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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

  • Nuclear Medicine
  • Published:
European Radiology Aims and scope Submit manuscript

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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Abbreviations

ADT:

Anti-androgen enzalutamide or abiraterone

AUROC:

Area under the receiver operating characteristics

BS:

Bone scan

ceCT:

Contrast-enhanced computed tomography

Cho-PET:

18F-choline PET/CT

CHT:

Docetaxel or cabazitaxel or estramustine

CI:

Confidence interval

DA:

Discriminant analysis

FU:

Follow-up

GLZLM:

Grey-level zone length matrix

HISTO:

Histogram

HT:

Hormone therapy

M:

Bone metastasis

MR:

Magnetic resonance

N:

Lymph-nodal disease

PCa:

Prostate cancer

PET:

Positron emission tomography

PPV:

Positive predictive value

RT:

Radiotherapy

SUV:

Standardized uptake value

SZLGE:

Short-zone low grey-level emphasis

T:

Primary or local relapse

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Acknowledgments

Thanks to the President of Fondazione Istituto G. Giglio Dr. Salvatore Albano for providing to the Hospital, included Nuclear Medicine Unit, the motivation and the support needed for high quality in clinical and scientific nuclear medicine procedures.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, 90015, Cefalù, PA, Italy

    Pierpaolo Alongi & Salvatore Scalisi

  2. Institute of Molecular Bioimaging and Physiology, National Research Council (CNR), Cefalù, PA, Italy

    Alessandro Stefano & Giorgio Russo

  3. Ri.MED Foundation, Palermo, Italy

    Albert Comelli

  4. Department of Biomedical and Dental Sciences and of Morpho-functional Imaging, Nuclear Medicine Unit, University of Messina, Messina, Italy

    Riccardo Laudicella

  5. Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy

    Giuseppe Arnone, Tommaso Vincenzo Bartolotta, Massimo Midiri & Roberto Lagalla

  6. Dipartimento di Scienze Agronomiche, Alimentari e Forestali (SAAF), University of Palermo, Palermo, Italy

    Stefano Barone

  7. Unit of Oncology, Fondazione Istituto G. Giglio, Cefalù, PA, Italy

    Massimiliano Spada

  8. Department of Radiology, Fondazione Istituto Giuseppe Giglio Ct.da Pietrapollastra, Via Pisciotto, 90015, Cefalù (Palermo), Italy

    Pierpaolo Purpura & Tommaso Vincenzo Bartolotta

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  1. Pierpaolo Alongi
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Corresponding author

Correspondence to Pierpaolo Alongi.

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Guarantor

The scientific guarantor of this publication is Pierpaolo Alongi, Head of Nuclear Medicine Unit at Fondazione Istituto Giglio of Cefalù, Italy.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Two authors have significant statistical expertise (Alessandro Stefano, Albert Comelli).

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported as SNMMI 2020 Congress presentation in “Alongi P, Stefano A, Comelli A, et al (2020) New Artificial intelligence model for 18F-Choline PET/CT in evaluation of high-risk prostate cancer outcome: texture analysis and radiomics features classification for prediction of disease progression. J Nucl Med 61:1303–1303.”

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• performed at one institution

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Alongi, P., Stefano, A., Comelli, A. et al. 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. Eur Radiol 31, 4595–4605 (2021). https://doi.org/10.1007/s00330-020-07617-8

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  • DOI: https://doi.org/10.1007/s00330-020-07617-8

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