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- Title
Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients.
- Authors
Alongi, Pierpaolo; Stefano, Alessandro; Comelli, Albert; Laudicella, Riccardo; Scalisi, Salvatore; Arnone, Giuseppe; Barone, Stefano; Spada, Massimiliano; Purpura, Pierpaolo; Bartolotta, Tommaso Vincenzo; Midiri, Massimo; Lagalla, Roberto; Russo, Giorgio
- Abstract
<bold>Objective: </bold>The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging.<bold>Material and Methods: </bold>Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M).<bold>Results: </bold>In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%.<bold>Conclusion: </bold>This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes.<bold>Key Points: </bold>• Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.
- Subjects
RADIOMICS; COMPUTED tomography; MACHINE learning; POSITRON emission tomography computed tomography; ARTIFICIAL intelligence; PROSTATE cancer; CANCER relapse; CHOLINE; PROSTATE tumors; LONGITUDINAL method
- Publication
European Radiology, 2021, Vol 31, Issue 7, p4595
- ISSN
0938-7994
- Publication type
journal article
- DOI
10.1007/s00330-020-07617-8