Skip to main content
SpringerLink
Log in

Prediction of High Pathological Grade in Prostate Cancer Patients Undergoing [18F]-PSMA PET/CT: A Preliminary Radiomics Study

  • Conference paper
  • First Online:
Image Analysis and Processing - ICIAP 2023 Workshops (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14366))

Included in the following conference series:

  • 185 Accesses

Abstract

The aim of this study was to evaluate the effectiveness of [18F]-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging in discriminating high pathological grade (Gleason score > 7), and low pathological grade (Gleason score < 7) using machine learning techniques. The study involved 81 patients with diagnosed prostate cancer who underwent positive [18F]-SPMA PET/CT scans. The PET images were used to identify the primary lesions, and then radiomics analyses were performed using an Imaging Biomarker Standardization Initiative (IBSI) compliant software, namely matRadiomics. Machine learning approaches were employed to identify relevant radiomics features for predicting high-risk malignant disease. The performance of the models was validated using 10 times repeated 5-fold cross validation scheme. The results showed a value of 0.75 for the area under the curve and an accuracy of 72% using the support vector machine (SVM). In conclusion, the study showcased the clinical potential of [18F]-SPMA PET/CT radiomics in differentiating high-risk and low-risk tumors, without the need for biopsy sampling. In-vivo PET/CT imaging could therefore be considered a noninvasive tool for virtual biopsy, facilitating personalized treatment management.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Banna, G.L., et al.: Predictive and prognostic value of early disease progression by PET evaluation in advanced non-small cell lung cancer. Oncology (Switzerland) 92, 39–47 (2017). https://doi.org/10.1159/000448005

    Article  Google Scholar 

  2. Cutaia, G., et al.: Radiomics and prostate MRI: current role and future applications. J. Imaging. 7, 34 (2021). https://doi.org/10.3390/jimaging7020034

    Article  Google Scholar 

  3. Torrisi, S.E., et al.: Assessment of survival in patients with idiopathic pulmonary fibrosis using quantitative HRCT indexes. Multidiscip Respir Med. 13, 1–8 (2018). https://doi.org/10.1186/s40248-018-0155-2

    Article  Google Scholar 

  4. Liberini, V., et al.: Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics. Eur Radiol Exp. 6 (2022). https://doi.org/10.1186/S41747-022-00282-0

  5. Vernuccio, F., et al.: Lo: diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis? Br. J. Radiol. 94 (2021). https://doi.org/10.1259/bjr.20210340

  6. Alongi, P., et al.: 18F-Florbetaben PET/CT to assess Alzheimer’s disease: a new analysis method for regional amyloid quantification. J. Neuroimaging 29, 383–393 (2019). https://doi.org/10.1111/jon.12601

    Article  Google Scholar 

  7. Castiglioni, I., Gilardi, M.C.: Radiomics: is it time to compose the puzzle? Clin Transl Imaging. (2018). https://doi.org/10.1007/s40336-018-0302-y

    Article  Google Scholar 

  8. Vernuccio, F., Cannella, R., Comelli, A., Salvaggio, G., Lagalla, R., Midiri, M.: Radiomics and artificial intelligence: new frontiers in medicine. Recent. Prog. Med. 111, 130–135 (2020). https://doi.org/10.1701/3315.32853

  9. Pasini, G., Stefano, A., Russo, G., Comelli, A., Marinozzi, F., Bini, F.: Phenotyping the histopathological subtypes of non-small-cell lung carcinoma: how beneficial is radiomics? Diagnostics 13 (2023). https://doi.org/10.3390/diagnostics13061167

  10. Russo, G., et al.: Feasibility on the use of radiomics features of 11[C]-MET PET/CT in central nervous system tumours: Preliminary results on potential grading discrimination using a machine learning model. Curr. Oncol. 28, 5318–5331 (2021). https://doi.org/10.3390/curroncol28060444

    Article  Google Scholar 

  11. Laudicella, R., et al.: Artificial neural networks in cardiovascular diseases and its potential for clinical application in molecular imaging. Curr. Radiopharm. 14, 209–219 (2020). https://doi.org/10.2174/1874471013666200621191259

    Article  Google Scholar 

  12. Benfante, V., et al.: A new preclinical decision support system based on PET radiomics: a preliminary study on the evaluation of an innovative 64Cu-labeled chelator in mouse models. J. Imaging. 8, 92 (2022). https://doi.org/10.3390/jimaging8040092

    Article  Google Scholar 

  13. Cuocolo, R., et al.: Clinically significant prostate cancer detection on MRI: a radiomic shape features study. Eur. J. Radiol. 116, 144–149 (2019). https://doi.org/10.1016/j.ejrad.2019.05.006

    Article  Google Scholar 

  14. Alongi, P., et al.: PSMA and choline PET for the assessment of response to therapy and survival outcomes in prostate cancer patients: a systematic review from the literature. Cancers (Basel) 14 (2022). https://doi.org/10.3390/CANCERS14071770

  15. Evangelista, L., et al.: [68Ga]Ga-PSMA Versus [18F]PSMA positron emission tomography/computed tomography in the staging of primary and recurrent prostate cancer. A systematic review of the literature. Eur. Urol. Oncol. 5, 273–282 (2022). https://doi.org/10.1016/J.EUO.2022.03.004

  16. Laudicella, R., et al.: Preliminary findings of the role of FAPi in prostate cancer theranostics. Diagnostics (Basel) 13 (2023). https://doi.org/10.3390/DIAGNOSTICS13061175

  17. Pasini, G., Bini, F., Russo, G., Marinozzi, F., Stefano, A.: matRadiomics: from biomedical image visualization to predictive model implementation. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds.) ICIAP 2022. LNCS, vol. 13373, pp. 374–385. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13321-3_33

  18. pyradiomics Documentation Release v3.0.post5+gf06ac1d pyradiomics community (2020)

    Google Scholar 

  19. Barone, S., et al.: Hybrid descriptive-inferential method for key feature selection in prostate cancer radiomics. Appl. Stoch Models Bus. Ind. 37, 961–972 (2021). https://doi.org/10.1002/asmb.2642

    Article  MathSciNet  Google Scholar 

  20. Comelli, A., et al.: A kernel support vector machine based technique for Crohn’s disease classification in human patients. In: Barolli, L., Terzo, O. (eds.) CISIS 2017. AISC, vol. 611, pp. 262–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61566-0_25

    Chapter  Google Scholar 

  21. Horng, H., et al.: Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects. Sci. Rep. 12, 1–12 (2022). https://doi.org/10.1038/s41598-022-08412-9

    Article  Google Scholar 

  22. Ferraro, D.A., et al.: Hot needles can confirm accurate lesion sampling intraoperatively using [18F]PSMA-1007 PET/CT-guided biopsy in patients with suspected prostate cancer. Eur. J. Nucl. Med. Mol. Imaging 49, 1721–1730 (2022). https://doi.org/10.1007/S00259-021-05599-3

    Article  Google Scholar 

  23. Laudicella, R., et al.: [68 Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [177 Lu]DOTATOC PRRT: The “Theragnomics” Concept. Cancers (Basel) 14, 984 (2022). https://doi.org/10.3390/cancers14040984

  24. Stefano, A., et al.: Robustness of pet radiomics features: impact of co-registration with MRI. Appl. Sci. (Switzerland) 11, 10170 (2021). https://doi.org/10.3390/app112110170

  25. Stefano, A., et al.: A fully automatic method for biological target volume segmentation of brain metastases. Int. J. Imaging Syst. Technol. 26, 29–37 (2016). https://doi.org/10.1002/ima.22154

  26. Stefano, A., et al.: A graph-based method for PET image segmentation in radiotherapy planning: a pilot study. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 711–720. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41184-7_72

    Chapter  Google Scholar 

  27. Comelli, A., et al.: Tissue classification to support local active delineation of brain tumors. In: Zheng, Y., Williams, B.M., Chen, K. (eds.) MIUA 2019. CCIS, vol. 1065, pp. 3–14. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39343-4_1

    Chapter  Google Scholar 

  28. Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23, 903–921 (2004). https://doi.org/10.1109/TMI.2004.828354

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015, Cefalù, Italy

    Alessandro Stefano, Selene Richiusa & Giovanni Pasini

  2. Cannizzaro Hospital, Catania, Italy

    Cristina Mantarro, Maria Gabriella Sabini, Sebastiano Cosentino & Massimo Ippolito

  3. Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184, Rome, Italy

    Giovanni Pasini

Authors
  1. Alessandro Stefano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristina Mantarro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Selene Richiusa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giovanni Pasini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maria Gabriella Sabini
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sebastiano Cosentino
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Massimo Ippolito
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giovanni Pasini .

Editor information

Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stefano, A. et al. (2024). Prediction of High Pathological Grade in Prostate Cancer Patients Undergoing [18F]-PSMA PET/CT: A Preliminary Radiomics Study. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14366. Springer, Cham. https://doi.org/10.1007/978-3-031-51026-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-51026-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51025-0

  • Online ISBN: 978-3-031-51026-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation