Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

• Published in: European journal of nuclear medicine and molecular imaging Vol. 48; no. 6; pp. 1795 - 1805
• Main Authors: Papp, L., Spielvogel, C. P., Grubmüller, B., Grahovac, M., Krajnc, D., Ecsedi, B., Sareshgi, R. A.M., Mohamad, D., Hamboeck, M., Rausch, I., Mitterhauser, M., Wadsak, W., Haug, A. R., Kenner, L., Mazal, P., Susani, M., Hartenbach, S., Baltzer, P., Helbich, T. H., Kramer, G., Shariat, S.F., Beyer, T., Hartenbach, M., Hacker, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-06-2021; Springer Nature B.V
• Subjects: Advanced Image Analyses (Radiomics and Artificial Intelligence); Antigens; Biopsy; Cancer surgery; Cardiology; Classification; Diffusion coefficient; Emission analysis; Imaging; Learning algorithms; Lesions; Machine learning; Magnetic resonance imaging; Medicine; Medicine & Public Health; Metastases; Nuclear Medicine; Oncology; Original; Original Article; Orthopedics; Patients; Performance evaluation; Positron emission; Positron emission tomography; Prediction models; Predictions; Prostate cancer; Prostate-specific antigen; Prostatectomy; Radiology; Radiomics; Relaxation time; Risk; Tomography; Biochemical recurrence prediction; PET/MRI; Overall patient risk prediction; Machine learning; Lesion risk prediction; Prostate cancer; Radiomics
• ISSN: 1619-7070; 1619-7089
• DOI: 10.1007/s00259-020-05140-y

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 [
18
F]FMC and [
68
Ga]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 [
68
Ga]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 (M
LH
). Furthermore, M
BCR
and M
OPR
predictive model schemes were built by combining M
LH
, 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 [
68
Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses.
Results
The area under the receiver operator characteristic curve (AUC) of the M
LH
model (0.86) was higher than the AUC of the [
68
Ga]Ga-PSMA-11 SUV
max
analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the M
BCR
and M
OPR
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.