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01-07-2021 | Prostate Cancer | Nuclear Medicine

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: Pierpaolo Alongi, Alessandro Stefano, Albert Comelli, Riccardo Laudicella, Salvatore Scalisi, Giuseppe Arnone, Stefano Barone, Massimiliano Spada, Pierpaolo Purpura, Tommaso Vincenzo Bartolotta, Massimo Midiri, Roberto Lagalla, Giorgio Russo

Published in: European Radiology | Issue 7/2021

Abstract

Objective

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.

Material and methods

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).

Results

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%.

Conclusion

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.

Key Points

• 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.

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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: Pierpaolo Alongi
Alessandro Stefano
Albert Comelli
Riccardo Laudicella
Salvatore Scalisi
Giuseppe Arnone
Stefano Barone
Massimiliano Spada
Pierpaolo Purpura
Tommaso Vincenzo Bartolotta
Massimo Midiri
Roberto Lagalla
Giorgio Russo
Publication date: 01-07-2021
Publisher: Springer Berlin Heidelberg
Keywords: Prostate Cancer
Prostate Cancer
Positron Emission Tomography
Bone Metastasis
Published in: European Radiology / Issue 7/2021
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07617-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objective

Material and methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Material and methods

Results

Conclusion

Key Points

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