PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomy
Abstract Objectives This study aims to assess the predictive capability of PSMA-PET imaging for disease outcomes in primary prostate cancer post-radical prostatectomy. In addition to conventional lesion uptake measures, the evaluation includes the distance of lesion to the prostate to enhance risk s...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-025-00907-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849761366325329920 |
|---|---|
| author | Ruohua Chen Ye Li Dong Liang Jianjun Liu Tao Sun |
| author_facet | Ruohua Chen Ye Li Dong Liang Jianjun Liu Tao Sun |
| author_sort | Ruohua Chen |
| collection | DOAJ |
| description | Abstract Objectives This study aims to assess the predictive capability of PSMA-PET imaging for disease outcomes in primary prostate cancer post-radical prostatectomy. In addition to conventional lesion uptake measures, the evaluation includes the distance of lesion to the prostate to enhance risk stratification and outcome prediction. Methods A cohort of 190 men diagnosed with primary prostate cancer and undergoing prostatectomy were initially screened, resulting in 103 patients meeting the selection criteria. Imaging parameters, including lesion SUVmax, primary metabolic tumor volume (PMTV), maximum distance from the lesion to the prostate (Dmax), and total distances from the lesion to the prostate (Dtotal), were extracted from 68Ga-PSMA-11 PET images. Findings were dichotomized based on primary lesion uptake, the tumor volume size, Dmax distance, and the presence of metastatic disease. Postoperative biochemical recurrence-free survival (BCRFS) was analyzed using Kaplan–Meier survival plots and Log-rank tests. Furthermore, univariate and multivariate Cox regression analyses were performed to evaluate the association of PET parameters with survival outcomes. Results Clinical and histopathological characteristics were summarized, including age, weight, height, metastasis status, baseline PSA, biopsy Gleason score, pt stage, margin status, and lymph node status. After a median follow-up of 20 months, 66 events occurred, with the estimated 3-year BCRFS being 46%. Increased PSMA intensity (SUVmax > 17.06) was associated with less favorable BCRFS (log-rank p = 0.017). Increased primary metabolic tumor volume (PMTV > 41.59 cm3) was also linked to less favorable BCRFS (log-rank p = 0.003). Dmax and Dtotal greater than 9.69 cm and 11.95 cm were identified as negative prognostic factors for BCRFS (log-rank p < 0.001 and p = 0.002, respectively). Based on PMTV and Dmax, patients were stratified into low-, intermediate-, and high-risk groups, with 3-year BCRFS rates of 57%, 31%, and 8%, respectively. Univariate Cox regression analysis revealed significant associations between BCRFS and factors such as baseline PSA (HR: 1.69, 95% CI 1.02–2.79, p = 0.042), SUVmax (HR: 1.56, 95% CI 1.04–1.91, p = 0.018), PMTV (HR: 2.05, 95% CI 1.26–3.34, p = 0.004), Dmax (HR: 2.24, 95% CI 1.37–3.65, p = 0.001), and Dtotal (HR: 2.11, 95% CI 1.29–3.45, p = 0.003). Multivariable Cox regression analysis identified the best model with PMTV (HR: 2.57, p = 0.004) and Dmax (HR: 1.98, p = 0.009) as independent predictors for biochemical recurrence (C-index = 0.68). Conclusion The lesion distance to prostate was defined and assessed in conjunction with conventional PET parameters to facilitate preoperative risk stratification in primary prostate cancer following radical prostatectomy. The findings contribute to improved outcome prediction and emphasize the potential of PSMA-PET imaging in enhancing management strategies for prostate cancer patients. Clinical relevance There is a critical need for non-invasive biomarkers that can predict treatment outcomes for patients with primary prostate cancer. Our study introduces the concept of using distance metrics, specifically the lesion distance to prostate in baseline PSMA-PET scans, to improve the prediction of biochemical recurrence following prostatectomy. These distance metrics consider the spatial distribution of lesions, offering a novel approach to assessing tumor spread and its implications for patient outcomes. |
| format | Article |
| id | doaj-art-d5a9446de0e942b4b4ed716773522d80 |
| institution | DOAJ |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-d5a9446de0e942b4b4ed716773522d802025-08-20T03:06:04ZengBMCCancer Imaging1470-73302025-07-0125111010.1186/s40644-025-00907-8PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomyRuohua Chen0Ye Li1Dong Liang2Jianjun Liu3Tao Sun4Department of Nuclear Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesDepartment of Nuclear Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Renji HospitalShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesAbstract Objectives This study aims to assess the predictive capability of PSMA-PET imaging for disease outcomes in primary prostate cancer post-radical prostatectomy. In addition to conventional lesion uptake measures, the evaluation includes the distance of lesion to the prostate to enhance risk stratification and outcome prediction. Methods A cohort of 190 men diagnosed with primary prostate cancer and undergoing prostatectomy were initially screened, resulting in 103 patients meeting the selection criteria. Imaging parameters, including lesion SUVmax, primary metabolic tumor volume (PMTV), maximum distance from the lesion to the prostate (Dmax), and total distances from the lesion to the prostate (Dtotal), were extracted from 68Ga-PSMA-11 PET images. Findings were dichotomized based on primary lesion uptake, the tumor volume size, Dmax distance, and the presence of metastatic disease. Postoperative biochemical recurrence-free survival (BCRFS) was analyzed using Kaplan–Meier survival plots and Log-rank tests. Furthermore, univariate and multivariate Cox regression analyses were performed to evaluate the association of PET parameters with survival outcomes. Results Clinical and histopathological characteristics were summarized, including age, weight, height, metastasis status, baseline PSA, biopsy Gleason score, pt stage, margin status, and lymph node status. After a median follow-up of 20 months, 66 events occurred, with the estimated 3-year BCRFS being 46%. Increased PSMA intensity (SUVmax > 17.06) was associated with less favorable BCRFS (log-rank p = 0.017). Increased primary metabolic tumor volume (PMTV > 41.59 cm3) was also linked to less favorable BCRFS (log-rank p = 0.003). Dmax and Dtotal greater than 9.69 cm and 11.95 cm were identified as negative prognostic factors for BCRFS (log-rank p < 0.001 and p = 0.002, respectively). Based on PMTV and Dmax, patients were stratified into low-, intermediate-, and high-risk groups, with 3-year BCRFS rates of 57%, 31%, and 8%, respectively. Univariate Cox regression analysis revealed significant associations between BCRFS and factors such as baseline PSA (HR: 1.69, 95% CI 1.02–2.79, p = 0.042), SUVmax (HR: 1.56, 95% CI 1.04–1.91, p = 0.018), PMTV (HR: 2.05, 95% CI 1.26–3.34, p = 0.004), Dmax (HR: 2.24, 95% CI 1.37–3.65, p = 0.001), and Dtotal (HR: 2.11, 95% CI 1.29–3.45, p = 0.003). Multivariable Cox regression analysis identified the best model with PMTV (HR: 2.57, p = 0.004) and Dmax (HR: 1.98, p = 0.009) as independent predictors for biochemical recurrence (C-index = 0.68). Conclusion The lesion distance to prostate was defined and assessed in conjunction with conventional PET parameters to facilitate preoperative risk stratification in primary prostate cancer following radical prostatectomy. The findings contribute to improved outcome prediction and emphasize the potential of PSMA-PET imaging in enhancing management strategies for prostate cancer patients. Clinical relevance There is a critical need for non-invasive biomarkers that can predict treatment outcomes for patients with primary prostate cancer. Our study introduces the concept of using distance metrics, specifically the lesion distance to prostate in baseline PSMA-PET scans, to improve the prediction of biochemical recurrence following prostatectomy. These distance metrics consider the spatial distribution of lesions, offering a novel approach to assessing tumor spread and its implications for patient outcomes.https://doi.org/10.1186/s40644-025-00907-8PSMA-PETProstate cancerBiochemical recurrenceOutcome prediction |
| spellingShingle | Ruohua Chen Ye Li Dong Liang Jianjun Liu Tao Sun PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomy Cancer Imaging PSMA-PET Prostate cancer Biochemical recurrence Outcome prediction |
| title | PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomy |
| title_full | PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomy |
| title_fullStr | PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomy |
| title_full_unstemmed | PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomy |
| title_short | PSMA-PET-derived distance features as biomarkers for predicting outcomes in primary prostate cancer post-radical prostatectomy |
| title_sort | psma pet derived distance features as biomarkers for predicting outcomes in primary prostate cancer post radical prostatectomy |
| topic | PSMA-PET Prostate cancer Biochemical recurrence Outcome prediction |
| url | https://doi.org/10.1186/s40644-025-00907-8 |
| work_keys_str_mv | AT ruohuachen psmapetderiveddistancefeaturesasbiomarkersforpredictingoutcomesinprimaryprostatecancerpostradicalprostatectomy AT yeli psmapetderiveddistancefeaturesasbiomarkersforpredictingoutcomesinprimaryprostatecancerpostradicalprostatectomy AT dongliang psmapetderiveddistancefeaturesasbiomarkersforpredictingoutcomesinprimaryprostatecancerpostradicalprostatectomy AT jianjunliu psmapetderiveddistancefeaturesasbiomarkersforpredictingoutcomesinprimaryprostatecancerpostradicalprostatectomy AT taosun psmapetderiveddistancefeaturesasbiomarkersforpredictingoutcomesinprimaryprostatecancerpostradicalprostatectomy |