Skip to main content

Advertisement

SpringerLink
Log in

The role of PET radiomic features in prostate cancer: a systematic review

  • Systematic Review
  • Published:
Clinical and Translational Imaging Aims and scope Submit manuscript

Abstract

Aim

This systematic review aims to present the available evidence on the use of radiomic features (RFs) extracted from PET imaging in patients with prostate cancer (PCa).

Materials and methods

A comprehensive literature search of studies on the utility of PET-derived RFs in patients with PCa was performed in the PubMed/MEDLINE database through February 24th, 2021 using the following search string: [“positron-emission tomography” (MeSh terms) OR “positron emission tomography computed tomography” (MeSh terms) OR “positron-emission tomography” (all fields) OR “positron emission tomography computed tomography” (all fields) OR “PET” (all fields)] AND [“radiomics” (all fields) OR “radiomic” (all fields) OR “radiogenomics” (all fields) OR “radiogenomic”(all fields) OR “machine learning”(all fields) OR “machine learning”(MeSh terms) OR “artificial intelligence”(MeSh terms) OR “artificial intelligence”(all fields)] AND [“prostatic neoplasms” (MeSh terms) OR “prostate cancer”(all fields) OR “prostatic carcinoma” (all fields) OR “prostate carcinoma” (all fields) OR “prostatic tumor” (all fields) OR “prostatic tumour” (all fields)]. The Google scholar database was interrogated to find additional studies.

Results

Seven studies were ultimately included in the systematic review and summarized in two relevant clinical sections: (1) primary staging and (2) restaging. In primary staging, RFs, extracted from 68 Ga-prostate-specific membrane antigen (PSMA) PET may characterize intraprostatic radiotracer hotspots in patients with high- and intermediate-risk, discriminate between Gleason Score (GS) 7 and ≥ 8 and between pN1 and pN0 disease, and suggest presence of intraprostatic lesions missed at visual PET examination. Machine learning (ML) may help selecting RFs able to predict risk classification (low vs. high), lymph node involvement, presence of nodal or distant metastasis, GS and extracapsular extension. At restaging, PET_Kurtosis may correlate with OS in patients with advanced PCa scheduled for 177Lu-PSMA treatment, whereas ML may assist discrimination of malignant lesions from physiologic/unspecific tracer accumulation, and predict disease progression.

Conclusion

To date, although PET literature appears still too narrow to draw definitive conclusions, PET-derived RFs appear promising in PCa. ML seems an important tool that may contribute to the widespread use of radiomics and subsequent implementation in the clinical setting.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446. https://doi.org/10.1016/j.ejca.2011.11.036

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, Weigelt B, Vargas HA (2017) Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clinl Radiol 72:3–10. https://doi.org/10.1016/j.crad.2016.09.013

    Article  CAS  Google Scholar 

  3. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169

    Article  PubMed  Google Scholar 

  4. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141

    Article  PubMed  Google Scholar 

  5. Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I (2020) Machine and deep learning methods for radiomics. Med Phys 47:e185–e202. https://doi.org/10.1002/mp.13678

    Article  PubMed  Google Scholar 

  6. Parekh VS, Jacobs MA (2019) Deep learning and radiomics in precision medicine. Expert Rev Precis Med Drug Dev 4:59–72. https://doi.org/10.1080/23808993.2019.1585805

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rawla P (2019) Epidemiology of prostate cancer. World J Oncol 10:63–89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Alqahtani S, Wei C, Zhang Y, Szewczyk-Bieda M, Wilson J, Huang Z, Nabi G (2020) Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system. Sci Rep 10:7722. https://doi.org/10.1038/s41598-020-64693-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kelloff GJ, Choyke P, Coffey DS (2009) Challenges in clinical prostate cancer: role of imaging. Am J Roentgenol 192:1455–1470. https://doi.org/10.2214/ajr.09.2579

    Article  Google Scholar 

  10. Alongi P, Laudicella R, Stefano A, Caobelli F, Comelli A, Vento A, Sardina D, Ganduscio G, Toia P, Ceci F, Mapelli P, Picchio M, Midiri M, Baldari S, Lagalla R, Russo G (2020) Choline PET/CT features to predict survival outcome in high risk prostate cancer restaging: a preliminary machine-learning radiomics study. Q J Nucl Med Mol Imaging. https://doi.org/10.23736/s1824-4785.20.03227-6

    Article  PubMed  Google Scholar 

  11. Rayn KN, Elnabawi YA, Sheth N (2018) Clinical implications of PET/CT in prostate cancer management. Transl Androl Urol 7:844–854

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hofman MS, Lawrentschuk N, Francis RJ, Tang C, Vela I, Thomas P, Rutherford N, Martin JM, Frydenberg M, Shakher R, Wong LM, Taubman K, Ting Lee S, Hsiao E, Roach P, Nottage M, Kirkwood I, Hayne D, Link E, Marusic P, Matera A, Herschtal A, Iravani A, Hicks RJ, Williams S, Murphy DG (2020) Prostate-specific membrane antigen PET-CT in patients with high-risk prostate cancer before curative-intent surgery or radiotherapy (proPSMA): a prospective, randomised, multicentre study. Lancet 395:1208–1216. https://doi.org/10.1016/s0140-6736(20)30314-7

    Article  CAS  PubMed  Google Scholar 

  13. Sonni I, Eiber M, Fendler WP, Alano RM, Vangala SS, Kishan AU, Nickols N, Rettig MB, Reiter RE, Czernin J, Calais J (2020) Impact of (68)Ga-PSMA-11 PET/CT on staging and management of prostate cancer patients in various clinical settings: a prospective single-center study. J Nucl Med 61:1153–1160. https://doi.org/10.2967/jnumed.119.237602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6:e1000097. https://doi.org/10.1371/journal.pmed.1000097

    Article  PubMed  PubMed Central  Google Scholar 

  15. Sollini M, Antunovic L, Chiti A, Kirienko M (2019) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 46:2656–2672. https://doi.org/10.1007/s00259-019-04372-x

    Article  PubMed  PubMed Central  Google Scholar 

  16. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009

    Article  PubMed  Google Scholar 

  17. Cysouw MCF, Jansen BHE, van de Brug T, Oprea-Lager DE, Pfaehler E, de Vries BM, van Moorselaar RJA, Hoekstra OS, Vis AN, Boellaard R (2021) Machine learning-based analysis of [(18)F]DCFPyL PET radiomics for risk stratification in primary prostate cancer. Eur J Nucl Med Mol Imaging 48:340–349. https://doi.org/10.1007/s00259-020-04971-z

    Article  CAS  PubMed  Google Scholar 

  18. Papp L, Spielvogel CP, Grubmüller B, Grahovac M, Krajnc D, Ecsedi B, Sareshgi RAM, Mohamad D, Hamboeck M, Rausch I, Mitterhauser M, Wadsak W, Haug AR, Kenner L, Mazal P, Susani M, Hartenbach S, Baltzer P, Helbich TH, Kramer G, Shariat SF, Beyer T, Hartenbach M, Hacker M (2020) Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [(68)Ga]Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-020-05140-y

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zamboglou C, Bettermann AS, Gratzke C, Mix M, Ruf J, Kiefer S, Jilg CA, Benndorf M, Spohn S, Fassbender TF, Bronsert P, Chen M, Guo H, Wang F, Qiu X, Grosu AL (2020) Uncovering the invisible-prevalence, characteristics, and radiomics feature-based detection of visually undetectable intraprostatic tumor lesions in (68)GaPSMA-11 PET images of patients with primary prostate cancer. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-020-05111-3

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zamboglou C, Carles M, Fechter T, Kiefer S, Reichel K, Fassbender TF, Bronsert P, Koeber G, Schilling O, Ruf J, Werner M, Jilg CA, Baltas D, Mix M, Grosu AL (2019) Radiomic features from PSMA PET for non-invasive intraprostatic tumor discrimination and characterization in patients with intermediate- and high-risk prostate cancer - a comparison study with histology reference. Theranostics 9:2595–2605. https://doi.org/10.7150/thno.32376

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Moazemi S, Khurshid Z, Erle A, Lütje S, Essler M, Schultz T, Bundschuh RA (2020) Machine learning facilitates hotspot classification in PSMA-PET/CT with nuclear medicine specialist accuracy. Diagnostics (Basel) 10:622. https://doi.org/10.3390/diagnostics10090622

    Article  CAS  Google Scholar 

  22. Moazemi S, Erle A, Lütje S, Gaertner FC, Essler M, Bundschuh RA (2021) Estimating the potential of radiomics features and radiomics signature from pretherapeutic PSMA-PET-CT scans and clinical data for prediction of overall survival when treated with (177)Lu-PSMA. Diagnostics (Basel). https://doi.org/10.3390/diagnostics11020186

    Article  Google Scholar 

  23. Alongi P, Stefano A, Comelli A, Laudicella R, Scalisi S, Arnone G, Barone S, Spada M, Purpura P, Bartolotta TV, Midiri M, Lagalla R, Russo G (2021) 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. https://doi.org/10.1007/s00330-020-07617-8

    Article  PubMed  Google Scholar 

  24. Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol 46:2638–2655. https://doi.org/10.1007/s00259-019-04391-8

    Article  Google Scholar 

  25. Domachevsky L, Goldberg N, Bernstine H, Nidam M, Groshar D (2018) Quantitative characterisation of clinically significant intra-prostatic cancer by prostate-specific membrane antigen (PSMA) expression and cell density on PSMA-11. Eur Radiol 28:5275–5283. https://doi.org/10.1007/s00330-018-5484-1

    Article  PubMed  Google Scholar 

  26. PSMA-PET: deep radiomic biomarkers of progression and response prediction in prostate cancer. https://ClinicalTrials.gov/show/NCT03594760

Download references

Funding

All the authors declare that they did not receive any financial support for this study.

Author information

Author notes

  1. Natale Quartuccio and Maurizio Marrale contributed equally to this work.

Authors and Affiliations

  1. Nuclear Medicine Unit, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, 90128, Palermo, Italy

    Natale Quartuccio, Letterio Sturiale, Giuseppe Arnone & Gaspare Arnone

  2. Department of Physics and Chemistry—Emilio Segrè, University of Palermo, Palermo, Italy

    Maurizio Marrale

  3. Istituto Nazionale di Fisica Nucleare, Sezione of Catania, Catania, Italy

    Maurizio Marrale

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

    Riccardo Laudicella, Massimiliano Siracusa & Sergio Baldari

  5. Nuclear Medicine Unit, Fondazione Istituto G. Giglio, Contrada Pietrapollastra Pisciotto, Cefalù, Italy

    Pierpaolo Alongi

  6. Section of Radiology—BiND, University Hospital “Paolo Giaccone”, Via del Vespro 129, 90127, Palermo, Italy

    Giuseppe Cutaia, Giuseppe Salvaggio & Massimo Midiri

Authors
  1. Natale Quartuccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maurizio Marrale
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Riccardo Laudicella
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pierpaolo Alongi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Massimiliano Siracusa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Letterio Sturiale
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Giuseppe Arnone
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giuseppe Cutaia
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Giuseppe Salvaggio
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Massimo Midiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sergio Baldari
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Gaspare Arnone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pierpaolo Alongi.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quartuccio, N., Marrale, M., Laudicella, R. et al. The role of PET radiomic features in prostate cancer: a systematic review. Clin Transl Imaging 9, 579–588 (2021). https://doi.org/10.1007/s40336-021-00436-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40336-021-00436-x

Keywords

Search

Navigation