Assessing Ga-68 PSMA PET/CT Parameters in Relation to Clinical and Pathological Risk Stratification in Localized Prostate Cancer

Assessing Ga-68 PSMA PET/CT Parameters in Relation to Clinical and Pathological Risk Stratification in Localized Prostate Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Ga-68 PSMA PET/CT Parameters in Relation to Clinical and Pathological Risk Stratification in Localized Prostate Cancer Furkan AVCI, Recep Halit Tokac, Mustafa Dinckal, Kasim Emre Ergun, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7970578/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accurate pre-operative risk stratification in prostate cancer is essential, yet conventional imaging poorly reflects tumour burden or aggressiveness. Gallium-68 Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography (Ga-68 PSMA PET/CT) provides combined diagnostic and prognostic insights. This study evaluated PSMA PET metrics for their added value beyond established clinical models. Methods We retrospectively analysed 62 men with localised prostate cancer who underwent Ga-68 PSMA PET/CT before radical prostatectomy. Semi-quantitative indices—SUVmax, SUVmean, SUVpeak, PSMA tumour volume (PSMA-TV), total lesion PSMA uptake (TL-PSMA), and PSMA total-lesion quotient (PSMA-TLQ)—were derived from intraprostatic lesions. These were correlated with pre-operative PSA and four risk systems: D’Amico, ISUP Grade, CAPRA, and the Briganti nomogram. Results PSA correlated significantly with all Ga-68 PSMA PET/CT indices (p < 0.05). Volumetric parameters (PSMA-TV and TL-PSMA) were the most consistent discriminators. TL-PSMA and PSMA-TV showed superior ability to differentiate high- from intermediate-risk disease (AUCs: 0.78 and 0.75) compared to SUV-based metrics (AUCs: 0.68–0.72). For distinguishing D’Amico high-risk from low-risk cancer, TL-PSMA and PSMA-TV achieved excellent performance (AUCs: 0.94 and 0.93). Using the Briganti ≥ 7% threshold, both metrics showed identical AUCs (0.87). Optimal cut-offs were TL-PSMA ≥ 34.6 and PSMA-TV ≥ 2.9, yielding sensitivities of 66–81% and specificities of 84–89%. The visual miPSMA score increased stepwise with ISUP grade and correlated positively with PSA (p = 0.03). Conclusions TL-PSMA and PSMA-TV serve as robust, non-invasive markers of tumour aggressiveness, correlating strongly with multiple clinical risk systems. Ga-68 PSMA PET/CT thus offers prognostic value beyond diagnosis, supporting refined risk stratification and personalised treatment planning. PSMA PET-CT Prostate Cancer Risk Stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Prostate cancer is the most frequently diagnosed malignancy in men and a leading cause of cancer-related morbidity worldwide 1 – 4 . With an aging global population and improved diagnostic awareness, prostate cancer continues to pose a major public health challenge.Correctly classifying tumour aggressiveness before treatment is pivotal: patients who are under-staged may miss timely curative therapy, whereas those who are over-staged risk unnecessary morbidity from overtreatment. While the five-year survival rate for patients without distant metastases approaches nearly 100%, this rate drops to approximately 30% for those with metastatic disease 5 Accurate risk stratification therefore underpins personalised management, improves survival, and optimises healthcare resources. Current algorithms combine serum PSA, systematic biopsy, Gleason-based grading and conventional imaging (CT, bone scan). Although helpful, PSA lacks specificity, Gleason scoring is limited by sampling error, and CT/bone scintigraphy under-detect nodal and distant disease 6 – 10 .PSMA is a transmembrane glycoprotein overexpressed on the surface of prostate cancer cells. The mechanism of PSMA-targeted imaging is based on radiolabeled small molecules with high affinity for PSMA-expressing cells 8 .Ga-68 PSMA PET/CT has markedly higher sensitivity and specificity and allows extraction of semi-quantitative (SUVmax, SUVmean, SUVpeak) and volumetric metrics (PSMA tumour volume [PSMA-TV], total-lesion PSMA uptake [TL-PSMA]) 11 – 14 . Early studies link these metrics to outcomes, and structured reporting systems (miPSMA score, miTNM) aim to standardise interpretation 15 , 16 . Nevertheless, volumetric PSMA parameters remain under-validated in localised disease, and their alignment with widely used clinical risk models (D’Amico 17 , ISUP Grade 18 , CAPRA 19 , Briganti 20 ) is poorly defined 21 – 25 . We therefore conducted a retrospective analysis of Ga-68 PSMA PET/CT in men with localised prostate cancer to (i) quantify the diagnostic and prognostic performance of SUV- and volume-based PET metrics, (ii) relate these metrics to pre-operative PSA and four established risk stratification systems, and (iii) identify clinically relevant cut-offs that could be incorporated into hybrid imaging–clinical scoring tools. By clarifying how PSMA-derived volumetric parameters complement conventional predictors, this study seeks to advance evidence-based, individualised treatment planning. Materials and Methods Study Design and Patient Selection This retrospective, single-center study included 62 patients with biopsy-proven, localized prostate cancer who underwent Ga-68 PSMA PET/CT imaging prior to radical prostatectomy between January 2021 and December 2024. Patients were identified from institutional records and selected based on the following criteria: (1) locoregional prostate adenocarcinoma confirmed both histologically (biopsy) and morphologically on Ga-68 PSMA PET/CT, (2) Ga-68 PSMA PET/CT performed within 8 weeks before surgery, and (3) availability of complete clinical, imaging, and pathological data. Exclusion criteria were: presence of distant metastases, prior oncological treatment (e.g., androgen deprivation therapy or radiotherapy), the presence of a concurrent second primary malignancy or inadequate image quality. Among the 932 patients initially screened, 870 were excluded based on these criteria. The final cohort consisted of 62 eligible patients. The detailed patient selection process is illustrated in Fig. 1 . Imaging Protocol Imaging Protocol All PET/CT scans were performed at the Department of Nuclear Medicine, Ege University, using a Siemens Biograph 16 PET/CT scanner. Ga-68 PSMA-11 was synthesized via a fully automated, Good Manufacturing Practice (GMP)-compliant procedure utilizing a Good Radiopharmaceutical Practice (GRP) synthesis module connected to a 68Ge/68Ga generator. The synthesis process was conducted with a disposable single-use cassette kit to ensure sterility and standardization. Radiochemical purity and labeling efficiency (≥ 95%) were verified using radio-high-performance liquid chromatography (radio-HPLC). Each patient received an intravenous injection of 1.8–2.2 MBq/kg of Ga-68 PSMA-11. After an uptake period of approximately 60 minutes, a low-dose CT scan was performed for attenuation correction, followed by PET acquisition from the vertex to the mid-thigh. PET images were reconstructed using iterative algorithms. Image Analysis Ga-68 PSMA PET/CT images were analyzed by two nuclear medicine specialists who were blinded to the clinical and pathological data. The primary intraprostatic lesion was first identified visually and then delineated as a three-dimensional region of interest (ROI) using a semi-automatic threshold-based algorithm. The following semi-quantitative PET parameters were extracted: maximum standardized uptake value (SUVmax), mean SUV (SUVmean), peak SUV (SUVpeak), PSMA tumor volume (PSMA-TV), total lesion PSMA uptake (TL-PSMA = SUVmean × PSMA-TV), and PSMA total lesion quotient (PSMA-TLQ = SUVmax / PSMA-TV). The miPSMA expression score and miTNM classification were also documented according to the PROMISE criteria 16 . Clinical Risk Stratification Each patient was classified preoperatively using four validated risk models: D’Amico, ISUP Grade, CAPRA, and Briganti nomograms. Input variables included PSA level, Gleason score from biopsy, clinical stage, and the percentage of positive biopsy cores. Surgical procedure The surgical procedure involved either open retropubic radical prostatectomy (RRP) or transperitoneal robotic-assisted laparoscopic prostatectomy (RALP) performed using the da Vinci Si System (Intuitive Surgical). Statistical Analysis Statistical analysis was conducted using IBM SPSS Statistics version 25.0. Normality of continuous variables was assessed using the Shapiro–Wilk test. Descriptive statistics were presented as means with ranges. Correlations between PET parameters and PSA levels were evaluated using Spearman’s correlation coefficient. Comparisons of PET-derived parameters across different risk stratification groups (D’Amico, ISUP Grade, CAPRA, Briganti) were made using the Kruskal–Wallis and Mann–Whitney U tests. The diagnostic performance of PET parameters in distinguishing between low, intermediate, and high-risk groups was evaluated using receiver operating characteristic (ROC) curve analysis. Area under the curve (AUC) values, p-values, and 95% confidence intervals were calculated, and the optimal cut-off values were determined using the Youden index. Sensitivity and specificity were reported for each selected cut-off. In addition, pairwise subgroup ROC analyses were performed to determine the discriminatory capacity of PET parameters in differentiating low-intermediate, intermediate-high, and low-high clinical risk groups. Finally, associations between miPSMA score and PSA levels, as well as ISUP Grade classification, were examined using Chi-square tests and effect size measured by Cramér's V. A p-value < 0.05 was considered statistically significant in all analyses. Results Patient Characteristics A total of 62 patients were included in the final analysis. Patient characteristics are summarized in Table 1 . The median age at diagnosis was 67 years (IQR: 7), and the median preoperative PSA level was 10.35 ng/mL (IQR: 9.7). 30 patients (48.4%) underwent RRP, while 32 (51.6%) were treated with RALP. The distribution of patients across the D’Amico, ISUP Grade, CAPRA, and Briganti risk categories is presented in Fig. 2 . The semi-quantitative PET parameters derived from the primary intraprostatic lesion are summarized in Table 2 . There was no statistically significant correlation between patient age and any PET-derived parameters. Table 1 Patient Characteristics Variable Results Number of Patients 62 Median Age (years) 67 (IQR = 7) Median PSA levels (ng/mL) 10.35 (IQR = 9.7) Radical Retropubic Prostatectomy 30 (48.4%) Robotic-assisted Radical Laparoscopic Prostatectomy 32 (51.6%) Note. IQR = interquartile range; PSA = prostate-specific antigen. Table 2 Descriptive Statistics for Patient PSMA PET/CT Parameters Parameter Mean ± Standard Derivation Min Max SUVmax 16,36 11,76 4,82 55,79 SUVpeak 10,55 8,1 3,18 45,34 SUVmean 8,27 5,5 2,91 34,97 PSMA-TV 5,94 4,7 0,4 22,69 TL-PSMA 57,62 75,01 2,61 403,71 PSMA TLQ 1,032 1,564 0,037 11,691 Note. SUVmax = maximum standardized uptake value; SUVpeak = peak standardized uptake value; SUVmean = mean standardized uptake value; PSMA-TV = prostate-specific membrane antigen-derived tumor volume; TL-PSMA = total lesion PSMA uptake; PSMA-TLQ = PSMA total lesion quotient. Values are presented as mean ± standard deviation with minimum-maximum range. Correlation of PET Parameters with PSA Significant positive correlations were observed between pre-operative PSA levels and SUVmax (r = 0.46, p < 0.001), SUVmean (r = 0.39, p < 0.001), SUVpeak (r = 0.47, p < 0.001), PSMA-TV (r = 0.59, p < 0.001), and TL-PSMA (r = 0.59, p < 0.001). No significant association was identified between PSA and PSMA-TLQ. Association with Clinical Risk Stratification Systems All PET parameters, except for PSMA-TLQ, showed statistically significant differences across the D’Amico risk categories (p < 0.05). In pairwise comparisons, volumetric parameters demonstrated superior performance. For distinguishing between D'Amico low- and high-risk groups, TL-PSMA and PSMA-TV exhibited excellent discriminatory power, with AUCs of 0.94 (95% CI: 0.81-1.00) and 0.80 (95% CI: 0.62–0.94), respectively ( Fig. 3 ). Similarly, when assessed against the ISUP Grade groups, all PET metrics except PSMA-TLQ showed significant discriminative capacity for separating intermediate- and high-risk patients (p < 0.05). TL-PSMA was the strongest performer in this regard (AUC: 0.78; 95% CI: 0.65–0.90), with a cutoff of 34.6 yielding 77% (95% CI: 0.53–0.90) sensitivity and 80% (95% CI: 0.63–0.92) specificity (Fig. 3 ) .. For the CAPRA score, all SUV-based and volumetric parameters demonstrated significant discriminatory ability. SUVmax (AUC: 0.74; 95% CI: 0.60–0.85), SUVmean (AUC: 0.73; 95% CI: 0.59–0.84), SUVpeak (AUC: 0.72; 95% CI: 0.58–0.84), PSMA-TV (AUC: 0.69; 95% CI: 0.55–0.83) and TL-PSMA (AUC: 0.70; 95% CI: 0.56–0.83) were significantly associated with CAPRA-based intermediate- and high-risk groups (p < 0.05). A TL-PSMA threshold of 34.6 resulted in 66% (95% CI: 0.48–0.81) sensitivity and 84% (95% CI: 0.64–0.95) specificity (Fig. 4 ) . Moreover, PSMA-TV (p = 0.009, AUC: 0.94; 95% CI: 0.82-1.00) and TL-PSMA (p = 0.009, AUC: 0.93; 95% CI: 0.78-1.00) were highly effective in distinguishing low- from high-risk patients. For the Briganti model, using a 7% threshold to stratify patients into low vs high risk for lymph node involvement, PSMA-TV (AUC: 0.87; 95% CI: 0.73–0.98), TL-PSMA (AUC: 0.87; 95% CI: 0.74–0.97), and PSMA-TLQ (AUC: 0.78; 95% CI: 0.59–0.93) showed statistically significant associations (p < 0.05). The Youden index determined optimal cutoffs of 13.2 for TL-PSMA and 2.9 for PSMA-TV, with corresponding sensitivities of 81% (95% CI: 0.66–0.89) and specificities of 89% (95% CI: 0.52–0.99). Visual Scoring Systems: miPSMA and miTNM A significant relationship was observed between the miPSMA scoring system and ISUP Grade classification (χ²(2) = 13.07; p < 0.05), with a moderate effect size (V = 0.32). Higher miPSMA scores were also significantly associated with elevated PSA levels (p < 0.05). However, no statistically significant association was found between the miTNM staging system and ISUP Grade risk categories, indicating a limited predictive utility of the miTNM score in this specific context. Discussion Accurate preoperative risk stratification remains challenging in localized prostate cancer despite multiple validated clinical tools. We evaluated whether semi-quantitative Ga-68 PSMA PET/CT parameters, particularly volumetric metrics (PSMA-TV and TL-PSMA), correlate with established risk classification systems and enhance prognostic assessment. We found that (i) both volumetric metrics correlate strongly with pre-operative PSA, underscoring that TL-PSMA and PSMA-TV—by combining metabolic activity and tumour volume—may capture overall tumour burden more faithfully than intensity-based measures such as SUVmax or PSMA-TLQ. (ii) Across D’Amico, ISUP and CAPRA scales, TL-PSMA and PSMA-TV consistently outperformed SUV-based indices in separating intermediate- from high-risk disease, emphasising their role in stratifying biological aggressiveness. (iii) Discrimination was even stronger at the extremes (low- vs high-risk) and for Briganti nodal-risk ≥ 7 %, while (iv) the visually assigned miPSMA score rose step-wise with ISUP grade, suggesting that even qualitative scoring can offer prognostic insight when quantitative tools are unavailable. Altogether, these results position volumetric Ga-68 PSMA-PET metrics as practical, non-invasive surrogates of biological aggressiveness in localised prostate cancer. The superiority of TL-PSMA and PSMA-TV over SUV-based parameters in our cohort merits deeper exploration from both technical and biological perspectives. SUVmax, despite its widespread use, suffers from several inherent limitations: it represents a single-voxel measurement susceptible to image noise, it inadequately captures tumor heterogeneity, and it provides no information about disease volume. SUVmean and SUVpeak attempt to address these shortcomings by incorporating regional averages, yet they still fail to account for the three-dimensional extent of PSMA-avid disease. In contrast, PSMA-TV quantifies the entire volume of tissue exceeding a predefined uptake threshold, while TL-PSMA incorporates both volumetric and metabolic components, analogous to metabolic tumor volume (MTV) and total lesion glycolysis (TLG) concepts in FDG PET oncology. The strong concordance we observed between PSMA PET metrics and multiple established risk classification systems—D'Amico, ISUP grade, CAPRA score, and Briganti nomogram—suggests that molecular imaging captures similar biological features to those embodied in clinical-pathological variables. This finding has several important implications. First, it validates the biological relevance of PSMA expression as a surrogate for tumor aggressiveness, extending beyond its primary utility for disease detection. Second, it raises the intriguing possibility that Ga-68 PSMA PET/CT could help resolve discordances or ambiguities within current risk classification schemes. For instance, patients with borderline PSA values or limited biopsy sampling might benefit from additional risk stratification information derived from imaging. In contrast to prior studies such as Koerber et al. 24 , which correlated SUVmax with Gleason score and D’Amico classification but evaluated only a single PET parameter, our study incorporates volumetric indices such as TL-PSMA and PSMA-TV, which demonstrated superior prognostic performance. Similarly, Roberts et al. 25 reported that PSMA uptake intensity predicted progression-free survival in their Australian cohort, but they primarily focused on SUVmax rather than volumetric parameters, potentially explaining why their predictive performance was modest compared to our findings. The critical distinction lies in the fact that volumetric indices capture both the spatial extent and metabolic activity of disease, whereas SUV-based metrics provide only a single-voxel or small-region assessment that may not adequately represent tumor heterogeneity. Although Zschaeck et al. 26 included PSMA-TV in their analysis, they did not consider TL-PSMA and limited their comparisons to Gleason and D’Amico classifications. Our study, in contrast, extends this analysis by incorporating the ISUP Grade, CAPRA score, and Briganti nomogram, thus providing a more integrative perspective. Other studies, such as those by Pratik et al. 27 , also supported the utility of SUVmax but overlooked the contribution of volumetric PET parameters. The findings of Okudan et al. 28 highlighted the significance of PSMA-TV in predicting biochemical recurrence in metastatic disease, reinforcing the value of volumetric metrics, while Aksu et al. 29 reported correlations between PSMA-TV, TL-PSMA, and PSA levels but did not assess their relationship with established clinical scoring systems and included a more heterogeneous patient population. The potential clinical utility of our findings extends across several decision nodes in prostate cancer management. For men considering active surveillance versus immediate intervention, elevated TL-PSMA or PSMA-TV might identify cases at higher risk of harboring occult aggressive disease despite favorable clinical parameters. Current active surveillance eligibility criteria rely heavily on biopsy findings, which are vulnerable to sampling error. Adding quantitative Ga-68 PSMA PET/CT metrics could reduce the risk of inappropriately surveying patients with higher-grade disease missed by biopsy. Conversely, patients with clinically high-risk features but low PSMA tumor burden might be candidates for de-escalated treatment approaches, though such strategies would require prospective validation with long-term oncological outcomes. For surgical planning, the correlation between PSMA metrics and adverse pathological features (positive margins, extraprostatic extension, seminal vesicle invasion) suggests potential value in predicting challenging cases where nerve-sparing approaches may be inadvisable or where extended lymph node dissection is warranted. The Briganti nomogram already guides surgical decision-making regarding lymphadenectomy; our data suggest that Ga-68 PSMA PET/CT metrics could complement or refine these predictions. Our finding that TL-PSMA and PSMA-TV each yield an AUC of 0.87 for nodal-risk ≥ 7 % is clinically pertinent: a TL-PSMA below 35 or PSMA-TV below 3 might safely exclude occult nodal disease and spare low-risk patients from unnecessary dissection. Conversely, elevated volumetric values could prompt extended nodal sampling or intensified systemic therapy. To our knowledge, no earlier study has benchmarked volumetric PSMA metrics against the Briganti model in a purely localised cohort, making our data a novel contribution. However, we must emphasize that our study assessed correlations with risk scores rather than actual surgical outcomes, and the incremental value of adding Ga-68 PSMA PET/CT data to existing nomograms requires formal decision-curve analysis. Radiation oncology applications merit consideration as well. Volumetric PSMA parameters could inform dose escalation strategies, with higher TL-PSMA potentially justifying biological dose escalation or integrated boosts to dominant intraprostatic lesions. The radiation oncology literature has explored similar concepts using multiparametric MRI, but Ga-68 PSMA PET/CT offers superior specificity for malignant tissue and potentially better reflects biological aggressiveness than MRI apparent diffusion coefficient (ADC) values. Whether PSMA-guided dose painting improves biochemical control compared to conventional whole-gland approaches remains to be demonstrated in randomized trials. Despite its strengths, this study has several limitations. It is retrospective in design, conducted at a single center, and includes a relatively modest sample size, which may limit the generalizability of the findings. Moreover, a potential selection bias may exist due to the inclusion of early-stage patients who underwent Ga-68 PSMA PET/CT based on clinician suspicion rather than standard staging protocols. To mitigate these limitations, we applied uniform inclusion criteria, utilized semi-automatic segmentation to reduce inter-observer variability, and validated the identified cut-off values across multiple established risk models. Nonetheless, external validation in larger, prospective, multicenter cohorts—ideally incorporating long-term oncological outcomes—is essential before these findings can be translated into routine clinical practice. Technical factors also merit discussion. Variation in scanner technology, radiotracer dose, uptake time, and reconstruction parameters can substantially affect SUV measurements and potentially volumetric segmentation as well. The lack of standardization across institutions represents a significant barrier to establishing universal cut-off values. Our cut-off values (TL-PSMA ≥ 34.6, PSMA-TV ≥ 2.9) should therefore be viewed as hypothesis-generating rather than prescriptive, requiring validation in cohorts using comparable acquisition and analysis protocols. Clinically, incorporating TL-PSMA and PSMA-TV into pre-operative work-ups could refine risk stratification, identify candidates for intensified therapy or closer surveillance, and reduce overtreatment in truly low-burden disease. Future research should evaluate these cut-offs prospectively, explore their role in active-surveillance protocols, and test whether integrating volumetric PET metrics into dynamic nomograms or AI-based decision tools improves cost-effectiveness and patient-centred outcomes. In conclusion, our study demonstrates that volumetric Ga-68 PSMA PET/CT parameters, particularly TL-PSMA and PSMA-TV, are robust, non-invasive surrogates for pathological aggressiveness in localized prostate cancer. These metrics align closely with multiple clinical risk systems and offer superior discriminatory ability compared to standard SUV-based measurements. The integration of these volumetric parameters into clinical practice has the potential to refine pre-operative risk stratification, leading to more personalized and effective treatment strategies for men with prostate cancer. Further validation in large-scale, prospective studies is warranted to confirm these findings and facilitate their adoption into routine clinical workflows. Declarations Conflict of Interest The authors declared no conflict of interest. Ethical Statement The study protocol was approved by the Ege University Clinical Research Ethics Committee (Decision no: 24-12T/52, date: 12.12.2024). All procedures were performed in accordance with the ethical standards of the institutional research committee and the 1964 Declaration of Helsinki and its later amendments. Informed consent was obtained from all individual participants included in the study. Clinical Trial Registration N/A Animal Studies N/A Financial Disclosure: The authors declared that this study has received no financial support. Use of Language Model Assistance A large language model (ChatGPT, OpenAI) was used to assist in the linguistic refinement, structural editing, and summarization of selected sections of the manuscript. All content was subsequently reviewed and verified by the authors. Availability of Data and Materials Some or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. References Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7970578","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537952671,"identity":"627cbc58-b8d4-4abb-a993-d99e9d1645ab","order_by":0,"name":"Furkan AVCI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBAC9gYGZiiTsfEBhJGAXwvPAYSWZgO4lgPEaWFgkyBOi0TuY4OfOxjkDY4fbqv48MuGgZ89x4D54x58WtKNE3vPMBhuOJPYdnNmXxqDZM8bA4YDz3BrsZdIYz7A28bAOHMGY9tt3p7DDAY3coBa8LiMB6jl4N82BnuQluK/Pf8Z7InRkgy0JbFfgrGNmeHHAQYDCUJaeJ4xG8u2SST38yQ2S/Y2JPNInHlWcOAMPi3sacySb9tsbNvYjz/88OOPnRx/e/LGBxV4tEABNEYY2xh4QDRhDQjwhwS1o2AUjIJRMGIAALH2T+MhZlC9AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0002-2392-2190","institution":"Ege University Faculty of Medicine: Ege Universitesi Tip Fakultesi","correspondingAuthor":true,"prefix":"","firstName":"Furkan","middleName":"","lastName":"AVCI","suffix":""},{"id":537952672,"identity":"92f1a4e8-239a-4674-9af3-7610fe0d901a","order_by":1,"name":"Recep Halit Tokac","email":"","orcid":"","institution":"City Hospital of Izmir","correspondingAuthor":false,"prefix":"","firstName":"Recep","middleName":"Halit","lastName":"Tokac","suffix":""},{"id":537952673,"identity":"9ce4555a-df50-47a8-b6c1-530bca15d772","order_by":2,"name":"Mustafa Dinckal","email":"","orcid":"","institution":"Harakani State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Dinckal","suffix":""},{"id":537952674,"identity":"5a6875eb-783e-4403-851a-3a71a5939b85","order_by":3,"name":"Kasim Emre Ergun","email":"","orcid":"","institution":"Ege University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kasim","middleName":"Emre","lastName":"Ergun","suffix":""},{"id":537952675,"identity":"522823c5-71b0-44ae-8029-673a9cdc724a","order_by":4,"name":"Banu Sarsik Kumbaraci","email":"","orcid":"","institution":"Ege University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Banu","middleName":"Sarsik","lastName":"Kumbaraci","suffix":""},{"id":537952676,"identity":"5f93c5fb-8d1d-4eb2-b463-8aaa503ac165","order_by":5,"name":"Fatih Tamer","email":"","orcid":"","institution":"Ege University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fatih","middleName":"","lastName":"Tamer","suffix":""},{"id":537952677,"identity":"c2362b3e-1533-4a4b-b7c1-6640d07ea581","order_by":6,"name":"Aziz Murat Argon","email":"","orcid":"","institution":"Ege University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Aziz","middleName":"Murat","lastName":"Argon","suffix":""},{"id":537952678,"identity":"61c81dd7-c9ef-4989-a6c6-c3d79be1da90","order_by":7,"name":"Ulkem Yararbas","email":"","orcid":"","institution":"Ege University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ulkem","middleName":"","lastName":"Yararbas","suffix":""}],"badges":[],"createdAt":"2025-10-28 16:52:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7970578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7970578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95667517,"identity":"5669dcff-8ca7-4f51-812f-b691c5a14ccc","added_by":"auto","created_at":"2025-11-11 16:56:53","extension":"xml","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8883,"visible":true,"origin":"","legend":"","description":"","filename":"anmeANMED2500450.xml","url":"https://assets-eu.researchsquare.com/files/rs-7970578/v1/e3381d36802033ea27ba0b02.xml"},{"id":95797867,"identity":"19ef1ff8-b29d-44c1-bc67-1817f6c144c4","added_by":"auto","created_at":"2025-11-13 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08:15:25","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103074,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7970578/v1/f3625d0b8c77abd03246752d.html"},{"id":95667520,"identity":"3da32b45-8c6f-4ebc-8c13-b138c27ac7b4","added_by":"auto","created_at":"2025-11-11 16:56:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePatient Selection Flowchart\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e The flowchart illustrates the screening process, including the total number of patients assessed for eligibility and the reasons for exclusion, resulting in the final study cohort of 62 patients.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7970578/v1/4c88b253a378a1b8fa68d0bc.png"},{"id":95667516,"identity":"f23a8d03-1ce0-436b-8a1f-742a43128edf","added_by":"auto","created_at":"2025-11-11 16:56:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of Patients Across Clinical Risk Stratification Systems\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Pie charts display the proportional distribution of patients stratified by D'Amico classification (low, intermediate, high risk), ISUP Grade Groups (GG) (1-5), CAPRA risk scores (low, intermediate, high risk), and Briganti nomogram risk categories. ISUP = International Society of Urological Pathology; CAPRA = Cancer of the Prostate Risk\u003c/p\u003e\n\u003cp\u003eAssessment.\u003c/p\u003e","description":"","filename":"floatimage216.png","url":"https://assets-eu.researchsquare.com/files/rs-7970578/v1/9648716da2351d5a0342af1a.png"},{"id":95798904,"identity":"e74829af-8646-48bf-bb4a-c34d5ec3a038","added_by":"auto","created_at":"2025-11-13 08:18:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eROC Curve Analysis for Discrimination of Intermediate-Risk Versus High-Risk Prostate Cancer\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Receiver operating characteristic (ROC) curves demonstrate the diagnostic performance of PSMA PET/CT-derived parameters (SUVmax, SUVmean, SUVpeak, PSMA-TV, TL-PSMA, and PSMA-TLQ) for differentiating intermediate- from high-risk disease according to D'Amico classification, ISUP Grade Groups (specifically Grade 2 vs. Grade 3), and CAPRA risk scores. TL-PSMA demonstrated the highest area under the curve (AUC) values across all three risk stratification systems. ISUP = International Society of Urological Pathology; CAPRA = Cancer of the Prostate Risk Assessment; PSMA-TV = PSMA-derived tumor volume; TL-PSMA = total lesion PSMA uptake; PSMA-TLQ = PSMA total lesion quotient; SUV = standardized uptake value.\u003c/p\u003e","description":"","filename":"floatimage318.png","url":"https://assets-eu.researchsquare.com/files/rs-7970578/v1/f98060a6c7e3cb917694e519.png"},{"id":95667525,"identity":"61a4f589-a8e3-4308-b836-eda39233b18b","added_by":"auto","created_at":"2025-11-11 16:56:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3737524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistopathological and Immunohistochemical Features of Prostate Cancer Cases With High Versus Low TL-PSMA Values\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Representative cases illustrating the correlation between TL-PSMA values and tumor grade. \u003cstrong\u003eTop row (Patient 1):\u003c/strong\u003e High TL-PSMA (36.4, above cut-off) corresponding to high-risk disease (D'Amico, ISUP Grade Group 4, CAPRA). (A) Gleason score 4+4=8 adenocarcinoma showing cribriform and fused glandular architecture (H\u0026amp;E, ×20). (B) Positive AMACR (brown) and negative p63 (red) immunostaining (×20). (C) Negative ERG in tumor cells with positive endothelial internal control; negative HMWCK (×20). \u003cstrong\u003eBottom row (Patient 2):\u003c/strong\u003e Low TL-PSMA (below cut-off) corresponding to intermediate-risk disease (D'Amico, ISUP Grade Group 2, CAPRA). (D) Gleason score 3+4=7 adenocarcinoma with well-formed separate glands (pattern 3) and focal fused glands (pattern 4) (H\u0026amp;E, ×20). (E) Positive AMACR (brown) and negative p63 (red) immunostaining (×20). (F) Negative ERG and HMWCK in malignant glands; entrapped benign glands show retained basal cells (arrow, internal control) (×20). TL-PSMA = total lesion PSMA uptake; ISUP = International Society of Urological Pathology; CAPRA = Cancer of the Prostate Risk Assessment; H\u0026amp;E = hematoxylin and eosin; AMACR = alpha-methylacyl-CoA racemase; ERG = ETS-related gene; HMWCK = high-molecular-weight cytokeratin.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7970578/v1/cf55f3efb97ffd3bef6a0442.png"},{"id":97136012,"identity":"919ffcca-8172-4660-bcd0-dab191be3058","added_by":"auto","created_at":"2025-12-01 09:54:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4180965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7970578/v1/30118334-1477-4446-afbf-97c421530704.pdf"}],"financialInterests":"","formattedTitle":"Assessing Ga-68 PSMA PET/CT Parameters in Relation to Clinical and Pathological Risk Stratification in Localized Prostate Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is the most frequently diagnosed malignancy in men and a leading cause of cancer-related morbidity worldwide \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. With an aging global population and improved diagnostic awareness, prostate cancer continues to pose a major public health challenge.Correctly classifying tumour aggressiveness before treatment is pivotal: patients who are under-staged may miss timely curative therapy, whereas those who are over-staged risk unnecessary morbidity from overtreatment. While the five-year survival rate for patients without distant metastases approaches nearly 100%, this rate drops to approximately 30% for those with metastatic disease \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Accurate risk stratification therefore underpins personalised management, improves survival, and optimises healthcare resources.\u003c/p\u003e\u003cp\u003eCurrent algorithms combine serum PSA, systematic biopsy, Gleason-based grading and conventional imaging (CT, bone scan). Although helpful, PSA lacks specificity, Gleason scoring is limited by sampling error, and CT/bone scintigraphy under-detect nodal and distant disease \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.PSMA is a transmembrane glycoprotein overexpressed on the surface of prostate cancer cells. The mechanism of PSMA-targeted imaging is based on radiolabeled small molecules with high affinity for PSMA-expressing cells \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e .Ga-68 PSMA PET/CT has markedly higher sensitivity and specificity and allows extraction of semi-quantitative (SUVmax, SUVmean, SUVpeak) and volumetric metrics (PSMA tumour volume [PSMA-TV], total-lesion PSMA uptake [TL-PSMA]) \u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Early studies link these metrics to outcomes, and structured reporting systems (miPSMA score, miTNM) aim to standardise interpretation \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Nevertheless, volumetric PSMA parameters remain under-validated in localised disease, and their alignment with widely used clinical risk models (D\u0026rsquo;Amico\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, ISUP Grade\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, CAPRA\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, Briganti\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e) is poorly defined \u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe therefore conducted a retrospective analysis of Ga-68 PSMA PET/CT in men with localised prostate cancer to (i) quantify the diagnostic and prognostic performance of SUV- and volume-based PET metrics, (ii) relate these metrics to pre-operative PSA and four established risk stratification systems, and (iii) identify clinically relevant cut-offs that could be incorporated into hybrid imaging\u0026ndash;clinical scoring tools. By clarifying how PSMA-derived volumetric parameters complement conventional predictors, this study seeks to advance evidence-based, individualised treatment planning.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Patient Selection\u003c/h2\u003e\u003cp\u003e This retrospective, single-center study included 62 patients with biopsy-proven, localized prostate cancer who underwent Ga-68 PSMA PET/CT imaging prior to radical prostatectomy between January 2021 and December 2024. Patients were identified from institutional records and selected based on the following criteria: (1) locoregional prostate adenocarcinoma confirmed both histologically (biopsy) and morphologically on Ga-68 PSMA PET/CT, (2) Ga-68 PSMA PET/CT performed within 8 weeks before surgery, and (3) availability of complete clinical, imaging, and pathological data. Exclusion criteria were: presence of distant metastases, prior oncological treatment (e.g., androgen deprivation therapy or radiotherapy), the presence of a concurrent second primary malignancy or inadequate image quality. Among the 932 patients initially screened, 870 were excluded based on these criteria. The final cohort consisted of 62 eligible patients. The detailed patient selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImaging Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eImaging Protocol\u003c/div\u003e\u003cp\u003eAll PET/CT scans were performed at the Department of Nuclear Medicine, Ege University, using a Siemens Biograph 16 PET/CT scanner. Ga-68 PSMA-11 was synthesized via a fully automated, Good Manufacturing Practice (GMP)-compliant procedure utilizing a Good Radiopharmaceutical Practice (GRP) synthesis module connected to a 68Ge/68Ga generator. The synthesis process was conducted with a disposable single-use cassette kit to ensure sterility and standardization. Radiochemical purity and labeling efficiency (\u0026ge;\u0026thinsp;95%) were verified using radio-high-performance liquid chromatography (radio-HPLC). Each patient received an intravenous injection of 1.8\u0026ndash;2.2 MBq/kg of Ga-68 PSMA-11. After an uptake period of approximately 60 minutes, a low-dose CT scan was performed for attenuation correction, followed by PET acquisition from the vertex to the mid-thigh. PET images were reconstructed using iterative algorithms.\u003c/p\u003e\n\u003ch3\u003eImage Analysis\u003c/h3\u003e\n\u003cp\u003eGa-68 PSMA PET/CT images were analyzed by two nuclear medicine specialists who were blinded to the clinical and pathological data. The primary intraprostatic lesion was first identified visually and then delineated as a three-dimensional region of interest (ROI) using a semi-automatic threshold-based algorithm. The following semi-quantitative PET parameters were extracted: maximum standardized uptake value (SUVmax), mean SUV (SUVmean), peak SUV (SUVpeak), PSMA tumor volume (PSMA-TV), total lesion PSMA uptake (TL-PSMA\u0026thinsp;=\u0026thinsp;SUVmean \u0026times; PSMA-TV), and PSMA total lesion quotient (PSMA-TLQ\u0026thinsp;=\u0026thinsp;SUVmax / PSMA-TV). The miPSMA expression score and miTNM classification were also documented according to the PROMISE criteria \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eClinical Risk Stratification\u003c/h3\u003e\n\u003cp\u003eEach patient was classified preoperatively using four validated risk models: D\u0026rsquo;Amico, ISUP Grade, CAPRA, and Briganti nomograms. Input variables included PSA level, Gleason score from biopsy, clinical stage, and the percentage of positive biopsy cores.\u003c/p\u003e\n\u003ch3\u003eSurgical procedure\u003c/h3\u003e\n\u003cp\u003eThe surgical procedure involved either open retropubic radical prostatectomy (RRP) or transperitoneal robotic-assisted laparoscopic prostatectomy (RALP) performed using the da Vinci Si System (Intuitive Surgical).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted using IBM SPSS Statistics version 25.0. Normality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test. Descriptive statistics were presented as means with ranges. Correlations between PET parameters and PSA levels were evaluated using Spearman\u0026rsquo;s correlation coefficient. Comparisons of PET-derived parameters across different risk stratification groups (D\u0026rsquo;Amico, ISUP Grade, CAPRA, Briganti) were made using the Kruskal\u0026ndash;Wallis and Mann\u0026ndash;Whitney U tests. The diagnostic performance of PET parameters in distinguishing between low, intermediate, and high-risk groups was evaluated using receiver operating characteristic (ROC) curve analysis. Area under the curve (AUC) values, p-values, and 95% confidence intervals were calculated, and the optimal cut-off values were determined using the Youden index. Sensitivity and specificity were reported for each selected cut-off. In addition, pairwise subgroup ROC analyses were performed to determine the discriminatory capacity of PET parameters in differentiating low-intermediate, intermediate-high, and low-high clinical risk groups. Finally, associations between miPSMA score and PSA levels, as well as ISUP Grade classification, were examined using Chi-square tests and effect size measured by Cram\u0026eacute;r's V. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant in all analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eA total of 62 patients were included in the final analysis. Patient characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age at diagnosis was 67 years (IQR: 7), and the median preoperative PSA level was 10.35 ng/mL (IQR: 9.7). 30 patients (48.4%) underwent RRP, while 32 (51.6%) were treated with RALP. The distribution of patients across the D\u0026rsquo;Amico, ISUP Grade, CAPRA, and Briganti risk categories is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The semi-quantitative PET parameters derived from the primary intraprostatic lesion are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There was no statistically significant correlation between patient age and any PET-derived parameters.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003ePatient Characteristics\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian Age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (IQR\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian PSA levels (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.35 (IQR\u0026thinsp;=\u0026thinsp;9.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadical Retropubic Prostatectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (48.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRobotic-assisted Radical Laparoscopic Prostatectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (51.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote.\u003c/em\u003e IQR\u0026thinsp;=\u0026thinsp;interquartile range; PSA\u0026thinsp;=\u0026thinsp;prostate-specific antigen.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDescriptive Statistics for Patient PSMA PET/CT Parameters\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026plusmn; Standard Derivation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16,36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11,76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4,82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55,79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVpeak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10,55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45,34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVmean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34,97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSMA-TV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22,69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTL-PSMA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57,62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75,01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e403,71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSMA TLQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11,691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e SUVmax\u0026thinsp;=\u0026thinsp;maximum standardized uptake value; SUVpeak\u0026thinsp;=\u0026thinsp;peak standardized uptake value; SUVmean\u0026thinsp;=\u0026thinsp;mean standardized uptake value; PSMA-TV\u0026thinsp;=\u0026thinsp;prostate-specific membrane antigen-derived tumor volume; TL-PSMA\u0026thinsp;=\u0026thinsp;total lesion PSMA uptake; PSMA-TLQ\u0026thinsp;=\u0026thinsp;PSMA total lesion quotient. Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation with minimum-maximum range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation of PET Parameters with PSA\u003c/h2\u003e\u003cp\u003eSignificant positive correlations were observed between pre-operative PSA levels and SUVmax (r\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SUVmean (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SUVpeak (r\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PSMA-TV (r\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TL-PSMA (r\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant association was identified between PSA and PSMA-TLQ.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAssociation with Clinical Risk Stratification Systems\u003c/h2\u003e\u003cp\u003eAll PET parameters, except for PSMA-TLQ, showed statistically significant differences across the D\u0026rsquo;Amico risk categories (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In pairwise comparisons, volumetric parameters demonstrated superior performance. For distinguishing between D'Amico low- and high-risk groups, TL-PSMA and PSMA-TV exhibited excellent discriminatory power, with AUCs of 0.94 (95% CI: 0.81-1.00) and 0.80 (95% CI: 0.62\u0026ndash;0.94), respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimilarly, when assessed against the ISUP Grade groups, all PET metrics except PSMA-TLQ showed significant discriminative capacity for separating intermediate- and high-risk patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TL-PSMA was the strongest performer in this regard (AUC: 0.78; 95% CI: 0.65\u0026ndash;0.90), with a cutoff of 34.6 yielding 77% (95% CI: 0.53\u0026ndash;0.90) sensitivity and 80% (95% CI: 0.63\u0026ndash;0.92) specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e)\u003c/span\u003e..\u003c/p\u003e\u003cp\u003eFor the CAPRA score, all SUV-based and volumetric parameters demonstrated significant discriminatory ability. SUVmax (AUC: 0.74; 95% CI: 0.60\u0026ndash;0.85), SUVmean (AUC: 0.73; 95% CI: 0.59\u0026ndash;0.84), SUVpeak (AUC: 0.72; 95% CI: 0.58\u0026ndash;0.84), PSMA-TV (AUC: 0.69; 95% CI: 0.55\u0026ndash;0.83) and TL-PSMA (AUC: 0.70; 95% CI: 0.56\u0026ndash;0.83) were significantly associated with CAPRA-based intermediate- and high-risk groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A TL-PSMA threshold of 34.6 resulted in 66% (95% CI: 0.48\u0026ndash;0.81) sensitivity and 84% (95% CI: 0.64\u0026ndash;0.95) specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e)\u003c/span\u003e. Moreover, PSMA-TV (p\u0026thinsp;=\u0026thinsp;0.009, AUC: 0.94; 95% CI: 0.82-1.00) and TL-PSMA (p\u0026thinsp;=\u0026thinsp;0.009, AUC: 0.93; 95% CI: 0.78-1.00) were highly effective in distinguishing low- from high-risk patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the Briganti model, using a 7% threshold to stratify patients into low vs high risk for lymph node involvement, PSMA-TV (AUC: 0.87; 95% CI: 0.73\u0026ndash;0.98), TL-PSMA (AUC: 0.87; 95% CI: 0.74\u0026ndash;0.97), and PSMA-TLQ (AUC: 0.78; 95% CI: 0.59\u0026ndash;0.93) showed statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Youden index determined optimal cutoffs of 13.2 for TL-PSMA and 2.9 for PSMA-TV, with corresponding sensitivities of 81% (95% CI: 0.66\u0026ndash;0.89) and specificities of 89% (95% CI: 0.52\u0026ndash;0.99).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eVisual Scoring Systems: miPSMA and miTNM\u003c/h2\u003e\u003cp\u003eA significant relationship was observed between the miPSMA scoring system and ISUP Grade classification (χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;13.07; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a moderate effect size (V\u0026thinsp;=\u0026thinsp;0.32). Higher miPSMA scores were also significantly associated with elevated PSA levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eHowever, no statistically significant association was found between the miTNM staging system and ISUP Grade risk categories, indicating a limited predictive utility of the miTNM score in this specific context.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccurate preoperative risk stratification remains challenging in localized prostate cancer despite multiple validated clinical tools. We evaluated whether semi-quantitative Ga-68 PSMA PET/CT parameters, particularly volumetric metrics (PSMA-TV and TL-PSMA), correlate with established risk classification systems and enhance prognostic assessment.\u003c/p\u003e\u003cp\u003eWe found that (i) both volumetric metrics correlate strongly with pre-operative PSA, underscoring that TL-PSMA and PSMA-TV\u0026mdash;by combining metabolic activity and tumour volume\u0026mdash;may capture overall tumour burden more faithfully than intensity-based measures such as SUVmax or PSMA-TLQ. (ii) Across D\u0026rsquo;Amico, ISUP and CAPRA scales, TL-PSMA and PSMA-TV consistently outperformed SUV-based indices in separating intermediate- from high-risk disease, emphasising their role in stratifying biological aggressiveness. (iii) Discrimination was even stronger at the extremes (low- vs high-risk) and for Briganti nodal-risk\u0026thinsp;\u0026ge;\u0026thinsp;7 %, while (iv) the visually assigned miPSMA score rose step-wise with ISUP grade, suggesting that even qualitative scoring can offer prognostic insight when quantitative tools are unavailable. Altogether, these results position volumetric Ga-68 PSMA-PET metrics as practical, non-invasive surrogates of biological aggressiveness in localised prostate cancer.\u003c/p\u003e\u003cp\u003eThe superiority of TL-PSMA and PSMA-TV over SUV-based parameters in our cohort merits deeper exploration from both technical and biological perspectives. SUVmax, despite its widespread use, suffers from several inherent limitations: it represents a single-voxel measurement susceptible to image noise, it inadequately captures tumor heterogeneity, and it provides no information about disease volume. SUVmean and SUVpeak attempt to address these shortcomings by incorporating regional averages, yet they still fail to account for the three-dimensional extent of PSMA-avid disease. In contrast, PSMA-TV quantifies the entire volume of tissue exceeding a predefined uptake threshold, while TL-PSMA incorporates both volumetric and metabolic components, analogous to metabolic tumor volume (MTV) and total lesion glycolysis (TLG) concepts in FDG PET oncology.\u003c/p\u003e\u003cp\u003eThe strong concordance we observed between PSMA PET metrics and multiple established risk classification systems\u0026mdash;D'Amico, ISUP grade, CAPRA score, and Briganti nomogram\u0026mdash;suggests that molecular imaging captures similar biological features to those embodied in clinical-pathological variables. This finding has several important implications. First, it validates the biological relevance of PSMA expression as a surrogate for tumor aggressiveness, extending beyond its primary utility for disease detection. Second, it raises the intriguing possibility that Ga-68 PSMA PET/CT could help resolve discordances or ambiguities within current risk classification schemes. For instance, patients with borderline PSA values or limited biopsy sampling might benefit from additional risk stratification information derived from imaging.\u003c/p\u003e\u003cp\u003eIn contrast to prior studies such as Koerber et al.\u003csup\u003e24\u003c/sup\u003e, which correlated SUVmax with Gleason score and D\u0026rsquo;Amico classification but evaluated only a single PET parameter, our study incorporates volumetric indices such as TL-PSMA and PSMA-TV, which demonstrated superior prognostic performance. Similarly, Roberts et al.\u003csup\u003e25\u003c/sup\u003e reported that PSMA uptake intensity predicted progression-free survival in their Australian cohort, but they primarily focused on SUVmax rather than volumetric parameters, potentially explaining why their predictive performance was modest compared to our findings. The critical distinction lies in the fact that volumetric indices capture both the spatial extent and metabolic activity of disease, whereas SUV-based metrics provide only a single-voxel or small-region assessment that may not adequately represent tumor heterogeneity. Although Zschaeck et al. \u003csup\u003e26\u003c/sup\u003e included PSMA-TV in their analysis, they did not consider TL-PSMA and limited their comparisons to Gleason and D\u0026rsquo;Amico classifications. Our study, in contrast, extends this analysis by incorporating the ISUP Grade, CAPRA score, and Briganti nomogram, thus providing a more integrative perspective.\u003c/p\u003e\u003cp\u003eOther studies, such as those by Pratik et al. \u003csup\u003e27\u003c/sup\u003e, also supported the utility of SUVmax but overlooked the contribution of volumetric PET parameters. The findings of Okudan et al. \u003csup\u003e28\u003c/sup\u003e highlighted the significance of PSMA-TV in predicting biochemical recurrence in metastatic disease, reinforcing the value of volumetric metrics, while Aksu et al. \u003csup\u003e29\u003c/sup\u003e reported correlations between PSMA-TV, TL-PSMA, and PSA levels but did not assess their relationship with established clinical scoring systems and included a more heterogeneous patient population.\u003c/p\u003e\u003cp\u003eThe potential clinical utility of our findings extends across several decision nodes in prostate cancer management. For men considering active surveillance versus immediate intervention, elevated TL-PSMA or PSMA-TV might identify cases at higher risk of harboring occult aggressive disease despite favorable clinical parameters. Current active surveillance eligibility criteria rely heavily on biopsy findings, which are vulnerable to sampling error. Adding quantitative Ga-68 PSMA PET/CT metrics could reduce the risk of inappropriately surveying patients with higher-grade disease missed by biopsy. Conversely, patients with clinically high-risk features but low PSMA tumor burden might be candidates for de-escalated treatment approaches, though such strategies would require prospective validation with long-term oncological outcomes.\u003c/p\u003e\u003cp\u003eFor surgical planning, the correlation between PSMA metrics and adverse pathological features (positive margins, extraprostatic extension, seminal vesicle invasion) suggests potential value in predicting challenging cases where nerve-sparing approaches may be inadvisable or where extended lymph node dissection is warranted. The Briganti nomogram already guides surgical decision-making regarding lymphadenectomy; our data suggest that Ga-68 PSMA PET/CT metrics could complement or refine these predictions. Our finding that TL-PSMA and PSMA-TV each yield an AUC of 0.87 for nodal-risk\u0026thinsp;\u0026ge;\u0026thinsp;7 % is clinically pertinent: a TL-PSMA below 35 or PSMA-TV below 3 might safely exclude occult nodal disease and spare low-risk patients from unnecessary dissection. Conversely, elevated volumetric values could prompt extended nodal sampling or intensified systemic therapy. To our knowledge, no earlier study has benchmarked volumetric PSMA metrics against the Briganti model in a purely localised cohort, making our data a novel contribution. However, we must emphasize that our study assessed correlations with risk scores rather than actual surgical outcomes, and the incremental value of adding Ga-68 PSMA PET/CT data to existing nomograms requires formal decision-curve analysis.\u003c/p\u003e\u003cp\u003eRadiation oncology applications merit consideration as well. Volumetric PSMA parameters could inform dose escalation strategies, with higher TL-PSMA potentially justifying biological dose escalation or integrated boosts to dominant intraprostatic lesions. The radiation oncology literature has explored similar concepts using multiparametric MRI, but Ga-68 PSMA PET/CT offers superior specificity for malignant tissue and potentially better reflects biological aggressiveness than MRI apparent diffusion coefficient (ADC) values. Whether PSMA-guided dose painting improves biochemical control compared to conventional whole-gland approaches remains to be demonstrated in randomized trials.\u003c/p\u003e\u003cp\u003eDespite its strengths, this study has several limitations. It is retrospective in design, conducted at a single center, and includes a relatively modest sample size, which may limit the generalizability of the findings. Moreover, a potential selection bias may exist due to the inclusion of early-stage patients who underwent Ga-68 PSMA PET/CT based on clinician suspicion rather than standard staging protocols. To mitigate these limitations, we applied uniform inclusion criteria, utilized semi-automatic segmentation to reduce inter-observer variability, and validated the identified cut-off values across multiple established risk models. Nonetheless, external validation in larger, prospective, multicenter cohorts\u0026mdash;ideally incorporating long-term oncological outcomes\u0026mdash;is essential before these findings can be translated into routine clinical practice.\u003c/p\u003e\u003cp\u003eTechnical factors also merit discussion. Variation in scanner technology, radiotracer dose, uptake time, and reconstruction parameters can substantially affect SUV measurements and potentially volumetric segmentation as well. The lack of standardization across institutions represents a significant barrier to establishing universal cut-off values. Our cut-off values (TL-PSMA\u0026thinsp;\u0026ge;\u0026thinsp;34.6, PSMA-TV\u0026thinsp;\u0026ge;\u0026thinsp;2.9) should therefore be viewed as hypothesis-generating rather than prescriptive, requiring validation in cohorts using comparable acquisition and analysis protocols.\u003c/p\u003e\u003cp\u003eClinically, incorporating TL-PSMA and PSMA-TV into pre-operative work-ups could refine risk stratification, identify candidates for intensified therapy or closer surveillance, and reduce overtreatment in truly low-burden disease. Future research should evaluate these cut-offs prospectively, explore their role in active-surveillance protocols, and test whether integrating volumetric PET metrics into dynamic nomograms or AI-based decision tools improves cost-effectiveness and patient-centred outcomes.\u003c/p\u003e\u003cp\u003e In conclusion, our study demonstrates that volumetric Ga-68 PSMA PET/CT parameters, particularly TL-PSMA and PSMA-TV, are robust, non-invasive surrogates for pathological aggressiveness in localized prostate cancer. These metrics align closely with multiple clinical risk systems and offer superior discriminatory ability compared to standard SUV-based measurements. The integration of these volumetric parameters into clinical practice has the potential to refine pre-operative risk stratification, leading to more personalized and effective treatment strategies for men with prostate cancer. Further validation in large-scale, prospective studies is warranted to confirm these findings and facilitate their adoption into routine clinical workflows.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eConflict of Interest\u003c/h3\u003e\n\u003cp\u003eThe authors declared no conflict of interest.\u003c/p\u003e\n\u003ch3\u003eEthical Statement\u003c/h3\u003e\n\u003cp\u003eThe study protocol was approved by the Ege University Clinical Research Ethics Committee (Decision no: 24-12T/52, date: 12.12.2024). All procedures were performed in accordance with the ethical standards of the institutional research committee and the 1964 Declaration of Helsinki and its later amendments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch3\u003eClinical Trial Registration\u003c/h3\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003ch3\u003eAnimal Studies\u003c/h3\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003ch2\u003eFinancial Disclosure:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declared that this study has received no financial support.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eUse of Language Model Assistance\u003c/h2\u003e\n\u003cp\u003eA large language model (ChatGPT, OpenAI) was used to assist in the linguistic refinement, structural editing, and summarization of selected sections of the manuscript. All content was subsequently reviewed and verified by the authors.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Materials\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSome or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal Burden of Disease Cancer Collaboration, Fitzmaurice C, Abate D, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. 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Nucl Med Commun. 2021;42(11):1254\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MNM.0000000000001459\u003c/span\u003e\u003cspan address=\"10.1097/MNM.0000000000001459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAksu A, Karahan Şen NP, Tuna EB, Aslan G, \u0026Ccedil;apa Kaya G. Evaluation of 68Ga-PSMA PET/CT with volumetric parameters for staging of prostate cancer patients. Nucl Med Commun. 2021;42(5):503\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MNM.0000000000001370\u003c/span\u003e\u003cspan address=\"10.1097/MNM.0000000000001370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PSMA, PET-CT, Prostate Cancer, Risk Stratification","lastPublishedDoi":"10.21203/rs.3.rs-7970578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7970578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAccurate pre-operative risk stratification in prostate cancer is essential, yet conventional imaging poorly reflects tumour burden or aggressiveness. Gallium-68 Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography (Ga-68 PSMA PET/CT) provides combined diagnostic and prognostic insights. This study evaluated PSMA PET metrics for their added value beyond established clinical models.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e We retrospectively analysed 62 men with localised prostate cancer who underwent Ga-68 PSMA PET/CT before radical prostatectomy. Semi-quantitative indices\u0026mdash;SUVmax, SUVmean, SUVpeak, PSMA tumour volume (PSMA-TV), total lesion PSMA uptake (TL-PSMA), and PSMA total-lesion quotient (PSMA-TLQ)\u0026mdash;were derived from intraprostatic lesions. These were correlated with pre-operative PSA and four risk systems: D\u0026rsquo;Amico, ISUP Grade, CAPRA, and the Briganti nomogram.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePSA correlated significantly with all Ga-68 PSMA PET/CT indices (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Volumetric parameters (PSMA-TV and TL-PSMA) were the most consistent discriminators. TL-PSMA and PSMA-TV showed superior ability to differentiate high- from intermediate-risk disease (AUCs: 0.78 and 0.75) compared to SUV-based metrics (AUCs: 0.68\u0026ndash;0.72). For distinguishing D\u0026rsquo;Amico high-risk from low-risk cancer, TL-PSMA and PSMA-TV achieved excellent performance (AUCs: 0.94 and 0.93). Using the Briganti\u0026thinsp;\u0026ge;\u0026thinsp;7% threshold, both metrics showed identical AUCs (0.87). Optimal cut-offs were TL-PSMA\u0026thinsp;\u0026ge;\u0026thinsp;34.6 and PSMA-TV\u0026thinsp;\u0026ge;\u0026thinsp;2.9, yielding sensitivities of 66\u0026ndash;81% and specificities of 84\u0026ndash;89%. The visual miPSMA score increased stepwise with ISUP grade and correlated positively with PSA (p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eTL-PSMA and PSMA-TV serve as robust, non-invasive markers of tumour aggressiveness, correlating strongly with multiple clinical risk systems. Ga-68 PSMA PET/CT thus offers prognostic value beyond diagnosis, supporting refined risk stratification and personalised treatment planning.\u003c/p\u003e","manuscriptTitle":"Assessing Ga-68 PSMA PET/CT Parameters in Relation to Clinical and Pathological Risk Stratification in Localized Prostate Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 16:56:49","doi":"10.21203/rs.3.rs-7970578/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b27db4d-f57e-4cc9-8594-a92712f792ae","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T02:50:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 16:56:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7970578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7970578","identity":"rs-7970578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}