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Record W4407877750 · doi:10.3390/curroncol32030127

Evaluation of Adrenal Metastases in Prostate Cancer Patients with [68GA]GA-PSMA PET/CT Imaging

2025· article· en· W4407877750 on OpenAlexvenueno aff
Ebuzer Kalender, Edanur Ekinci, Umut Elboğa, Ertan Şahin

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerHounsfield scaleStandardized uptake valueRadiologyNuclear medicineProstateCancerPositron emission tomographyInternal medicineComputed tomography

Abstract

fetched live from OpenAlex

Objectives: This study aimed to evaluate the imaging and clinical characteristics of adrenal metastases detected by [68Ga]Ga-PSMA PET/CT in prostate cancer patients, with a focus on diagnostic accuracy and prognostic implications. Specifically, we examined the correlation between adrenal lesion characteristics and prognostic markers, such as prostate-specific antigen (PSA) levels and Gleason scores. This study also assessed the diagnostic performance of PSA, standardized uptake value maximum (SUVmax), and Hounsfield Unit (HU) values in differentiating adrenal metastases from benign adrenal adenomas. Materials and Methods: This retrospective study included 44 prostate cancer patients with adrenal lesions identified using [68Ga]Ga-PSMA PET/CT between January 2020 and October 2024. The patients were categorized into two groups: benign adrenal adenomas (n = 16) and adrenal metastases (n = 28). The PET/CT imaging was performed using a 5-ring Discovery IQ PET/CT scanner with QClear reconstruction, following the injection of 2.5 MBq/kg [68Ga]Ga-PSMA ligand and a standardized uptake time of 60 min. The imaging parameters (SUVmax and HU values), clinical characteristics (PSA levels, Gleason scores, and presence of lymphadenopathy), and patient outcomes were analyzed. A ROC analysis was conducted to evaluate the diagnostic performance of these key parameters. Results: Patients with adrenal metastases had significantly higher PSA levels (mean: 45.6 ± 12.4 ng/mL vs. 18.3 ± 6.7 ng/mL; p < 0.01) and Gleason scores (median: 8 vs. 6; p < 0.01) than those with benign adenomas. SUVmax values were significantly elevated in metastatic lesions (mean: 12.8 ± 4.3 vs. 3.4 ± 1.2; p < 0.001), and HU values were also higher (mean: 45 ± 15 vs. 18 ± 10; p < 0.01). The ROC analysis revealed that SUVmax had the highest diagnostic accuracy (AUC: 0.87), followed by PSA (AUC: 0.85) and HU (AUC: 0.80). Disease progression was observed in 67.9% of metastatic cases versus 18.8% in the adenoma group (p < 0.001), and median overall survival was shorter in metastatic cases (24 months vs. 38 months; p < 0.01). Conclusions: [68Ga]Ga-PSMA PET/CT is a valuable imaging modality for distinguishing adrenal metastases from benign adenomas in prostate cancer patients. The integration of PSA, SUVmax, and HU values into diagnostic workflows enhances diagnostic precision and improves clinical decision-making. Future research should focus on the prospective validation of these findings in larger cohorts and explore artificial intelligence-based approaches for automated lesion characterization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.090
GPT teacher head0.472
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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