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Editorial

PET/CT in Prostate Cancer

by
Laura Evangelista
1,2,* and
Stefano Fanti
3,4
1
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Italy
2
IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Italy
3
Division of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
4
Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(15), 3751; https://doi.org/10.3390/cancers15153751
Submission received: 18 July 2023 / Accepted: 21 July 2023 / Published: 25 July 2023
(This article belongs to the Special Issue PET/CT in Prostate Cancer)
Over the last decade, PET/CT has played a crucial role in managing patients with prostate cancer (PCa), significantly impacting various aspects of the disease. A search on PubMed using MESH terms “Positron Emission Tomography” and “Prostate Cancer” yielded over 3100 papers, with the highest number published in 2020 (n = 513 articles). The introduction of new agents like 11C/18F-Choline, 18F-Fluciclovine, and 68Ga/18F-PSMA has posed a significant challenge, but also holds promise, not only for diagnostics but also for therapeutic applications. Radiolabeled PSMA PET/CT has gained substantial interest, as alternative indications have not been fully explored using 11C/18F-Choline and 18F-Fluciclovine. The role of radiopharmaceutical agents in the initial diagnosis of PCa has become a crucial topic. Additionally, the integration of artificial intelligence has emerged as an interesting avenue for improvement in this field.
This special issue includes 14 papers discussing the aforementioned topics, and they are summarized in Table 1, of these papers, ten are original articles, and the remaining four are expert and systematic reviews. The topics covered range from machine learning applications [1,2,3,4] to the detection rate and diagnostic performance of PET/CT imaging [5,6,7,8] and its prognostic value [8,9,10,11,12]. There are also diverse topics covered in the papers [13,14]. Radiolabeled PSMA was commonly used in several papers [1,2,3,5,13,14], along with radiolabeled choline in three papers [4,8,9], and 18F-Fluciclovine in one study [6]. Additionally, two papers employed dual-tracer PET imaging (18F-FDG and radiolabeled PSMA) as a biomarker for predicting a theragnostic approach with 177Lu-PSMA [11,12]. Another paper focused on the treatment of metastatic castrate-resistant PCa (crPCa) with 223Ra [10].
The key take-home messages from these manuscripts are as follows:
  • Radiomic analysis enhances prostate lesion detection and tumor grading prediction, especially for intermediate ISUP-grade lesions or in cases with an undefined PIRADS-3 score. It can also aid in improving the detection of liver metastases in crPCa, as radiolabeled PSMA, mainly 18F-PSMA, may have limitations in physiological biodistribution.
  • The findings of 68Ga-PSMA PET/CT can predict progression-free survival (PFS) in oligometastatic patients treated with metastatic-directed therapy (MDT). Dual-imaging PET with PSMA and FDG before 177Lu-PSMA-617 treatment appears significant, but the meaning of the imaging mismatch should be further evaluated.
  • In the oligometastatic setting, PSMA PET should be carefully interpreted and considered in clinical trials, as about 20% of patients only exhibit PSMA uptake in the bone, and half of them may benefit from MDT.
  • There are unresolved issues including the role of PSMA PET in the initial diagnosis compared to mpMRI, and the utility of PET parameters for evaluating treatment response.
In conclusion, this Special Issue presents valuable research that can enhance the management and prognosis of PCa patients, with the potential to have a significant impact on their outcomes.

Author Contributions

Conceptualization, L.E. and S.F.; writing—original draft preparation, L.E.; writing—review and editing, L.E. and S.F. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gaudiano, C.; Mottola, M.; Bianchi, L.; Corcioni, B.; Braccischi, L.; Tomassoni, M.T.; Cattabriga, A.; Cocozza, M.A.; Giunchi, F.; Schiavina, R.; et al. An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience. Cancers 2023, 15, 3438. [Google Scholar] [CrossRef] [PubMed]
  2. Mattoni, S.; Farolfi, A.; Formaggio, F.; Bruno, G.; Caroli, P.; Cerci, J.J.; Eiber, M.; Fendler, W.P.; Golfieri, R.; Herrmann, K.; et al. PSMA PET for the Evaluation of Liver Metastases in Castration-Resistant Prostate Cancer Patients: A Multicenter Retrospective Study. Cancers 2022, 14, 5680. [Google Scholar] [CrossRef] [PubMed]
  3. Feliciani, G.; Celli, M.; Ferroni, F.; Menghi, E.; Azzali, I.; Caroli, P.; Matteucci, F.; Barone, D.; Paganelli, G.; Sarnelli, A. Radiomics Analysis on [68Ga]Ga-PSMA-11 PET and MRI-ADC for the Prediction of Prostate Cancer ISUP Grades: Preliminary Results of the BIOPSTAGE Trial. Cancers 2022, 14, 1888. [Google Scholar] [CrossRef] [PubMed]
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  8. Alongi, P.; Laudicella, R.; Lanzafame, H.; Farolfi, A.; Mapelli, P.; Picchio, M.; Burger, I.A.; Iagaru, A.; Minutoli, F.; Evangelista, L. 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 2022, 14, 1770. [Google Scholar] [CrossRef] [PubMed]
  9. Lanfranchi, F.; Belgioia, L.; Marcenaro, M.; Zanardi, E.; Timon, G.; Riondato, M.; Giasotto, V.; Zawaideh, J.P.; Tomasello, L.; Mantica, G.; et al. Oligometastatic Prostate Cancer Treated with Metastasis-Directed Therapy Guided by Positron Emission Tomography: Does the Tracer Matter? Cancers 2023, 15, 323. [Google Scholar] [CrossRef] [PubMed]
  10. Bauckneht, M.; Rebuzzi, S.E.; Ponzano, M.; Borea, R.; Signori, A.; Frantellizzi, V.; Lodi Rizzini, E.; Mascia, M.; Lavelli, V.; Miceli, A.; et al. Prognostic Value of the BIO-Ra Score in Metastatic Castration-Resistant Prostate Cancer Patients Treated with Radium-223 after the European Medicines Agency Restricted Use: Secondary Investigations of the Multicentric BIO-Ra Study. Cancers 2022, 14, 1744. [Google Scholar] [CrossRef] [PubMed]
  11. Hartrampf, P.E.; Lapa, C.; Serfling, S.E.; Buck, A.K.; Seitz, A.K.; Meyer, P.T.; Ruf, J.; Michalski, K. Development of Discordant Hypermetabolic Prostate Cancer Lesions in the Course of [177Lu]PSMA Radioligand Therapy and Their Possible Influence on Patient Outcome. Cancers 2021, 13, 4270. [Google Scholar] [CrossRef] [PubMed]
  12. Khreish, F.; Ribbat, K.; Bartholomä, M.; Maus, S.; Stemler, T.; Hierlmeier, I.; Linxweiler, J.; Schreckenberger, M.; Ezziddin, S.; Rosar, F. Value of Combined PET Imaging with [18F]FDG and [68Ga]Ga-PSMA-11 in mCRPC Patients with Worsening Disease during [177Lu]Lu-PSMA-617 RLT. Cancers 2021, 13, 4134. [Google Scholar] [CrossRef] [PubMed]
  13. Vetrone, L.; Mei, R.; Bianchi, L.; Giunchi, F.; Farolfi, A.; Castellucci, P.; Droghetti, M.; Presutti, M.; Degiovanni, A.; Schiavina, R.; et al. Histology and PSMA Expression on Immunohistochemistry in High-Risk Prostate Cancer Patients: Comparison with 68Ga-PSMA PET/CT Features in Primary Staging. Cancers 2023, 15, 1716. [Google Scholar] [CrossRef] [PubMed]
  14. Sabbagh, A.; Mohamad, O.; Lichter, K.E.; Hope, T.A. Management of Patients with Recurrent and Metachronous Oligometastatic Prostate Cancer in the Era of PSMA PET. Cancers 2022, 14, 6194. [Google Scholar] [CrossRef] [PubMed]
Table 1. Summary of the published papers in this Special Issue (according to the website: https://www.mdpi.com/journal/cancers/special_issues/PET_CT_Prostate_Cancer, accessed on 17 July 2023).
Table 1. Summary of the published papers in this Special Issue (according to the website: https://www.mdpi.com/journal/cancers/special_issues/PET_CT_Prostate_Cancer, accessed on 17 July 2023).
Authors, Ref.Type of ArticleNumber of PatientsNumber of PapersTopicSetting of Disease (Agent)
Gaudiano et al. [1]Original article133-Machine learningDiagnosis (PSMA)
Serani et al. [5]Original article179-Detection rateInitial staging and recurrent disease (PSMA)
Vetrone et al. [13]Original article44-Semiquantitative PET dataStaging (PSMA)
Lanfranchi et al. [9]Original article37-Prognostic valueRecurrent disease OMD (PSMA and Choline)
Mattoni et al. [2]Original article60-Radiomics analysisInitial staging and recurrent disease (PSMA)
Feliciani et al. [3]Original article28-Radiomics analysisDiagnosis
(PSMA)
Bauckneht et al. [10]Original article494-Prognostic valueMetastatic crPCa
(223Ra)
Nappi et al. [6]Original article58-Detection rate and performanceRecurrent disease
(18F-Fluciclovine)
Harthrampf et al. [11]Original article32-Prognostic valueMetastatic crPCa (PSMA + FDG)
Khreish et al. [12]Original article29-Prognostic valueMetastatic crPCa (PSMA + FDG)
Sabbagh et al. [14]Expert Review-NAOMDPSMA
Caracciolo et al. [7]Systematic Review-8Diagnostic performanceDiagnosis (PSMA)
Alongi et al. [8]Systematic review-40Diagnostic performance and prognostic valueAssessment of response to therapy (Choline and PSMA)
Guglielmo et al. [4]Systematic review-6Radiomics analysisStaging (PSMA and Choline)
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Evangelista, L.; Fanti, S. PET/CT in Prostate Cancer. Cancers 2023, 15, 3751. https://doi.org/10.3390/cancers15153751

AMA Style

Evangelista L, Fanti S. PET/CT in Prostate Cancer. Cancers. 2023; 15(15):3751. https://doi.org/10.3390/cancers15153751

Chicago/Turabian Style

Evangelista, Laura, and Stefano Fanti. 2023. "PET/CT in Prostate Cancer" Cancers 15, no. 15: 3751. https://doi.org/10.3390/cancers15153751

APA Style

Evangelista, L., & Fanti, S. (2023). PET/CT in Prostate Cancer. Cancers, 15(15), 3751. https://doi.org/10.3390/cancers15153751

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