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - M Hartenbach
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - M Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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