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Karimzadeh A, Hansen K, Hein S, Haller B, Heck MM, Tauber R, D Alessandria C, Eiber M, Rauscher I. Impact of baseline 18F-flotufolastat PET bone tumor volume for prognosticating severe hematologic toxicity in patients with metastatic castration-resistant prostate Cancer receiving 177Lu-PSMA-targeted radioligand therapy. Eur J Nucl Med Mol Imaging 2025:10.1007/s00259-025-07200-7. [PMID: 40383857 DOI: 10.1007/s00259-025-07200-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2025] [Accepted: 03/06/2025] [Indexed: 05/20/2025]
Abstract
PURPOSE This retrospective analysis evaluated the prognostic value of baseline 18F-flotufolastat-PET bone tumor metrics for severe hematologic toxicity in metastatic castration-resistant prostate cancer (mCRPC) patients treated with [177Lu]Lu-PSMA-I&T. METHODS Data from 182 mCRPC patients with baseline 18F-flotufolastat-PET scans and complete hematologic profiles were analyzed. Bone lesions were semiautomatically delineated, and clinical parameters (e.g., pretreatments, lab results) were assessed. Hematologic adverse events (AEs) were defined per Common Terminology Criteria for Adverse Events version 5.0, with grades 3-4 considered severe. Cox regression was used to identify prognostic factors for AEs. RESULTS Baseline bone tumor volume prognosticated leukocytopenia (HR 1.03 per 100 ml, p = 0.036), while the number of bone lesions was prognostic for anemia (HR 1.04 per 10 lesions, p < 0.001) and severe anemia (HR per 10 lesions 1.05, p = 0.009). Higher baseline hemoglobin correlated with reduced leukocytopenia (HR 0.74, p = 0.002), thrombocytopenia (HR 0.80, p = 0.033), and severe anemia (HR 0.52, p < 0.001). Baseline kidney dysfunction was linked to anemia (HR 2.46, p = 0.002) and severe anemia (HR 3.81, p = 0.023). Prior [223Ra]Radiumdichloride treatment prognosticated severe thrombocytopenia (HR 6.43, p = 0.021). CONCLUSION Baseline 18F-flotufolastat-PET metrics and pretherapeutic clinical parameters are key prognostic factors for severe hematologic toxicity in mCRPC patients treated with [177Lu]Lu-PSMA-I&T.
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Affiliation(s)
- Amir Karimzadeh
- Department of Nuclear Medicine, School of Medicine and Health, TUM University Hospital, Munich, Germany.
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
| | - Kimberley Hansen
- Department of Nuclear Medicine, School of Medicine and Health, TUM University Hospital, Munich, Germany
| | - Stefan Hein
- Department of Nuclear Medicine, School of Medicine and Health, TUM University Hospital, Munich, Germany
| | - Bernhard Haller
- School of Medicine and Health, Institute of AI and Informatics in Medicine, Technical University of Munich, TUM University Hospital, Munich, Germany
| | - Matthias M Heck
- Department of Urology, School of Medicine and Health, TUM University Hospital, Munich, Germany
| | - Robert Tauber
- Department of Urology, School of Medicine and Health, TUM University Hospital, Munich, Germany
| | - Calogero D Alessandria
- Department of Nuclear Medicine, School of Medicine and Health, TUM University Hospital, Munich, Germany
| | - Matthias Eiber
- Department of Nuclear Medicine, School of Medicine and Health, TUM University Hospital, Munich, Germany
- Bavarian Cancer Research Center, Munich, Germany
| | - Isabel Rauscher
- Department of Nuclear Medicine, School of Medicine and Health, TUM University Hospital, Munich, Germany
- Bavarian Cancer Research Center, Munich, Germany
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Karimzadeh A, Hansen K, Hasa E, Haller B, Heck MM, Tauber R, D Alessandria C, Weber WA, Eiber M, Rauscher I. Prognostic 18F-flotufolastat PET parameters for outcome assessment of 177Lu-labeled PSMA-targeted radioligand therapy in metastatic castration-resistant prostate cancer. Eur J Nucl Med Mol Imaging 2025; 52:2041-2050. [PMID: 39847077 PMCID: PMC12014739 DOI: 10.1007/s00259-024-07003-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2024] [Accepted: 11/24/2024] [Indexed: 01/24/2025]
Abstract
PURPOSE This retrospective analysis evaluates baseline 18F-flotufolastat positron emission tomography (PET) parameters as prognostic parameters for treatment response and outcome in patients with metastatic castration-resistant prostate cancer (mCRPC) undergoing treatment with [177Lu]Lu-PSMA-I&T. METHODS A total of 188 mCRPC patients with baseline 18F-flotufolastat PET scans were included. Tumor lesions were semiautomatically delineated, with imaging parameters including volume-based and standardized uptake value (SUV)-based metrics. Outcome measures included prostate-specific antigen (PSA) response, PSA-progression-free survival (PSA-PFS), and overall survival (OS). Univariate and multivariate regression analyses assessed the impact of baseline imaging and pretherapeutic clinical parameters on outcome. Event time distributions were estimated with the Kaplan-Meier method, and groups were compared with log-rank tests. RESULTS Significant prognostic parameters for PSA response and PSA-PFS included log-transformed whole-body SUVmax (odds ratio (OR), 3.26, 95% confidence interval (CI), 2.01-5.55 and hazard ratio (HR), 0.51, 95% CI, 0.4-0.66; both p < 0.001) and prior chemotherapy (OR 0.3, 95% CI, 0.12-0.72 and HR 1.64, 95% CI, 1.07-2.58; p = 0.008 and p = 0.028, respectively). For OS, significant prognosticators were the following log-transformed parameters: number of lesions (HR 1.38, 95% CI, 1.24-1.53; p < 0.001), TTV (HR 1.27, 95% CI, 1.18-1.37; p < 0.001), and ITLV (HR 1.24, 95% CI, 1.16-1.33; p < 0.001), with log-transformed TTV (HR 1.15, 95% CI, 1.04-1.27; p = 0.008) remaining significant in multivariate analysis. CONCLUSION At baseline, SUV-based 18F-flotufolastat PET metrics (e.g., whole-body SUVmax) serve as significant positive prognosticators for short-term outcomes (PSA response and PSA-PFS). In contrast, volume-based metrics (e.g., TTV) are significant negative prognosticators for long-term outcome (OS), in mCRPC patients treated with [177Lu]Lu-PSMA-I&T.
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Affiliation(s)
- Amir Karimzadeh
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
| | - Kimberley Hansen
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Ergela Hasa
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Bernhard Haller
- Institute of AI and Informatics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Matthias M Heck
- Department of Urology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Robert Tauber
- Department of Urology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Calogero D Alessandria
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Wolfgang A Weber
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Bavarian Cancer Research Center, Munich, Germany
| | - Matthias Eiber
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Bavarian Cancer Research Center, Munich, Germany
| | - Isabel Rauscher
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
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Gafita A, Djaileb L, Calais J, Eiber M, Fendler WP. RECIP 1.0: A Roadmap for Clinical Implementation. J Nucl Med 2025; 66:673-675. [PMID: 40147848 DOI: 10.2967/jnumed.124.268730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Accepted: 03/03/2025] [Indexed: 03/29/2025] Open
Affiliation(s)
- Andrei Gafita
- Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland;
| | - Loic Djaileb
- LRB, Nuclear Medicine Department, CHU Grenoble Alpes, INSERM, Université Grenoble Alpes, Grenoble, France
| | - Jeremie Calais
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Matthias Eiber
- Department of Nuclear Medicine, Technical University of Munich, Munich, Germany; and
| | - Wolfgang P Fendler
- Department of Nuclear Medicine, University Hospital Essen, German Cancer Consortium, West German Cancer Center, Essen, Germany
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Liu J, Sandhu K, Woon DTS, Perera M, Lawrentschuk N. The Value of Artificial Intelligence in Prostate-Specific Membrane Antigen Positron Emission Tomography: An Update. Semin Nucl Med 2025; 55:371-376. [PMID: 39893058 DOI: 10.1053/j.semnuclmed.2024.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2024] [Revised: 12/15/2024] [Accepted: 12/17/2024] [Indexed: 02/04/2025]
Abstract
This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scans for prostate cancer (PCa). A literature review was conducted on the Medline, Embase, Web of Science, and IEEE Xplore databases. The search focused on studies that utilizes AI to evaluate PSMA PET scans. Original English language studies published from inception to October 2024 were included, while case reports, series, commentaries, and conference proceedings were excluded. AI applications show promise in automating the detection of metastatic disease and anatomical segmentation in PSMA PET scans. AI was also able to predict response to PSMA-based theragnostic and aids in tumor burden segmentation, improving radiotherapy planning. AI could also differentiate intraprostatic PCa with higher histological grade and predict extra-prostatic extension. AI has potential in evaluating PSMA PET scans for PCa, particularly in detecting metastasis, measuring tumor burden, detecting high grade intraprostatic cancer, and predicting treatment outcomes. Larger multicenter prospective studies are necessary to validate and enhance the generalizability of these AI models.
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Affiliation(s)
- Jianliang Liu
- EJ Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, Australia; Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia; University of Melbourne, Department of Surgery, Melbourne, Australia; Department of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Kieran Sandhu
- Department of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Dixon T S Woon
- EJ Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, Australia; University of Melbourne, Department of Surgery, Melbourne, Australia
| | - Marlon Perera
- University of Melbourne, Department of Surgery, Melbourne, Australia; Department of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Nathan Lawrentschuk
- EJ Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, Australia; Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, Australia; University of Melbourne, Department of Surgery, Melbourne, Australia; Department of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia.
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Belliveau C, Benhacene-Boudam MK, Juneau D, Plouznikoff N, Olivié D, Alley S, Barkati M, Delouya G, Taussky D, Lambert C, Beauchemin MC, Ménard C. F 18-DCFPyL PSMA-PET/CT Versus MRI: Identifying the Prostate Cancer Region Most at Risk of Radiation Therapy Recurrence for Tumor Dose Escalation. Pract Radiat Oncol 2025; 15:160-168. [PMID: 39818681 DOI: 10.1016/j.prro.2024.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/12/2024] [Accepted: 09/04/2024] [Indexed: 01/18/2025]
Abstract
PURPOSE Local recurrence of prostate cancer (PCa) after radiation therapy (RT) typically occurs at the site of dominant tumor burden, and recent evidence confirms that magnetic resonance imaging (MRI) guided tumor dose escalation improves outcomes. With the emergence of prostate-specific membrane antigen (PSMA) positron emission tomography (PET), we hypothesize that PSMA-PET and MRI may not equally depict the region most at risk of recurrence after RT. METHODS AND MATERIALS Patients with intermediate- to high-risk PCa and MRI plus PSMA-PET performed before RT were identified. The sextant most at risk of recurrence was defined as the pathologically dominant region with peak biopsy percentage core length involvement and any sextant with ≥ 40% percentage core length involvement (pathologic gross tumor volume [pGTV], per prior work). Imaging methods were reviewed independently to compare GTVs with pGTVs most at risk of recurrence. A paired chi-square test was employed for analysis. RESULTS Eighty-eight patients (n = 88) were identified. Overall, there were no differences in the sensitivity of MRI and PSMA-PET for identifying the pGTV most at risk of recurrence. However, PSMA-PET demonstrated a trend of improved sensitivity for high-risk PCa compared with MRI (n = 46, 96% vs 87%, P = .06), while MRI outperformed PSMA-PET for the intermediate-risk group (n = 42, 93% vs 81%, P = .03). PSMA-PET showed lower specificity, misidentifying GTV in uninvolved pathologic sextants for 12% of intermediate-risk patients, whereas MRI was faultless (12% vs 0%, P = .03). MRI and PSMA-PET each misidentified uninvolved sextants for 9% of patients in the high-risk group. CONCLUSIONS MRI demonstrates superior sensitivity in identifying the region most at risk of RT recurrence for intermediate-risk PCa, whereas PSMA-PET may add value for some high-risk patients. Informed by sextant biopsy information and MRI, clinicians should consider integrating PSMA-PET for patients with high-risk diseases when delineating GTVs.
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Affiliation(s)
- Colin Belliveau
- Radiation Oncology, Centre Hospitalier de l'Université de Montréal (CHUM), Quebec, Canada.
| | | | - Daniel Juneau
- Nuclear Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada
| | - Nicolas Plouznikoff
- Nuclear Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada
| | - Damien Olivié
- Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada
| | | | - Maroie Barkati
- Radiation Oncology, Centre Hospitalier de l'Université de Montréal (CHUM), Quebec, Canada
| | - Guila Delouya
- Radiation Oncology, Centre Hospitalier de l'Université de Montréal (CHUM), Quebec, Canada
| | - Daniel Taussky
- Radiation Oncology, Centre Hospitalier de l'Université de Montréal (CHUM), Quebec, Canada
| | - Carole Lambert
- Radiation Oncology, Centre Hospitalier de l'Université de Montréal (CHUM), Quebec, Canada
| | | | - Cynthia Ménard
- Radiation Oncology, Centre Hospitalier de l'Université de Montréal (CHUM), Quebec, Canada
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Gafita A, Martin AJ, Emmett L, Eiber M, Iravani A, Fendler WP, Buteau J, Sandhu S, Azad AA, Herrmann K, Stockler MR, Davis ID, Hofman MS. Validation of Prognostic and Predictive Models for Therapeutic Response in Patients Treated with [ 177Lu]Lu-PSMA-617 Versus Cabazitaxel for Metastatic Castration-resistant Prostate Cancer (TheraP): A Post Hoc Analysis from a Randomised, Open-label, Phase 2 Trial. Eur Urol Oncol 2025; 8:21-28. [PMID: 38584037 DOI: 10.1016/j.euo.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
BACKGROUND Prognostic models have been developed using data from a multicentre noncomparative study to forecast the likelihood of a 50% reduction in prostate-specific antigen (PSA50), longer prostate-specific antigen (PSA) progression-free survival (PFS), and longer overall survival (OS) in patients with metastatic castration-resistant prostate cancer receiving [177Lu]Lu-PSMA radioligand therapy. The predictive utility of the models to identify patients likely to benefit most from [177Lu]Lu-PSMA compared with standard chemotherapy has not been established. OBJECTIVE To determine the predictive value of the models using data from the randomised, open-label, phase 2, TheraP trial (primary objective) and to evaluate the clinical net benefit of the PSA50 model (secondary objective). DESIGN, SETTING, AND PARTICIPANTS All 200 patients were randomised in the TheraP trial to receive [177Lu]Lu-PSMA-617 (n = 99) or cabazitaxel (n = 101) between February 2018 and September 2019. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Predictive performance was investigated by testing whether the association between the modelled outcome classifications (favourable vs unfavourable outcome) was different for patients randomised to [177Lu]Lu-PSMA versus cabazitaxel. The clinical benefit of the PSA50 model was evaluated using a decision curve analysis. RESULTS AND LIMITATIONS The probability of PSA50 in patients classified as having a favourable outcome was greater in the [177Lu]Lu-PSMA-617 group than in the cabazitaxel group (odds ratio 6.36 [95% confidence interval {CI} 1.69-30.80] vs 0.96 [95% CI 0.32-3.05]; p = 0.038 for treatment-by-model interaction). The PSA50 rate in patients with a favourable outcome for [177Lu]Lu-PSMA-617 versus cabazitaxel was 62/88 (70%) versus 31/85 (36%). The decision curve analysis indicated that the use of the PSA50 model had a clinical net benefit when the probability of a PSA response was ≥30%. The predictive performance of the models for PSA PFS and OS was not established (treatment-by-model interaction: p = 0.36 and p = 0.41, respectively). CONCLUSIONS A previously developed outcome classification model for PSA50 was demonstrated to be both predictive and prognostic for the outcome after [177Lu]Lu-PSMA-617 versus cabazitaxel, while the PSA PFS and OS models had purely prognostic value. The models may aid clinicians in defining strategies for patients with metastatic castration-resistant prostate cancer who failed first-line chemotherapy and are eligible for [177Lu]Lu-PSMA-617 and cabazitaxel. PATIENT SUMMARY In this report, we validated previously developed statistical models that can predict a response to Lu-PSMA radioligand therapy in patients with advanced prostate cancer. We found that the statistical models can predict patient survival, and aid in determining whether Lu-PSMA therapy or cabazitaxel yields a higher probability to achieve a serum prostate-specific antigen response.
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Affiliation(s)
- Andrei Gafita
- Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Theranostics Center, Baltimore, MD, USA.
| | - Andrew J Martin
- NHMRC Clinical Trials Center, University of Sydney, Sydney, NSW, Australia; ANZUP Cancer Trials Group, Sydney, NSW, Australia
| | - Louise Emmett
- ANZUP Cancer Trials Group, Sydney, NSW, Australia; Department of Theranostics and Nuclear Medicine, St Vincent's Hospital, Sydney, NSW, Australia; Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Matthias Eiber
- Department of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar, Munich, Germany
| | - Amir Iravani
- ANZUP Cancer Trials Group, Sydney, NSW, Australia; Prostate Cancer Theranostics and Imaging Center of Excellence (ProsTIC), Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Center, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia; Department of Radiology, University of Washington, Seattle, WA, USA
| | - Wolfgang P Fendler
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - James Buteau
- ANZUP Cancer Trials Group, Sydney, NSW, Australia; Prostate Cancer Theranostics and Imaging Center of Excellence (ProsTIC), Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Center, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Shahneen Sandhu
- ANZUP Cancer Trials Group, Sydney, NSW, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia; Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, VIC, Australia
| | - Arun A Azad
- ANZUP Cancer Trials Group, Sydney, NSW, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia; Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, VIC, Australia
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Martin R Stockler
- NHMRC Clinical Trials Center, University of Sydney, Sydney, NSW, Australia; ANZUP Cancer Trials Group, Sydney, NSW, Australia
| | - Ian D Davis
- ANZUP Cancer Trials Group, Sydney, NSW, Australia; Monash University, Melbourne, VIC, Australia; Eastern Health, Melbourne, VIC, Australia
| | - Michael S Hofman
- ANZUP Cancer Trials Group, Sydney, NSW, Australia; Prostate Cancer Theranostics and Imaging Center of Excellence (ProsTIC), Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Center, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
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Awuah WA, Ahluwalia A, Tan JK, Sanker V, Roy S, Ben-Jaafar A, Shah DM, Tenkorang PO, Aderinto N, Abdul-Rahman T, Atallah O, Alexiou A. Theranostics Advances in the Treatment and Diagnosis of Neurological and Neurosurgical Diseases. Arch Med Res 2025; 56:103085. [PMID: 39369666 DOI: 10.1016/j.arcmed.2024.103085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 08/19/2024] [Accepted: 09/03/2024] [Indexed: 10/08/2024]
Abstract
Theranostics represents a significant advance in the fields of neurology and neurosurgery, offering innovative approaches that combine the diagnosis and treatment of various neurological disorders. This innovation serves as a cornerstone of personalized medicine, where therapeutic strategies are closely integrated with diagnostic tools to enable precise and targeted interventions. Primary research results emphasize the profound impact of theranostics in Neuro Oncol. In this context, it has provided valuable insights into the complexity of the tumor microenvironment and mechanisms of resistance. In addition, in the field of neurodegenerative diseases (NDs), theranostics has facilitated the identification of distinct disease subtypes and novel therapeutic targets. It has also unravelled the intricate pathophysiology underlying conditions such as cerebrovascular disease (CVD) and epilepsy, setting the stage for more refined treatment approaches. As theranostics continues to evolve through ongoing research and refinement, its goals include further advancing the field of precision medicine, developing practical biomarkers for clinical use, and opening doors to new therapeutic opportunities. Nevertheless, the integration of these approaches into clinical settings presents challenges, including ethical considerations, the need for advanced data interpretation, standardization of procedures, and ensuring cost-effectiveness. Despite these obstacles, the promise of theranostics to significantly improve patient outcomes in the fields of neurology and neurosurgery remains a source of optimism for the future of healthcare.
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Affiliation(s)
| | - Arjun Ahluwalia
- School of Medicine, Queen's University Belfast, Belfast, United Kingdom
| | | | - Vivek Sanker
- Department of Neurosurgery, Stanford University, CA, USA
| | - Sakshi Roy
- School of Medicine, Queen's University Belfast, Belfast, United Kingdom
| | - Adam Ben-Jaafar
- University College Dublin, School of Medicine, Belfield, Dublin 4, Ireland
| | - Devansh Mitesh Shah
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
| | | | - Nicholas Aderinto
- Internal Medicine Department, LAUTECH Teaching Hospital, Ogbomoso, Nigeria
| | | | - Oday Atallah
- Department of Neurosurgery, Hannover Medical School, Carl-Neuberg-Strasse 1, Hannover, Germany
| | - Athanasios Alexiou
- University Centre for Research and Development, Chandigarh University, Chandigarh-Ludhiana Highway, Mohali, Punjab, India; Department of Research and Development, Funogen, Athens, Greece; Department of Research and Development, AFNP Med, Wien, Austria; Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, Australia.
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Benitez CM, Sahlstedt H, Sonni I, Brynolfsson J, Berenji GR, Juarez JE, Kane N, Tsai S, Rettig M, Nickols NG, Duriseti S. Treatment Response Assessment According to Updated PROMISE Criteria in Patients with Metastatic Prostate Cancer Using an Automated Imaging Platform for Identification, Measurement, and Temporal Tracking of Disease. Eur Urol Oncol 2024:S2588-9311(24)00240-2. [PMID: 39521638 DOI: 10.1016/j.euo.2024.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 09/09/2024] [Accepted: 10/07/2024] [Indexed: 11/16/2024]
Abstract
BACKGROUND AND OBJECTIVE Prostate-specific membrane antigen (PSMA) molecular imaging is widely used for disease assessment in prostate cancer (PC). Artificial intelligence (AI) platforms such as automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) identify and quantify locoregional and distant disease, thereby expediting lesion identification and standardizing reporting. Our aim was to evaluate the ability of the updated aPROMISE platform to assess treatment responses based on integration of the RECIP (Response Evaluation Criteria in PSMA positron emission tomography-computed tomography [PET/CT]) 1.0 classification. METHODS The study included 33 patients with castration-sensitive PC (CSPC) and 34 with castration-resistant PC (CRPC) who underwent PSMA-targeted molecular imaging before and ≥2 mo after completion of treatment. Tracer-avid lesions were identified using aPROMISE for pretreatment and post-treatment PET/CT scans. Detected lesions were manually approved by an experienced nuclear medicine physician, and total tumor volume (TTV) was calculated. Response was assessed according to RECIP 1.0 as CR (complete response), PR (partial response), PD (progressive disease), or SD (stable disease). KEY FINDINGS AND LIMITATIONS: aPROMISE identified 1576 lesions on baseline scans and 1631 lesions on follow-up imaging, 618 (35%) of which were new. Of the 67 patients, aPROMISE classified four as CR, 16 as PR, 34 as SD, and 13 as PD; five cases were misclassified. The agreement between aPROMISE and clinician validation was 89.6% (κ = 0.79). CONCLUSIONS AND CLINICAL IMPLICATIONS aPROMISE may serve as a novel assessment tool for treatment response that integrates PSMA PET/CT results and RECIP imaging criteria. The precision and accuracy of this automated process should be validated in prospective clinical studies. PATIENT SUMMARY We used an artificial intelligence (AI) tool to analyze scans for prostate cancer before and after treatment to see if we could track how cancer spots respond to treatment. We found that the AI approach was successful in tracking individual tumor changes, showing which tumors disappeared, and identifying new tumors in response to prostate cancer treatment.
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Affiliation(s)
- Cecil M Benitez
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA
| | | | - Ida Sonni
- VA Greater Los Angeles Healthcare System, Department of Nuclear Medicine, Los Angeles, CA, USA; Department of Radiological Sciences, University of California-Los Angeles, Los Angeles, CA, USA
| | | | - Gholam Reza Berenji
- VA Greater Los Angeles Healthcare System, Department of Nuclear Medicine, Los Angeles, CA, USA
| | - Jesus Eduardo Juarez
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA
| | - Nathanael Kane
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Sonny Tsai
- Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Matthew Rettig
- Department of Hematology-Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Medicine, University of California-Los Angeles, Los Angeles, CA, USA; Department of Urology, University of California-Los Angeles, Los Angeles, CA, USA
| | - Nicholas George Nickols
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Urology, University of California-Los Angeles, Los Angeles, CA, USA
| | - Sai Duriseti
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, USA; Department of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
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Pang L, Zhang Z, Liu G, Hu P, Chen S, Gu Y, Huang Y, Zhang J, Shi Y, Cao T, Zhang Y, Shi H. Comparison of the Accuracy of a Deep Learning Method for Lesion Detection in PET/CT and PET/MRI Images. Mol Imaging Biol 2024; 26:802-811. [PMID: 39141195 DOI: 10.1007/s11307-024-01943-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 07/30/2024] [Accepted: 07/31/2024] [Indexed: 08/15/2024]
Abstract
PURPOSE Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate it, and explore factors affecting performance. PROCEDURES The 2022 AutoPet Challenge's 1014 PET/CT dataset was used to train the lesion detection model based on 2D and 3D fractional-residual (F-Res) models. To extend this to PET/MRI, a network for converting MR images to synthetic CT (sCT) was developed, using 41 sets of whole-body MR and corresponding CT data. 38 patients' PET/CT and PET/MRI data were used to verify the universal lesion recognition algorithm. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Total lesion glycolysis (TLG), metabolic tumor volume (MTV), and lesion count were calculated from the resultant lesion masks. Experienced physicians reviewed and corrected the model's outputs, establishing the ground truth. The performance of the lesion detection deep-learning model on different PET images was assessed by detection accuracy, precision, recall, and dice coefficients. Data with a detection accuracy score (DAS) less than 1 was used for analysis of outliers. RESULTS Compared to PET/CT, PET/MRI scans had a significantly longer delay time (135 ± 45 min vs 61 ± 12 min) and lower SNR (6.17 ± 1.11 vs 9.27 ± 2.77). However, CNR values were similar (7.37 ± 5.40 vs 5.86 ± 6.69). PET/MRI detected more lesions (with a mean difference of -3.184). TLG and MTV showed no significant differences between PET/CT and PET/MRI (TLG: 119.18 ± 203.15 vs 123.57 ± 151.58, p = 0.41; MTV: 36.58 ± 57.00 vs 39.16 ± 48.34, p = 0.33). A total of 12 PET/CT and 14 PET/MRI datasets were included in the analysis of outliers. Outlier analysis revealed PET/CT anomalies in intestines, ureters, and muscles, while PET/MRI anomalies were in intestines, testicles, and low tracer uptake regions, with false positives in ureters (PET/CT) and intestines/testicles (PET/MRI). CONCLUSION The deep learning lesion detection model performs well with both PET/CT and PET/MRI. SNR, CNR and reconstruction parameters minimally impact recognition accuracy, but delay time post-injection is significant.
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Affiliation(s)
- Lifang Pang
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Zheng Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China
| | - Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Pengcheng Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Shuguang Chen
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
| | - Yushen Gu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yukun Huang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China
| | - Jia Zhang
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China
| | - Yuhang Shi
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China
| | - Tuoyu Cao
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China
| | - Yiqiu Zhang
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Li Y, Imami MR, Zhao L, Amindarolzarbi A, Mena E, Leal J, Chen J, Gafita A, Voter AF, Li X, Du Y, Zhu C, Choyke PL, Zou B, Jiao Z, Rowe SP, Pomper MG, Bai HX. An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:2206-2215. [PMID: 38587770 PMCID: PMC11522269 DOI: 10.1007/s10278-024-01104-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 01/22/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024]
Abstract
Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy.
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Affiliation(s)
- Yang Li
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, China
| | - Maliha R Imami
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Linmei Zhao
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Alireza Amindarolzarbi
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Esther Mena
- National Institutes of Health, Bethesda, 20892, USA
| | - Jeffrey Leal
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Junyu Chen
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Andrei Gafita
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Andrew F Voter
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Xin Li
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Yong Du
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Chengzhang Zhu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | | | - Beiji Zou
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, China
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Zhicheng Jiao
- Warren Alpert Medical School of Brown University, Providence, 02903, USA
| | - Steven P Rowe
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Martin G Pomper
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Harrison X Bai
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA.
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11
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Fu Y, Zhao M, Chen J, Wen Q, Chen B. Enhancing prostate cancer diagnosis and reducing unnecessary biopsies with [ 18F]DCFPyL PET/CT imaging in PI-RADS 3/4 patients. Sci Rep 2024; 14:15525. [PMID: 38969741 PMCID: PMC11226634 DOI: 10.1038/s41598-024-65452-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 06/20/2024] [Indexed: 07/07/2024] Open
Abstract
For patients presenting with prostate imaging reporting and data system (PI-RADS) 3/4 findings on magnetic resonance imaging (MRI) examinations, the standard recommendation typically involves undergoing a biopsy for pathological assessment to ascertain the nature of the lesion. This course of action, though essential for accurate diagnosis, invariably amplifies the psychological distress experienced by patients and introduces a host of potential complications associated with the biopsy procedure. However, [18F]DCFPyL PET/CT imaging emerges as a promising alternative, demonstrating considerable diagnostic efficacy in discerning benign prostate lesions from malignant ones. This study aims to explore the diagnostic value of [18F]DCFPyL PET/CT imaging for prostate cancer in patients with PI-RADS 3/4 lesions, assisting in clinical decision-making to avoid unnecessary biopsies. 30 patients diagnosed with PI-RADS 3/4 lesions through mpMRI underwent [18F]DCFPyL PET/CT imaging, with final biopsy pathology results as the "reference standard". Diagnostic performance was assessed through receiver operating characteristic (ROC) analysis, evaluating the diagnostic efficacy of molecular imaging PSMA (miPSMA) visual analysis and semi-quantitative analysis in [18F]DCFPyL PET/CT imaging. Lesions were assigned miPSMA scores according to the prostate cancer molecular imaging standardized evaluation criteria. Among the 30 patients, 13 were pathologically confirmed to have prostate cancer. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of visual analysis in [18F]DCFPyL PET/CT imaging for diagnosing PI-RADS 3/4 lesions were 61.5%, 88.2%, 80.0%, 75.0%, and 76.5%, respectively. Using SUVmax 4.17 as the optimal threshold, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for diagnosis were 92.3%, 88.2%, 85.7%, 93.8%, and 90.0%, respectively. The area under the ROC curve (AUC) for semi-quantitative analysis was 0.94, significantly higher than visual analysis at 0.80. [18F]DCFPyL PET/CT imaging accurately diagnosed benign lesions in 15 (50%) of the PI-RADS 3/4 patients. For patients with PI-RADS 4 lesions, the positive predictive value of [18F]DCFPyL PET/CT imaging reached 100%. [18F]DCFPyL PET/CT imaging provides potential preoperative prediction of lesion nature in mpMRI PI-RADS 3/4 patients, which may aid in treatment decision-making and reducing unnecessary biopsies.
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Affiliation(s)
- Yang Fu
- Department of Nuclear Medicine, China-Japan Union Hospital of Jilin University, No. 126, Xiantai Street, Changchun, 130033, China
| | - Min Zhao
- Department of Nuclear Medicine, China-Japan Union Hospital of Jilin University, No. 126, Xiantai Street, Changchun, 130033, China
| | - Jie Chen
- Department of Nuclear Medicine, China-Japan Union Hospital of Jilin University, No. 126, Xiantai Street, Changchun, 130033, China
| | - Qiang Wen
- Department of Nuclear Medicine, China-Japan Union Hospital of Jilin University, No. 126, Xiantai Street, Changchun, 130033, China.
| | - Bin Chen
- Department of Nuclear Medicine, China-Japan Union Hospital of Jilin University, No. 126, Xiantai Street, Changchun, 130033, China.
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12
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Seifert R, Gafita A, Solnes LB, Iagaru A. Prostate-specific Membrane Antigen: Interpretation Criteria, Standardized Reporting, and the Use of Machine Learning. PET Clin 2024; 19:363-369. [PMID: 38705743 DOI: 10.1016/j.cpet.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Prostate-specific membrane antigen targeting positron emission tomography (PSMA-PET) is routinely used for the staging and restaging of patients with various stages of prostate cancer. For clear communication with referring physicians and to improve inter-reader agreement, the use of standardized reporting templates is mandatory. Increasingly, tumor volume is used by reporting and response assessment frameworks to prognosticate patient outcome or measure response to therapy. However, the quantification of tumor volume is often too time-consuming in routine clinical practice. Machine learning-based tools can facilitate the quantification of tumor volume for improved outcome prognostication.
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Affiliation(s)
- Robert Seifert
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland; Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
| | - Andrei Gafita
- Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lilja B Solnes
- Division of Nuclear Medicine and Molecular Imaging, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrei Iagaru
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Drive H2200, Stanford 94305, USA
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13
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Clore J, Scott PJH. [ 68Ga]PSMA-11 for positron emission tomography (PET) imaging of prostate-specific membrane antigen (PSMA)-positive lesions in men with prostate cancer. Expert Rev Mol Diagn 2024; 24:565-582. [PMID: 39054633 DOI: 10.1080/14737159.2024.2383439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024]
Abstract
INTRODUCTION Theranostics targeting prostate-specific membrane antigen (PSMA) represent a new targeted approach for prostate cancer care that combines diagnostic and therapeutic radiopharmaceuticals to diagnose and treat the disease. Positron emission tomography (PET) is the imaging method of choice and several diagnostic radiopharmaceuticals for quantifying PSMA have received FDA approval and are in clinical use. [68Ga]Ga-PSMA-11 is one such imaging agent and the focus of this article. One beta-emitting radioligand therapy ([177Lu]Lu-PSMA-617) has also received FDA approval for prostate cancer treatment, and several other alpha- and beta-emitting radioligand therapies are in clinical trials. AREAS COVERED Theranostics targeting PSMA in men with prostate cancer are discussed with a focus on use of [68Ga]Ga-PSMA-11 for imaging PSMA-positive lesions in men with prostate cancer. The review covers [68Ga]Ga-PSMA-11 manufacture, current regulatory status, comparison of [68Ga]Ga-PSMA-11 to other imaging techniques, clinical updates, and emerging applications of artificial intelligence for [68Ga]Ga-PSMA-11 PET. EXPERT OPINION [68Ga]Ga-PSMA-11 is used in conjunction with a PET/CT scan to image PSMA positive lesions in men with prostate cancer. It is manufactured by chelating precursor with68Ga, either from a generator or cyclotron, and has regulatory approval around the world. It is widely used clinically in conjunction with radioligand therapies like [177Lu]Lu-PSMA-617.
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Affiliation(s)
- Jessica Clore
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Peter J H Scott
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
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14
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García-Zoghby L, Amo-Salas M, Soriano Castrejón ÁM, García Vicente AM. Whole-body tumour burden on [18F]DCFPyL PET/CT in biochemical recurrence of prostate cancer: association with tumour biology and PSA kinetics. Eur J Nucl Med Mol Imaging 2024; 51:2467-2483. [PMID: 38520513 DOI: 10.1007/s00259-024-06685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/08/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE The objective was to assess the association between molecular imaging (mi) variables on [18F]DCFPyL-PET/CT with clinical and disease characteristics and prostate specific antigen (PSA) related variables in patients with biochemical recurrence of prostate cancer (BRPC). MATERIAL AND METHODS We analysed patients with BRPC after radical treatment. We obtained clinical and PSA variables: International Society of Urology Pathology (ISUP) grade group, European Association of Urology (EAU) risk classification, PSA (PSA≤1ng/ml, 1 2), PSA doubling time (PSAdt) and PSA velocity (PSAvel). All PET/CT scans were reviewed with the assistance of automated Prostate Molecular Imaging Standardized Evaluation (aPROMISE) software and lesions' segmentation in positive scans was performed using this platform. Standardized uptake value (SUV) derived variables; tumour burden variables [whole-body tumour volume (wbTV), whole-body tumour lesion activity (wbTLA) and whole-body mi PSMA (wbPSMA)] and miTNM staging were obtained. Cut-off of PSA and kinetics able to predict PET/CT results were obtained. Associations between disease and mi variables were analysed using ANOVA, Kruskal-Wallis and Spearman's correlation tests. Multivariate analysis was also performed. RESULTS Two hundred and seventy-five patients were studied. [18F]DCFPyL-PET/CT were positive in 165/275 patients. In multivariate analysis, moment of biochemical recurrence, ISUP group, PSA level and PSAvel showed significant association with the detection rate. miTNM showed significant association with PSA level (p<0.001) and kinetics (p<0.001), being higher in patients with metastatic disease. Both PSA and PSAvel showed moderate correlation with wbTV, wbTLA and wbPSMA (p<0.001). A weak correlation with SUVs was found. Mean wbTV, wbTLA and wbPSMA values were significantly higher in PSA > 2ng/ml, PSAdt ≤ 6 months and PSAvel ≥ 0.2ng/ml/month groups. Also, wbTV (p=0.039) and wbPSMA (p=0.020) were significantly higher in patients with ISUP grade group 5. PSA and PSAvel cut-offs (1.15 ng/ml and 0.065 ng/ml/month) were significantly associated with a positive PET/CT. CONCLUSION Higher PSA values, unfavourable PSA kinetics and ISUP grade group 5 were robust predictive variables of larger tumour burden variables on [18F]DCFPyL PET/CT assessed by aPROMISE platform.
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Affiliation(s)
- Laura García-Zoghby
- Nuclear Medicine Department, University Hospital of Toledo, Av. del Río Guadiana, s/n, 45007, Toledo, Spain.
| | - Mariano Amo-Salas
- Department of Mathematics, Castilla-La Mancha University, Cam. Moledores, s/n, 13071, Ciudad Real, Spain
| | | | - Ana María García Vicente
- Nuclear Medicine Department, University Hospital of Toledo, Av. del Río Guadiana, s/n, 45007, Toledo, Spain
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15
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Oldan JD, Almaguel F, Voter AF, Duran A, Gafita A, Pomper MG, Hope TA, Rowe SP. PSMA-Targeted Radiopharmaceuticals for Prostate Cancer Diagnosis and Therapy. Cancer J 2024; 30:176-184. [PMID: 38753752 DOI: 10.1097/ppo.0000000000000718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
ABSTRACT Prostate cancer (PCa) is the most common noncutaneous malignancy in men. Until recent years, accurate imaging of men with newly diagnosed PCa, or recurrent or low-volume metastatic disease, was limited. Further, therapeutic options for men with advanced, metastatic, castration-resistant disease were increasingly limited as a result of increasing numbers of systemic therapies being combined in the upfront metastatic setting. The advent of urea-based, small-molecule inhibitors of prostate-specific membrane antigen (PSMA) has partially addressed those shortcomings in diagnosis and therapy of PCa. On the diagnostic side, there are multiple pivotal phase III trials with several different agents having demonstrated utility in the initial staging setting, with generally modest sensitivity but very high specificity for determining otherwise-occult pelvic nodal involvement. That latter statistic drives the utility of the scan by allowing imaging interpreters to read with very high sensitivity while maintaining a robust specificity. Other pivotal phase III trials have demonstrated high detection efficiency in patients with biochemical failure, with high positive predictive value at the lesion level, opening up possible new avenues of therapy such as metastasis-directed therapy. Beyond the diagnostic aspects of PSMA-targeted radiotracers, the same urea-based chemical scaffolds can be altered to deliver therapeutic isotopes to PCa cells that express PSMA. To date, one such agent, when combined with best standard-of-care therapy, has demonstrated an ability to improve overall survival, progression-free survival, and freedom from skeletal events relative to best standard-of-care therapy alone in men with metastatic, castration-resistant PCa who are post chemotherapy. Within the current milieu, there are a number of important future directions including the use of artificial intelligence to better leverage diagnostic findings, further medicinal chemistry refinements to the urea-based structure that may allow improved tumor targeting and decreased toxicities, and the incorporation of new radionuclides that may better balance efficacy with toxicities than those nuclides that are available.
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Affiliation(s)
- Jorge D Oldan
- From the Department of Radiology, University of North Carolina, Chapel Hill, NC
| | - Frankis Almaguel
- Department of Radiology, Loma Linda University School of Medicine, Loma Linda, CA
| | - Andrew F Voter
- The Russell H. Morgan Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alfonso Duran
- Department of Radiology, Loma Linda University School of Medicine, Loma Linda, CA
| | - Andrei Gafita
- The Russell H. Morgan Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Martin G Pomper
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | - Steven P Rowe
- From the Department of Radiology, University of North Carolina, Chapel Hill, NC
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Yazdani E, Karamzadeh-Ziarati N, Cheshmi SS, Sadeghi M, Geramifar P, Vosoughi H, Jahromi MK, Kheradpisheh SR. Automated segmentation of lesions and organs at risk on [ 68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR. Cancer Imaging 2024; 24:30. [PMID: 38424612 PMCID: PMC10903052 DOI: 10.1186/s40644-024-00675-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Prostate-specific membrane antigen (PSMA) PET/CT imaging is widely used for quantitative image analysis, especially in radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC). Unknown features influencing PSMA biodistribution can be explored by analyzing segmented organs at risk (OAR) and lesions. Manual segmentation is time-consuming and labor-intensive, so automated segmentation methods are desirable. Training deep-learning segmentation models is challenging due to the scarcity of high-quality annotated images. Addressing this, we developed shifted windows UNEt TRansformers (Swin UNETR) for fully automated segmentation. Within a self-supervised framework, the model's encoder was pre-trained on unlabeled data. The entire model was fine-tuned, including its decoder, using labeled data. METHODS In this work, 752 whole-body [68Ga]Ga-PSMA-11 PET/CT images were collected from two centers. For self-supervised model pre-training, 652 unlabeled images were employed. The remaining 100 images were manually labeled for supervised training. In the supervised training phase, 5-fold cross-validation was used with 64 images for model training and 16 for validation, from one center. For testing, 20 hold-out images, evenly distributed between two centers, were used. Image segmentation and quantification metrics were evaluated on the test set compared to the ground-truth segmentation conducted by a nuclear medicine physician. RESULTS The model generates high-quality OARs and lesion segmentation in lesion-positive cases, including mCRPC. The results show that self-supervised pre-training significantly improved the average dice similarity coefficient (DSC) for all classes by about 3%. Compared to nnU-Net, a well-established model in medical image segmentation, our approach outperformed with a 5% higher DSC. This improvement was attributed to our model's combined use of self-supervised pre-training and supervised fine-tuning, specifically when applied to PET/CT input. Our best model had the lowest DSC for lesions at 0.68 and the highest for liver at 0.95. CONCLUSIONS We developed a state-of-the-art neural network using self-supervised pre-training on whole-body [68Ga]Ga-PSMA-11 PET/CT images, followed by fine-tuning on a limited set of annotated images. The model generates high-quality OARs and lesion segmentation for PSMA image analysis. The generalizable model holds potential for various clinical applications, including enhanced RLT and patient-specific internal dosimetry.
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Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, 14155-6183, Iran
- Fintech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran
| | | | - Seyyed Saeid Cheshmi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, 14155-6183, Iran.
- Fintech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran.
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Habibeh Vosoughi
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Nuclear Medicine and Molecular Imaging Department, Imam Reza International University, Razavi Hospital, Mashhad, Iran
| | - Mahmood Kazemi Jahromi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran, 14155-6183, Iran
- Fintech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Saeed Reza Kheradpisheh
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
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Yang X, Silosky M, Wehrend J, Litwiller DV, Nachiappan M, Metzler SD, Ghosh D, Xing F, Chin BB. Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance. Bioengineering (Basel) 2024; 11:226. [PMID: 38534501 DOI: 10.3390/bioengineering11030226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 02/18/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n = 83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n = 123 subjects). Set1, the data set from Scanner1, was reconstructed with standard clinical parameters (5 min; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-min). Set2, data from Scanner2 representing out-of-domain clinical scans, used standard iterative reconstruction (5 min; OSEM). A deep neural network was trained with each dataset: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50% and 75%) of Set1 for training. Scanner1, list-mode 2-min reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1 = 0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1 = 0.755; p-value = 0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1 = 0.478) to 100% training data (F1 = 0.713; p < 0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.
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Affiliation(s)
- Xinyi Yang
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael Silosky
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jonathan Wehrend
- Department of Radiology, Santa Clara Valley Medical Center, San Jose, CA 95128, USA
| | | | - Muthiah Nachiappan
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Scott D Metzler
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Fuyong Xing
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- The Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Bennett B Chin
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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18
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García Vicente AM, Lucas Lucas C, Pérez-Beteta J, Borrelli P, García Zoghby L, Amo-Salas M, Soriano Castrejón ÁM. Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study. Sci Rep 2024; 14:3001. [PMID: 38321201 PMCID: PMC10847509 DOI: 10.1038/s41598-024-53683-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 02/03/2024] [Indexed: 02/08/2024] Open
Abstract
To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumor. Patients with a recent diagnosis of intermediate/high-risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose. In positive-18F-DCFPyL-PET/CT scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with two softwares. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers. In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained. We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen's kappa coefficient (k) for the concordance analysis. Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, tumor volume (TV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p < 0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k = 0.733; p < 0.001 and k = 0.812; p < 0.001, respectively) although the concordances with histopathology were moderate (p < 0001). The analytical validation of aPROMISE showed a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.
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Affiliation(s)
- Ana María García Vicente
- Nuclear Medicine Department, Complejo Hospitalario Universitario de Toledo, Avda. Rio Guadiana s/n, 45007, Toledo, Spain.
| | | | - Julián Pérez-Beteta
- Mathematical Oncology Laboratory (MOLab), Castilla-La Mancha University, Ciudad Real, Spain
- Department of Mathematics, Castilla-La Mancha University, Ciudad Real, Spain
| | - Pablo Borrelli
- Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Laura García Zoghby
- Nuclear Medicine Department, Complejo Hospitalario Universitario de Toledo, Avda. Rio Guadiana s/n, 45007, Toledo, Spain
| | - Mariano Amo-Salas
- Department of Mathematics, Castilla-La Mancha University, Ciudad Real, Spain
| | - Ángel María Soriano Castrejón
- Nuclear Medicine Department, Complejo Hospitalario Universitario de Toledo, Avda. Rio Guadiana s/n, 45007, Toledo, Spain
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19
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Liu J, Cundy TP, Woon DTS, Lawrentschuk N. A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans. Cancers (Basel) 2024; 16:486. [PMID: 38339239 PMCID: PMC10854940 DOI: 10.3390/cancers16030486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.
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Affiliation(s)
- Jianliang Liu
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Thomas P. Cundy
- Discipline of Surgery, University of Adelaide, Adelaide, SA 5005, Australia
| | - Dixon T. S. Woon
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Nathan Lawrentschuk
- E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia; (J.L.)
- Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia
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20
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Lindgren Belal S, Frantz S, Minarik D, Enqvist O, Wikström E, Edenbrandt L, Trägårdh E. Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging. Semin Nucl Med 2024; 54:141-149. [PMID: 37357026 DOI: 10.1053/j.semnuclmed.2023.06.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/27/2023]
Abstract
Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.
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Affiliation(s)
- Sarah Lindgren Belal
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Surgery, Skåne University Hospital, Malmö, Sweden
| | - Sophia Frantz
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Health Technology Assessment South, Skåne University Hospital, Lund, Sweden
| | - David Minarik
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Radiation Physics, Skåne University Hospital, Malmö, Sweden
| | - Olof Enqvist
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden; Department of Clinical Physiology and Nuclear Medicine, Malmö Sweden
| | - Erik Wikström
- Department of Health Technology Assessment South, Skåne University Hospital, Lund, Sweden
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Elin Trägårdh
- Department of Translational Medicine and Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden; Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Malmö, Sweden.
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21
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Duan H, Davidzon GA, Moradi F, Liang T, Song H, Iagaru A. Modified PROMISE criteria for standardized interpretation of gastrin-releasing peptide receptor (GRPR)-targeted PET. Eur J Nucl Med Mol Imaging 2023; 50:4087-4095. [PMID: 37555901 DOI: 10.1007/s00259-023-06385-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 08/03/2023] [Indexed: 08/10/2023]
Abstract
PURPOSE There are image interpretation criteria to standardize reporting prostate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET). As up to 10% of prostate cancer (PC) do not express PSMA, other targets such as gastrin-releasing peptide receptor (GRPR) are evaluated. Research on GRPR-targeted imaging has been slowly increasing in usage at staging and biochemical recurrence (BCR) of PC. We therefore propose a modification of the Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE) criteria (mPROMISE) for GRPR-targeted PET. METHODS [68 Ga]Ga-RM2 PET data from initially prospective studies performed at our institution were retrospectively reviewed: 44 patients were imaged for staging and 100 patients for BCR PC. Two nuclear medicine physicians independently evaluated PET according to the mPROMISE criteria. A third expert reader served as standard reference. Interreader reliability was computed for GRPR expression, prostate bed (T), lymph node (N), skeleton (Mb), organ (Mc) metastases, and final judgment of the scan. RESULTS The interrater reliability for GRPR PET at staging was moderate for GRPR expression (0.59; 95% confidence interval [CI] 0.40, 0.78), substantial for T-stage (0.78; 95% CI 0.63, 0.94), and almost perfect for N-stage (0.97; 95% CI 0.92, 1.00) and final judgment (0.92; 95% CI 0.82, 1.00). The interreader agreement at BCR showed substantial agreement for GRPR expression (0.70; 95% CI 0.59, 0.81) and final judgment (0.65; 95% CI 0.53, 0.78), while almost perfect agreement was seen across the major categories (T, N, Mb, Mc). Acceptable performance of the mPROMISE criteria was found for all subsets when compared to the standard reference. CONCLUSION Interpreting GRPR-targeted PET using the mPROMISE criteria showed its reliability with substantial or almost perfect interrater agreement across all major categories. The proposed modification of the PROMISE criteria will aid clinicians in decreasing the level of uncertainty, and clinical trials to achieve uniform evaluation, reporting, and comparability of GRPR-targeted PET. TRIAL REGISTRATION Clinicaltrials.gov Identifier: NCT03113617 and NCT02624518.
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Affiliation(s)
- Heying Duan
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA, 94305, USA
| | - Guido A Davidzon
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA, 94305, USA
| | - Farshad Moradi
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA, 94305, USA
| | - Tie Liang
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA, 94305, USA
| | - Hong Song
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA, 94305, USA
| | - Andrei Iagaru
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, 300 Pasteur Drive, H2200, Stanford, CA, 94305, USA.
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22
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Gafita A, Djaileb L, Rauscher I, Fendler WP, Hadaschik B, Rowe SP, Herrmann K, Calais J, Rettig M, Eiber M, Weber M, Benz MR, Farolfi A. Response Evaluation Criteria in PSMA PET/CT (RECIP 1.0) in Metastatic Castration-resistant Prostate Cancer. Radiology 2023; 308:e222148. [PMID: 37432081 PMCID: PMC10374938 DOI: 10.1148/radiol.222148] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 04/28/2023] [Accepted: 05/26/2023] [Indexed: 07/12/2023]
Abstract
Background Response Evaluation Criteria in Prostate-specific Membrane Antigen (PSMA) PET/CT (RECIP 1.0) initially integrated software-based quantitative assessment of PSMA-positive total tumor volume (TTV). Clinical implementation of such software is not expected soon, limiting the use of RECIP in practice. Purpose To assess the agreement of RECIP determined using tumor segmentation software (quantitative RECIP) with RECIP determined by qualitative reads by nuclear medicine physicians (visual RECIP) for response evaluation in metastatic castration-resistant prostate cancer. Materials and Methods This multicenter retrospective study at three academic centers included men who received lutetium 177 (177Lu) PSMA treatment between December 2014 and July 2019. PSMA PET/CT images at baseline and 12 weeks were assessed qualitatively by five readers for changes in TTV and for new lesions. Quantitative changes in TTV were also measured using tumor segmentation software. The status of new lesions was combined with qualitative changes in TTV to determine visual RECIP and with quantitative changes in TTV to determine quantitative RECIP. The primary outcomes were the agreement between visual and quantitative RECIP and the interreader reliability of visual RECIP according to the Fleiss κ. The secondary outcome was the association of visual RECIP with overall survival according to Cox regression. Results A total of 124 men (median age, 73 years [IQR, 67-76 years]) were included. Forty (32%) and 84 (68%) men had quantitative RECIP progressive disease (PD) and non-PD, respectively. Agreement between visual versus quantitative RECIP was excellent (κ = 0.89; 118 of 124 men [95%]). Agreement among readers in classifying visual RECIP PD versus non-PD was excellent (κ = 0.81; 103 of 124 men [83%]). RECIP PD was associated with significantly shorter overall survival compared with non-PD (hazard ratio, 2.6 [95% CI: 1.7, 3.8]; P < .001). Conclusion Qualitatively assessed RECIP demonstrated excellent agreement with quantitative RECIP and excellent interreader reliability and can be readily implemented in clinical practice for response evaluation in men with metastatic castration-resistant prostate cancer undergoing 177Lu-PSMA therapy. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
| | | | - Isabel Rauscher
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Wolfgang P. Fendler
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Boris Hadaschik
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Steven P. Rowe
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Ken Herrmann
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Jeremie Calais
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Matthew Rettig
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Matthias Eiber
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Manuel Weber
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Matthias R. Benz
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
| | - Andrea Farolfi
- From the Ahmanson Translational Theranostics Division, Department of
Molecular and Medical Pharmacology (A.G., L.D., J.C., M.R.B., A.F.), Department
of Medicine and Urology, David Geffen School of Medicine (M.R.), and Department
of Radiological Sciences (M.R.B.), University of California–Los Angeles,
Los Angeles, Calif; Division of Nuclear Medicine and Molecular Imaging, The
Russell H. Morgan Department of Radiology and Radiological Science, Johns
Hopkins University School of Medicine, 601 N Caroline St, JHOC 3225A, Baltimore,
MD 21287 (A.G., S.P.R.); Department of Nuclear Medicine, Université
Grenoble Alpes, INSERM, CHU Grenoble Alpes, Grenoble, France (L.D.); Department
of Nuclear Medicine, Technical University Munich, Klinikum rechts der Isar,
Munich, Germany (I.R., M.E.); Departments of Nuclear Medicine (W.P.F., K.H.,
M.R.B.) and Urology (B.H.), University of Duisburg-Essen and German Cancer
Consortium (DKTK), University Hospital Essen, Essen, Germany; Department of
Medicine, VA Greater Los Angeles, Los Angeles, Calif (M.R.); and Nuclear
Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
(A.F.)
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23
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Hotta M, Gafita A, Murthy V, Benz MR, Sonni I, Burger IA, Eiber M, Emmett L, Farolfi A, Fendler WP, Weber MM, Hofman MS, Hope TA, Kratochwil C, Czernin J, Calais J. PSMA PET Tumor-to-Salivary Gland Ratio to Predict Response to [ 177Lu]PSMA Radioligand Therapy: An International Multicenter Retrospective Study. J Nucl Med 2023; 64:1024-1029. [PMID: 36997329 PMCID: PMC11937727 DOI: 10.2967/jnumed.122.265242] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/10/2023] [Accepted: 02/10/2023] [Indexed: 04/01/2023] Open
Abstract
Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy can improve the outcome of patients with advanced metastatic castration-resistant prostate cancer, but patients do not respond uniformly. We hypothesized that using the salivary glands as a reference organ can enable selective patient stratification. We aimed to establish a PSMA PET tumor-to-salivary gland ratio (PSG score) to predict outcomes after [177Lu]PSMA. Methods: In total, 237 men with metastatic castration-resistant prostate cancer treated with [177Lu]PSMA were included. A quantitative PSG (qPSG) score (SUVmean ratio of whole-body tumor to parotid glands) was semiautomatically calculated on baseline [68Ga]PSMA-11 PET images. Patients were divided into 3 groups: high (qPSG > 1.5), intermediate (qPSG = 0.5-1.5), and low (qPSG < 0.5) scores. Ten readers interpreted the 3-dimensional maximum-intensity-projection baseline [68Ga]PSMA-11 PET images and classified patients into 3 groups based on visual PSG (vPSG) score: high (most of the lesions showed higher uptake than the parotid glands) intermediate (neither low nor high), and low (most of the lesions showed lower uptake than the parotid glands). Outcome data included a more than 50% prostate-specific antigen decline, prostate-specific antigen (PSA) progression-free survival, and overall survival (OS). Results: Of the 237 patients, the numbers in the high, intermediate, and low groups were 56 (23.6%), 163 (68.8%), and 18 (7.6%), respectively, for qPSG score and 106 (44.7%), 96 (40.5%), and 35 (14.8%), respectively, for vPSG score. The interreader reproducibility of the vPSG score was substantial (Fleiss weighted κ, 0.68). The more than 50% prostate-specific antigen decline was better in patients with a higher PSG score (high vs. intermediate vs. low, 69.6% vs. 38.7% vs. 16.7%, respectively, for qPSG [P < 0.001] and 63.2% vs 33.3% vs 16.1%, respectively, for vPSG [P < 0.001]). The median PSA progression-free survival of the high, intermediate, and low groups by qPSG score was 7.2, 4.0, and 1.9 mo (P < 0.001), respectively, by qPSG score and 6.7, 3.8, and 1.9 mo (P < 0.001), respectively, by vPSG score. The median OS of the high, intermediate, and low groups was 15.0, 11.2, and 13.9 mo (P = 0.017), respectively, by qPSG score and 14.3, 9.6, and 12.9 mo (P = 0.018), respectively, by vPSG score. Conclusion: The PSG score was prognostic for PSA response and OS after [177Lu]PSMA. The visual PSG score assessed on 3-dimensional maximum-intensity-projection PET images yielded substantial reproducibility and comparable prognostic value to the quantitative score.
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Affiliation(s)
- Masatoshi Hotta
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California;
| | - Andrei Gafita
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California
| | - Vishnu Murthy
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California
| | - Matthias R Benz
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California
| | - Ida Sonni
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California
| | - Irene A Burger
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Eiber
- Department of Nuclear Medicine, Technical University Munich, Munich, Germany
| | - Louise Emmett
- Department of Theranostics and Nuclear Medicine, St. Vincent's Hospital, Sydney, New South Wales, Australia
| | - Andrea Farolfi
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Wolfgang P Fendler
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium-University Hospital Essen, Essen, Germany
| | - Manuel M Weber
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium-University Hospital Essen, Essen, Germany
| | - Michael S Hofman
- Prostate Cancer Theranostics and Imaging Centre of Excellence, Molecular Imaging Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Centre, and Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California; and
| | - Clemens Kratochwil
- Department of Nuclear Medicine, University Hospital Heidelberg, Heidelberg, Germany
| | - Johannes Czernin
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California
| | - Jeremie Calais
- Ahmanson Translational Theranostics Division, UCLA, Los Angeles, California
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24
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Chervenkov L, Sirakov N, Kostov G, Velikova T, Hadjidekov G. Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging. World J Radiol 2023; 15:136-145. [PMID: 37275303 PMCID: PMC10236970 DOI: 10.4329/wjr.v15.i5.136] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/21/2023] [Accepted: 04/10/2023] [Indexed: 05/23/2023] Open
Abstract
Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca.
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Affiliation(s)
- Lyubomir Chervenkov
- Department of Diagnostic Imaging, Medical University Plovdiv, Plovdiv 4000, Bulgaria
- Research Complex for Translational Neuroscience, Medical University of Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4002, Bulgaria
| | - Nikolay Sirakov
- Research Complex for Translational Neuroscience, Medical University of Plovdiv, Bul. Vasil Aprilov 15A, Plovdiv 4002, Bulgaria
- Department of Diagnostic Imaging, Dental Allergology and Physiotherapy, Faculty of Dental Medicine, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Gancho Kostov
- Department of Special Surgery, Medical University Plovdiv, Plovdiv 4000, Bulgaria
| | - Tsvetelina Velikova
- Department of Clinical Immunology, University Hospital Lozenetz, Sofia 1407, Bulgaria
- Department of Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
| | - George Hadjidekov
- Department of Radiology, University Hospital Lozenetz, Sofia 1407, Bulgaria
- Department of Physics, Biophysics and Radiology, Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria
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25
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Seifert R, Emmett L, Rowe SP, Herrmann K, Hadaschik B, Calais J, Giesel FL, Reiter R, Maurer T, Heck M, Gafita A, Morris MJ, Fanti S, Weber WA, Hope TA, Hofman MS, Fendler WP, Eiber M. Second Version of the Prostate Cancer Molecular Imaging Standardized Evaluation Framework Including Response Evaluation for Clinical Trials (PROMISE V2). Eur Urol 2023; 83:405-412. [PMID: 36935345 DOI: 10.1016/j.eururo.2023.02.002] [Citation(s) in RCA: 127] [Impact Index Per Article: 63.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/18/2022] [Accepted: 02/01/2023] [Indexed: 03/19/2023]
Abstract
CONTEXT Prostate-specific membrane antigen (PSMA) targeting positron emission tomography (PET) is emerging to become a reference imaging tool for the staging and restaging of patients with prostate cancer for both clinical routine and trials. The prostate cancer molecular imaging standardized evaluation (PROMISE) criteria have been proposed as a framework for whole-body staging (molecular imaging TNM staging, denoted miTNM staging) to describe the prostate cancer disease extent on PSMA-PET. OBJECTIVE To create a comprehensive and integrated framework for PSMA-PET image interpretation and reporting. EVIDENCE ACQUISITION We propose the PROMISE V2 framework, which integrates an updated miTNM system, improved assessment of local disease, and a slightly modified PSMA-expression score for clinical routine. We have added a response monitoring framework defining qualitative and quantitative parameters to be recorded for a longitudinal assessment in clinical trials. EVIDENCE SYNTHESIS We provide a comprehensive literature review on the current use of the PROMISE framework in clinical research and prospective trials. PROMISE variables demonstrate a clear association with survival. PSMA expression assessed by the PSMA-expression score was used in several trials, and a low PSMA-expression score is a negative prognosticator of overall survival after 177Lu-PSMA radioligand therapy. The proposed imaging parameters recorded for response assessment in clinical trials can be utilized to determine response according to PSMA-PET progression (PPP) or Response Evaluation Criteria in PSMA-PET/Computed Tomography (RECIP) frameworks, but also future response criteria. CONCLUSIONS PROMISE V2 offers standardized reporting of disease extent for clinical routine and research. Parameters recorded within clinical trials facilitate objective response assessment. PATIENT SUMMARY Prostate-specific membrane antigen (PSMA) targeting positron emission tomography (PET) has become a standard imaging examination for prostate cancer. We propose a comprehensive framework for the analysis and reporting of PSMA-PET findings that will improve the communication between imaging experts and uro-oncologists.
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Affiliation(s)
- Robert Seifert
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany.
| | - Louise Emmett
- Department of Theranostics and Nuclear Medicine, St Vincent's Hospital, Sydney, NSW, Australia
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins, University School of Medicine, Baltimore, MD, USA; The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; Ahmanson Translational Theranostics, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, CA, USA
| | - Boris Hadaschik
- Department of Urology, University Hospital Essen, Essen, Germany
| | - Jeremie Calais
- Ahmanson Translational Theranostics, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, CA, USA
| | - Frederik L Giesel
- Department of Nuclear Medicine, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Robert Reiter
- Department of Urology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Tobias Maurer
- Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Heck
- Department of Urology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany; Bavarian Cancer Research Center (BZKF), Erlangen, Germany
| | - Andrei Gafita
- Ahmanson Translational Theranostics, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, CA, USA
| | - Michael J Morris
- Genitourinary Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Wolfgang A Weber
- Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Michael S Hofman
- Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC), Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Wolfgang Peter Fendler
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; PET Committee of the German Society of Nuclear Medicine, Göttingen, Germany
| | - Matthias Eiber
- Bavarian Cancer Research Center (BZKF), Erlangen, Germany; Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
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26
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Automation: A revolutionary vision of artificial intelligence in theranostics. Bull Cancer 2023; 110:233-241. [PMID: 36509576 DOI: 10.1016/j.bulcan.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/12/2022] [Accepted: 10/26/2022] [Indexed: 12/13/2022]
Abstract
The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.
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27
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Levi J, Song H. The other immuno-PET: Metabolic tracers in evaluation of immune responses to immune checkpoint inhibitor therapy for solid tumors. Front Immunol 2023; 13:1113924. [PMID: 36700226 PMCID: PMC9868703 DOI: 10.3389/fimmu.2022.1113924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Unique patterns of response to immune checkpoint inhibitor therapy, discernable in the earliest clinical trials, demanded a reconsideration of the standard methods of radiological treatment assessment. Immunomonitoring, that characterizes immune responses, offers several significant advantages over the tumor-centric approach currently used in the clinical practice: 1) better understanding of the drugs' mechanism of action and treatment resistance, 2) earlier assessment of response to therapy, 3) patient/therapy selection, 4) evaluation of toxicity and 5) more accurate end-point in clinical trials. PET imaging in combination with the right agent offers non-invasive tracking of immune processes on a whole-body level and thus represents a method uniquely well-suited for immunomonitoring. Small molecule metabolic tracers, largely neglected in the immuno-PET discourse, offer a way to monitor immune responses by assessing cellular metabolism known to be intricately linked with immune cell function. In this review, we highlight the use of small molecule metabolic tracers in imaging immune responses, provide a view of their value in the clinic and discuss the importance of image analysis in the context of tracking a moving target.
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Affiliation(s)
- Jelena Levi
- CellSight Technologies Incorporated, San Francisco, CA, United States,*Correspondence: Jelena Levi,
| | - Hong Song
- Department of Radiology, Stanford University, Palo Alto, CA, United States
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28
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Moradi F, Duan H, Song H, Davidzon GA, Chung BI, Thong AEC, Loening AM, Ghanouni P, Sonn G, Iagaru A. 68Ga-PSMA-11 PET/MRI in Patients with Newly Diagnosed Intermediate- or High-Risk Prostate Adenocarcinoma: PET Findings Correlate with Outcomes After Definitive Treatment. J Nucl Med 2022; 63:1822-1828. [PMID: 35512996 DOI: 10.2967/jnumed.122.263897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/22/2022] [Indexed: 01/11/2023] Open
Abstract
Prostate-specific membrane antigen (PSMA) PET offers an accuracy superior to other imaging modalities in initial staging of prostate cancer and is more likely to affect management. We examined the prognostic value of 68Ga-PSMA-11 uptake in the primary lesion and presence of metastatic disease on PET in newly diagnosed prostate cancer patients before initial therapy. Methods: In a prospective study from April 2016 to December 2020, 68Ga-PSMA-11 PET/MRI was performed in men with a new diagnosis of intermediate- or high-grade prostate cancer who were candidates for prostatectomy. Patients were followed up after initial therapy for up to 5 y. We examined the Kendall correlation between PET (intense uptake in the primary lesion and presence of metastatic disease) and clinical and pathologic findings (grade group, extraprostatic extension, nodal involvement) relevant for risk stratification, and examined the relationship between PET findings and outcome using Kaplan-Meier analysis. Results: Seventy-three men (age, 64.0 ± 6.3 y) were imaged. Seventy-two had focal uptake in the prostate, and in 20 (27%) PSMA-avid metastatic disease was identified. Uptake correlated with grade group and prostate-specific antigen (PSA). Presence of PSMA metastasis correlated with grade group and pathologic nodal stage. PSMA PET had higher per-patient positivity than nodal dissection in patients with only 5-15 nodes removed (8/41 vs. 3/41) but lower positivity if more than 15 nodes were removed (13/21 vs. 10/21). High uptake in the primary lesion (SUVmax > 12.5, P = 0.008) and presence of PSMA metastasis (P = 0.013) were associated with biochemical failure, and corresponding hazard ratios for recurrence within 2 y (4.93 and 3.95, respectively) were similar to or higher than other clinicopathologic prognostic factors. Conclusion: 68Ga-PSMA-11 PET can risk-stratify patients with intermediate- or high-grade prostate cancer before prostatectomy based on degree of uptake in the prostate and presence of metastatic disease.
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Affiliation(s)
- Farshad Moradi
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, Stanford, California;
| | - Heying Duan
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, Stanford, California
| | - Hong Song
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, Stanford, California
| | - Guido A Davidzon
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, Stanford, California
| | - Benjamin I Chung
- Department of Urology, Stanford University, Stanford, California; and
| | - Alan E C Thong
- Department of Urology, Stanford University, Stanford, California; and
| | - Andreas M Loening
- Division of Body MRI, Department of Radiology, Stanford University, Stanford, California
| | - Pejman Ghanouni
- Division of Body MRI, Department of Radiology, Stanford University, Stanford, California
| | - Geoffrey Sonn
- Department of Urology, Stanford University, Stanford, California; and
| | - Andrei Iagaru
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, Stanford, California
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29
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Miyahira AK, Soule HR. The 28th Annual Prostate Cancer Foundation Scientific Retreat report. Prostate 2022; 82:1346-1377. [PMID: 35852016 DOI: 10.1002/pros.24409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND The 28th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held virtually over 4 days, on October 28-29 and November 4-5, 2021. METHODS The Annual PCF Scientific Retreat is a leading global scientific conference that focuses on first-in-field, unpublished, and high-impact basic, translational, and clinical prostate cancer research, as well as research from other fields with high probability for impacting prostate cancer research and patient care. RESULTS Primary areas of research discussed at the 2021 PCF Retreat included: (i) prostate cancer disparities; (ii) prostate cancer survivorship; (iii) next-generation precision medicine; (iv) PSMA theranostics; (v) prostate cancer lineage plasticity; (vi) tumor metabolism as a cancer driver and treatment target; (vii) prostate cancer genetics and polygenic risk scores; (viii) glucocorticoid receptor biology in castration-resistant prostate cancer (CRPC); (ix) therapeutic degraders; (x) new approaches for immunotherapy in prostate cancer; (xi) novel technologies to overcome the suppressive tumor microenvironment; and (xii) real-world evidence and synthetic/virtual control arms. CONCLUSIONS This article provides a summary of the presentations from the 2021 PCF Scientific Retreat. We hope that sharing this knowledge will help to improve the understanding of the current state of research and direct new advances in prostate cancer research and care.
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Affiliation(s)
- Andrea K Miyahira
- Science Department, Prostate Cancer Foundation, Santa Monica, California, USA
| | - Howard R Soule
- Science Department, Prostate Cancer Foundation, Santa Monica, California, USA
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30
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Prostate specific membrane antigen positron emission tomography in primary prostate cancer diagnosis: First-line imaging is afoot. Cancer Lett 2022; 548:215883. [PMID: 36027998 DOI: 10.1016/j.canlet.2022.215883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 08/11/2022] [Indexed: 11/23/2022]
Abstract
Prostate specific membrane antigen positron emission tomography (PSMA PET) is an excellent molecular imaging technique for prostate cancer. Currently, PSMA PET for patients with primary prostate cancer is supplementary to conventional imaging techniques, according to guidelines. This supplementary function of PSMA PET is due to a lack of systematic review of its strengths, limitations, and potential development direction. Thus, we review PSMA ligands, detection, T, N, and M staging, treatment management, and false results of PSMA PET in clinical studies. We also discuss the strengths and challenges of PSMA PET. PSMA PET can greatly increase the detection rate of prostate cancer and accuracy of T/N/M staging, which facilitates more appropriate treatment for primary prostate cancer. Lastly, we propose that PSMA PET could become the first-line imaging modality for primary prostate cancer, and we describe its potential expanded application.
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31
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Voter AF, Werner RA, Pienta KJ, Gorin MA, Pomper MG, Solnes LB, Rowe SP. Piflufolastat F-18 ( 18F-DCFPyL) for PSMA PET imaging in prostate cancer. Expert Rev Anticancer Ther 2022; 22:681-694. [DOI: 10.1080/14737140.2022.2081155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Andrew F. Voter
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Transitional Year Residency Program, Aurora St. Luke’s Medical Center, Advocate Aurora Health, Milwaukee, WI, USA
| | - Rudolf A. Werner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Kenneth J. Pienta
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael A. Gorin
- Urology Associates and UPMC Western Maryland, Cumberland, MD, USA
- Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Martin G. Pomper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lilja B. Solnes
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Steven P. Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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The future of radiology: What if artificial intelligence is really as good as predicted? Diagn Interv Imaging 2022; 103:385-386. [DOI: 10.1016/j.diii.2022.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 12/30/2022]
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Gafita A, Marcus C, Kostos L, Schuster DM, Calais J, Hofman MS. Predictors and Real-World Use of Prostate-Specific Radioligand Therapy: PSMA and Beyond. Am Soc Clin Oncol Educ Book 2022; 42:1-17. [PMID: 35609224 DOI: 10.1200/edbk_350946] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PSMA is a transmembrane protein that is markedly overexpressed in prostate cancer, making it an excellent target for imaging and treating patients with prostate cancer. Several small molecule inhibitors and antibodies of PSMA have been radiolabeled for use as therapeutic agents and are currently under clinical investigation. PSMA-based radionuclide therapy is a promising therapeutic option for men with metastatic prostate cancer. The phase II TheraP study demonstrated superior efficacy, lower side effects, and improved patient-reported outcomes compared with cabazitaxel. The phase III VISION study demonstrated that radionuclide therapy with β-emitter 177Lu-PSMA-617 can prolong survival and improve quality of life when offered in addition to standard-of-care therapy in men with PSMA-positive metastatic castration-resistant prostate cancer whose disease had progressed with conventional treatments. Nevertheless, up to 30% of patients have inherent resistance to PSMA-based radionuclide therapy, and acquired resistance is inevitable. Hence, strategies to increase the efficacy of PSMA-based radionuclide therapy have been under clinical investigation. These include better patient selection; increased radiation damage delivery via dosimetry-based administered dose or use of α-emitters instead of β-emitters; or using combinatorial approaches to overcome radioresistance mechanisms (innate or acquired), such as with novel hormonal agents, PARP inhibitors, or immunotherapy.
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Affiliation(s)
- Andrei Gafita
- Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA
| | - Charles Marcus
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Louise Kostos
- Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - David M Schuster
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Jeremie Calais
- Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA
| | - Michael S Hofman
- Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging; Prostate Cancer Theranostics and Imaging Centre of Excellence, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
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Luining WI, Cysouw MCF, Meijer D, Hendrikse NH, Boellaard R, Vis AN, Oprea-Lager DE. Targeting PSMA Revolutionizes the Role of Nuclear Medicine in Diagnosis and Treatment of Prostate Cancer. Cancers (Basel) 2022; 14:1169. [PMID: 35267481 PMCID: PMC8909566 DOI: 10.3390/cancers14051169] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 02/21/2022] [Indexed: 02/08/2023] Open
Abstract
Targeting the prostate-specific membrane antigen (PSMA) protein has become of great clinical value in prostate cancer (PCa) care. PSMA positron emission tomography/computed tomography (PET/CT) is increasingly used in initial staging and restaging at biochemical recurrence in patients with PCa, where it has shown superior detection rates compared to previous imaging modalities. Apart from targeting PSMA for diagnostic purposes, there is a growing interest in developing ligands to target the PSMA-protein for radioligand therapy (RLT). PSMA-based RLT is a novel treatment that couples a PSMA-antibody to (alpha or beta-emitting) radionuclide, such as Lutetium-177 (177Lu), to deliver high radiation doses to tumor cells locally. Treatment with 177Lu-PSMA RLT has demonstrated a superior overall survival rate within randomized clinical trials as compared to routine clinical care in patients with metastatic castration-resistant prostate cancer (mCRPC). The current review provides an overview of the literature regarding recent developments in nuclear medicine related to PSMA-targeted PET imaging and Theranostics.
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Affiliation(s)
- Wietske I. Luining
- Department of Urology, Prostate Cancer Network Netherlands, Amsterdam University Medical Center, VU University, 1081 HV Amsterdam, The Netherlands; (D.M.); (A.N.V.)
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Location VUmc, 1081 HV Amsterdam, The Netherlands; (M.C.F.C.); (N.H.H.); (R.B.); (D.E.O.-L.)
| | - Matthijs C. F. Cysouw
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Location VUmc, 1081 HV Amsterdam, The Netherlands; (M.C.F.C.); (N.H.H.); (R.B.); (D.E.O.-L.)
| | - Dennie Meijer
- Department of Urology, Prostate Cancer Network Netherlands, Amsterdam University Medical Center, VU University, 1081 HV Amsterdam, The Netherlands; (D.M.); (A.N.V.)
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Location VUmc, 1081 HV Amsterdam, The Netherlands; (M.C.F.C.); (N.H.H.); (R.B.); (D.E.O.-L.)
| | - N. Harry Hendrikse
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Location VUmc, 1081 HV Amsterdam, The Netherlands; (M.C.F.C.); (N.H.H.); (R.B.); (D.E.O.-L.)
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Location VUmc, 1081 HV Amsterdam, The Netherlands; (M.C.F.C.); (N.H.H.); (R.B.); (D.E.O.-L.)
| | - André N. Vis
- Department of Urology, Prostate Cancer Network Netherlands, Amsterdam University Medical Center, VU University, 1081 HV Amsterdam, The Netherlands; (D.M.); (A.N.V.)
| | - Daniela E. Oprea-Lager
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Location VUmc, 1081 HV Amsterdam, The Netherlands; (M.C.F.C.); (N.H.H.); (R.B.); (D.E.O.-L.)
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Oprea-Lager DE, Cysouw MC, Boellaard R, Deroose CM, de Geus-Oei LF, Lopci E, Bidaut L, Herrmann K, Fournier LS, Bäuerle T, deSouza NM, Lecouvet FE. Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography. Front Oncol 2021; 11:772530. [PMID: 34869009 PMCID: PMC8640187 DOI: 10.3389/fonc.2021.772530] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/04/2021] [Indexed: 12/14/2022] Open
Abstract
Metastatic tumor deposits in bone marrow elicit differential bone responses that vary with the type of malignancy. This results in either sclerotic, lytic, or mixed bone lesions, which can change in morphology due to treatment effects and/or secondary bone remodeling. Hence, morphological imaging is regarded unsuitable for response assessment of bone metastases and in the current Response Evaluation Criteria In Solid Tumors 1.1 (RECIST1.1) guideline bone metastases are deemed unmeasurable. Nevertheless, the advent of functional and molecular imaging modalities such as whole-body magnetic resonance imaging (WB-MRI) and positron emission tomography (PET) has improved the ability for follow-up of bone metastases, regardless of their morphology. Both these modalities not only have improved sensitivity for visual detection of bone lesions, but also allow for objective measurements of bone lesion characteristics. WB-MRI provides a global assessment of skeletal metastases and for a one-step "all-organ" approach of metastatic disease. Novel MRI techniques include diffusion-weighted imaging (DWI) targeting highly cellular lesions, dynamic contrast-enhanced MRI (DCE-MRI) for quantitative assessment of bone lesion vascularization, and multiparametric MRI (mpMRI) combining anatomical and functional sequences. Recommendations for a homogenization of MRI image acquisitions and generalizable response criteria have been developed. For PET, many metabolic and molecular radiotracers are available, some targeting tumor characteristics not confined to cancer type (e.g. 18F-FDG) while other targeted radiotracers target specific molecular characteristics, such as prostate specific membrane antigen (PSMA) ligands for prostate cancer. Supporting data on quantitative PET analysis regarding repeatability, reproducibility, and harmonization of PET/CT system performance is available. Bone metastases detected on PET and MRI can be quantitatively assessed using validated methodologies, both on a whole-body and individual lesion basis. Both have the advantage of covering not only bone lesions but visceral and nodal lesions as well. Hybrid imaging, combining PET with MRI, may provide complementary parameters on the morphologic, functional, metabolic and molecular level of bone metastases in one examination. For clinical implementation of measuring bone metastases in response assessment using WB-MRI and PET, current RECIST1.1 guidelines need to be adapted. This review summarizes available data and insights into imaging of bone metastases using MRI and PET.
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Affiliation(s)
- Daniela E. Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Matthijs C.F. Cysouw
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Christophe M. Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine & Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, Netherlands
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS – Humanitas Research Hospital, Milan, Italy
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, United Kingdom
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen, and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Laure S. Fournier
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Paris Cardiovascular Research Center (PARCC), Institut National de la Santé et de la Recherche Médicale (INSERM), Radiology Department, Assistance Publique-Hôpitaux de Paris (AP-HP), Hopital europeen Georges Pompidou, Université de Paris, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
| | - Tobias Bäuerle
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Nandita M. deSouza
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Frederic E. Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
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Analytical performance of aPROMISE: automated anatomic contextualization, detection, and quantification of [ 18F]DCFPyL (PSMA) imaging for standardized reporting. Eur J Nucl Med Mol Imaging 2021; 49:1041-1051. [PMID: 34463809 PMCID: PMC8803714 DOI: 10.1007/s00259-021-05497-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 07/09/2021] [Indexed: 11/21/2022]
Abstract
Purpose The application of automated image analyses could improve and facilitate standardization and consistency of quantification in [18F]DCFPyL (PSMA) PET/CT scans. In the current study, we analytically validated aPROMISE, a software as a medical device that segments organs in low-dose CT images with deep learning, and subsequently detects and quantifies potential pathological lesions in PSMA PET/CT. Methods To evaluate the deep learning algorithm, the automated segmentations of the low-dose CT component of PSMA PET/CT scans from 20 patients were compared to manual segmentations. Dice scores were used to quantify the similarities between the automated and manual segmentations. Next, the automated quantification of tracer uptake in the reference organs and detection and pre-segmentation of potential lesions were evaluated in 339 patients with prostate cancer, who were all enrolled in the phase II/III OSPREY study. Three nuclear medicine physicians performed the retrospective independent reads of OSPREY images with aPROMISE. Quantitative consistency was assessed by the pairwise Pearson correlations and standard deviation between the readers and aPROMISE. The sensitivity of detection and pre-segmentation of potential lesions was evaluated by determining the percent of manually selected abnormal lesions that were automatically detected by aPROMISE. Results The Dice scores for bone segmentations ranged from 0.88 to 0.95. The Dice scores of the PSMA PET/CT reference organs, thoracic aorta and liver, were 0.89 and 0.97, respectively. Dice scores of other visceral organs, including prostate, were observed to be above 0.79. The Pearson correlation for blood pool reference was higher between any manual reader and aPROMISE, than between any pair of manual readers. The standard deviations of reference organ uptake across all patients as determined by aPROMISE (SD = 0.21 blood pool and SD = 1.16 liver) were lower compared to those of the manual readers. Finally, the sensitivity of aPROMISE detection and pre-segmentation was 91.5% for regional lymph nodes, 90.6% for all lymph nodes, and 86.7% for bone in metastatic patients. Conclusion In this analytical study, we demonstrated the segmentation accuracy of the deep learning algorithm, the consistency in quantitative assessment across multiple readers, and the high sensitivity in detecting potential lesions. The study provides a foundational framework for clinical evaluation of aPROMISE in standardized reporting of PSMA PET/CT. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05497-8.
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