Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI
| Autor: | Marko Grahovac, Bernhard Grubmüller, Laszlo Papp, Clemens P. Spielvogel, Wolfgang Wadsak, Boglarka Ecsedi, S.F. Shariat, Markus Hartenbach, Thomas H. Helbich, Alexander Haug, Martin Susani, Markus Mitterhauser, Martina Hamboeck, Sabrina Hartenbach, D Mohamad, Reza Agha Mohammadi Sareshgi, Peter R. Mazal, Gero Kramer, Lukas Kenner, Thomas Beyer, M Hacker, Pascal A. T. Baltzer, Ivo Rausch, Denis Krajnc |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Biochemical recurrence
Male medicine.medical_treatment Standardized uptake value Gallium Radioisotopes Machine learning computer.software_genre Prostate cancer Positron Emission Tomography Computed Tomography Medicine Humans Radiology Nuclear Medicine and imaging Prospective Studies Stage (cooking) Edetic Acid Radiomics Receiver operating characteristic medicine.diagnostic_test business.industry Prostatectomy Biochemical recurrence prediction Prostatic Neoplasms Magnetic resonance imaging General Medicine medicine.disease Magnetic Resonance Imaging PET/MRI Positron emission tomography Positron-Emission Tomography Original Article Artificial intelligence Supervised Machine Learning Overall patient risk prediction business computer Lesion risk prediction |
| Zdroj: | European Journal of Nuclear Medicine and Molecular Imaging |
| ISSN: | 1619-7089 1619-7070 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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