Predictor Of Biochemical Recurrence Research Articles

Quantitative Estimation of Iron and Fat Content in Prostate Cancer by Multiparametric MRI and Its Application in Optimizing D'Amico Score.

The risk of biochemical recurrence (BCR) in prostate cancer (PCa) is typically assessed using D'Amico score. However, iron and fat content in PCa are closely related to tumor cell proliferation and the risk of BCR may be estimated using multiparametric MRI (mpMRI). To noninvasively estimate fat and iron content in PCa and to evaluate their utility in enhancing D'Amico scores for predicting BCR in PCa patients. Prospective. Forty-eight male patients in the BCR group (age 71.31 ± 5.74 years) and 27 male patients in the non-BCR group (age 70.3 ± 6.04 years). 3.0 T, Turbo-spin echo T2-weighted imaging, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) imaging, Gradient echo Q-Dixon sequence. The mean fat fraction (FF) and T2* values of lesions were extracted from the FF map and the T2* map. Additionally, prostate volume, mean apparent diffusion coefficient (ADC) value, periprostatic fat thickness (PPFT), subcutaneous fat thickness (SFT), blood lipid content, pre- and post-operative prostate-specific antigen (PSA) values were collected. Stepwise-COX regression analysis was employed to identify the significant predictors of BCR, which led to the construction of an improvement-adjusted (IA) model. Then the IA model as well as the D'Amico score were evaluated using C-index and time-dependent AUC, decision-curve analysis, and Kaplan-Meier curve. P < 0.05 was statistically significant. Significant differences were observed in PSA, D'Amico score, ISUP grade, T2*, FF, and ADC values of the lesions in the BCR group compared with the non-BCR group. Mean T2*, FF, and ADC values of the lesions were screened to construct the IA model incorporated into the D'Amico score (IA Model: C-index = 0.749; AUC = 0.812; D'Amico score: C-index = 0.672; AUC = 0.723). This study demonstrated that mpMRI can quantitatively estimate fat and iron within PCa lesions. By integrating ADC, FF, and T2* values into the D'Amico score, the preoperative-risk assessment for BCR can be improved. 2 TECHNICAL EFFICACY: Stage 2.

Significant Effect of Carbon-Ion Radiation Therapy Combined With Androgen Deprivation on Biochemical Recurrence Rates in High-Risk Prostate Cancer Patients: A Two-Center Controlled Trial Compare With X-Ray External Beam Radiation Therapy.

To compare the effects of carbon-ion radiation therapy (CIRT) and external beam radiotherapy (EBRT) on the prognosis of patients with prostate cancer. The effects of initial prostate-specific antigen (iPSA), clinical Tumor (cT) stage, radiotherapy method, and other clinical factors on the prognosis of 577 patients with radiotherapy were analyzed. Cox regression analysis showed that CIRT (RR: 0.49, p = 0.0215), cT stage ≥ 3 (RR: 2.72, p = 0.0003), and iPSA ≥ 16 ng/mL (RR: 1.74, p = 0.0347) were independent predictors of biochemical recurrence (BCR). After propensity score matching (PSM), CIRT (RR: 0.42, p = 0.0147), cT stage ≥ 3 (RR: 2.55, p = 0.0092), and iPSA ≥ 16 ng/mL (RR: 2.12, p = 0.0366) were still the predictors of univariate analysis. In multivariate analysis, CIRT (RR: 0.42, p = 0.015) and cT stage≥ 3 (RR:2.21, p = 0.0332) were independent predictors of BCR. Among them, we used iPSA and cT stages to establish a new radiotherapy selection model based on BCR risk. Patients who met more than one factor (score ≥ 1) and underwent CIRT had significantly better BCR progression-free survival (PFS) than those who received EBRT (p ≤ 0.01). This was also confirmed by Kaplan-Meier analysis after PSM. CIRT patients exhibited lower 5-year BCR rates compared to the EBRT group. Patients with a risk score of our model ≥ 1 undergoing CIRT were more likely to experience BCR benefits compared to those receiving EBRT.

The Association Between Lymphovascular or Perineural Invasion in Radical Prostatectomy Specimen and Biochemical Recurrence.

The aim of this study was to test for the association between lymphovascular invasion or perineural invasion in radical prostatectomy (RP) specimens and biochemical recurrence (BCR). Relying on a tertiary-care database, we identified prostate cancer patients treated with RP between January 2014 and June 2023. Of these, the majority underwent robotic-assisted RP (81%). Kaplan-Meier survival analyses and Cox regression models addressed BCR according to either lymphovascular invasion or perineural invasion in RP specimens. Additionally, the linear trend test assessed the association between the Gleason Grade Group or pathologic tumor stage and lymphovascular or perineural invasion. Of 822 patients, 78 (9%) exhibited lymphovascular invasion and 633 (77%) exhibited perineural invasion in RP specimens. In survival analyses, the five-year BCR-free survival rates were 62% in patients with lymphovascular invasion vs. 70% in patients without lymphovascular invasion (p = 0.04) and 64% in patients with perineural invasion vs. 82% in patients without perineural invasion (p = 0.01). In univariable Cox regression models, lymphovascular invasion (hazard ratio 1.58, 95% confidence interval 1.01-2.47; p = 0.045) and perineural invasion (hazard ratio 1.77, 95% confidence interval 1.13-2.77; p = 0.013) were both associated with a higher BCR rate. After accounting for age at surgery, PSA value, pathologic tumor stage, Gleason Grade Group, lymph node invasion, positive surgical margin, surgical approach, and adjuvant radiation therapy, lymphovascular (p = 0.740) or perineural invasion (p = 0.341) were not significantly associated with a higher BCR since the Gleason Grade Group and pathologic tumor stage highly correlated with lymphovascular as well as perineural invasion. In univariable models, lymphovascular or perineural invasion is associated with BCR. After adjustment for standard pathologic tumor characteristics, lymphovascular or perineural invasion is not an independent predictor for BCR.

Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review.

Background/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.

Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information

The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy.&#xD;Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners.&#xD;Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion.&#xD;Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model.&#xD;Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images.

Pre-operative prediction of BCR-free survival with mRNA variables in prostate cancer.

Technological innovation yielded opportunities to obtain mRNA expression data for prostate cancer (PCa) patients even prior to biopsy, which can be used in a precision medicine approach to treatment decision-making. This can apply in particular to predict the risk of, and time to biochemical recurrence (BCR). Most mRNA-based models currently proposed to this end are designed for risk classification and post-operative prediction. Effective pre-operative prediction would facilitate early treatment decision-making, in particular by indicating more appropriate therapeutic pathways for patient profiles who would likely not benefit from a systematic prostatectomy regime. The aim of this study is to investigate the possibility to leverage mRNA information pre-operatively for BCR-free survival prediction. To do this, we considered time-to-event machine learning (ML) methodologies, rather than classification models at a specific survival horizon. We retrospectively analysed a cohort of 135 patients with clinical follow-up data and mRNA information comprising over 26,000 features (data accessible at NCBI GEO database, accession GSE21032). The performance of ML models including random survival forest, boosted and regularised Cox models were assessed, in terms of model discrimination, calibration, and predictive accuracy for overall, 3-year and 5-year survival, aligning with common clinical endpoints. Results showed that the inclusion of mRNA information could yield a gain in performance for pre-operative BCR prediction. ML-based time-to-event models significantly outperformed reference nomograms that used only routine clinical information with respect to all metrics considered. We believe this is the first study proposing pre-operative transcriptomics models for BCR prediction in PCa. External validation of these findings, including confirmation of the mRNA variables identified as potential key predictors in this study, could pave the way for pre-operative precision nomograms to facilitate timely personalised clinical decision-making.

Loss of phosphatase and tensin homolog expression castration-sensitive prostate cancer predicts outcomes in men after prostatectomy.

This study aimed to investigate the potential for using the phosphatase and tensin homolog (PTEN) gene as a prognostic marker in post-prostatectomy patients with castration-sensitive prostate cancer (PCa). A total of 180 patients with castration-sensitive PCa who underwent radical prostatectomy at our institution were included in this study. PTEN expression was evaluated using immunohistochemistry, and patients were classified into two groups based on the staining intensity: PTEN-Normal and PTEN-Loss. The association between PTEN expression and biochemical recurrence was analyzed using the Cox proportional hazards model. Patients in the PTEN-Loss group had a higher risk of biochemical recurrence (hazard ratio, 4.642; 95% confidence interval, 2.137-10.083; p < 0.001) and a lower recurrence-free rate compared to the PTEN-Normal group (35% vs. 75%). In addition to clinicopathological factors, such as the serum prostate-specific antigen level, Gleason score, and T stage, evaluation of PTEN expression improved the prediction of biochemical recurrence after prostatectomy (area under the curve, 0.577 vs. 0.688). Low PTEN expression is a significant predictor of biochemical recurrence in patients with castration-sensitive PCa who have already undergone prostatectomy.

Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma.

Positron emission tomography (PET) using radiolabeled prostate-specific membrane antigen targeting PET-imaging agents has been increasingly used over the past decade for imaging and directing prostate carcinoma treatment. Here, we summarize the available literature data on radiomics and machine learning using these imaging agents in prostate carcinoma. Gleason scores derived from biopsy and after resection are discordant in a large number of prostate carcinoma patients. Available studies suggest that radiomics and machine learning applied to PSMA-radioligand avid primary prostate carcinoma might be better performing than biopsy-based Gleason-scoring and could serve as an alternative for non-invasive GS characterization. Furthermore, it may allow for the prediction of biochemical recurrence with a net benefit for clinical utilization. Machine learning based on PET/CT radiomics features was also shown to be able to differentiate benign from malignant increased tracer uptake on PSMA-targeting radioligand PET/CT examinations, thus paving the way for a fully automated image reading in nuclear medicine. As for prediction to treatment outcome following 177Lu-PSMA therapy and overall survival, a limited number of studies have reported promising results on radiomics and machine learning applied to PSMA-targeting radioligand PET/CT images for this purpose. Its added value to clinical parameters warrants further exploration in larger datasets of patients.

Preoperative multiparametric magnetic resonance imaging based risk stratification system for predicting biochemical recurrence after radical prostatectomy

BackgroundMultiparametric magnetic resonance imaging (mpMRI) is used as a current marker in preoperative staging and surgical decision-making, but current evidence on predicting post-surgical oncological outcomes based on preoperative mpMRI findings is limited. In this study We aimed to develop a risk classification based on mpMRI and mpMRI-derived biopsy findings to predict early biochemical recurrence (BCR) after radical prostatectomy. MethodsBetween January 2017 and January 2023, the data of 289 patients who underwent mpMRI, transrectal ultrasound-guided cognitive and fusion targeted biopsies, and subsequent radical prostatectomy (RP) with or without pelvic lymph node dissection in a single center were retrospectively re-evaluated. BCR was defined as a prostate specific-antigen (PSA) ≥ 0.2 ng/mL at least twice after RP. Multivariate logistic regression models tested the predictors of BCR. The regression tree analysis stratified patients into risk groups based on preoperative mpMRI characteristics. Receiver operating characteristic (ROC)-derived area under the curve (AUC) estimates were used to test the accuracy of the regression tree–derived risk stratification tool. ResultsBCR was detected in 47 patients (16.2 %) at a median follow-up of 24 months. In mpMRI based multivariate analyses, the maximum diameter of the index lesion (HR 1.081, 95%Cl 1.015–1.151, p = 0.015) the presence of PI-RADS 5 lesions (HR 2.604, 95%Cl 1.043–6.493, p = 0.04), ≥iT3a stage (HR 2.403, 95%Cl 1.013–5.714, p = 0.046) and ISUP grade ≥4 on biopsy (HR 2.440, 95%Cl 1.123–5.301, p = 0.024) were independent predictors of BCR. In regression tree analysis, patients were stratified into three risk groups: maximum diameter of index lesion, biopsy ISUP grade, and clinical stage on mpMRI. The regression tree–derived risk stratification model had moderate-good accuracy in predicting early BCR (AUC 77 %) ConclusionStraightforward mpMRI and mpMRI-derived biopsy-based risk stratification for BCR prediction provide an additional clinical predictive model to the currently available pathological risk tools.

Prediction of biochemical recurrence after radical prostatectomy from primary tumour characteristics.

To construct and externally calibrate a predictive model for early biochemical recurrence (BCR) after radical prostatectomy (RP) incorporating clinical and modern imaging characteristics of the primary tumour. Patients who underwent RP following multiparametric magnetic resonance imaging, prostate biopsy and prostate-specific membrane antigen-positron emission tomography/computed tomography (PSMA-PET/CT), from two centres in Australia and the Netherlands. The primary outcome was biochemical recurrence-free survival (BRFS), where BCR was defined as a rising PSA level of ≥0.2 ng/mL or initiation of postoperative treatment per clinician discretion. Proportional hazards models to predict time to event were developed in the Australian sample using relevant pre- and post-surgical parameters and primary tumour maximum standardised uptake value (SUVmax) on diagnostic PSMA-PET/CT. Calibration was assessed in an external dataset from the Netherlands with the same inclusion criteria. Data from 846 patients were used to develop the models. Tumour SUVmax was associated with worse predicted 3-year BRFS for both pre- and post-surgical models. SUVmax change from 4 to 16 lessened the predicted 3-year BRFS from 66% to 42% for a patient aged 65 years with typical pre-surgical parameters (PSA level 8 ng/mL, Prostate Imaging-Reporting and Data System score 4/5 and biopsy Gleason score ≥4 + 5). Considering post-surgical variables, a patient with the same age and PSA level but pathological stage pT3a, RP Gleason score ≥4 + 5 and negative margins, SUVmax change from 4 to 16 lessened the predicted 3-year BRFS from 76% to 61%. Calibration on an external sample (n = 464) showed reasonable performance; however, a tendency to overestimate survival in patients with good prognostic factors was observed. Tumour SUVmax on diagnostic PSMA-PET/CT has utility additional to commonly recognised variables for prediction of BRFS after RP.

Risk of Biochemical Recurrence and Metastasis in Prostate Cancer Patients Treated with Radical Prostatectomy After a 10-year Disease-free Interval

Background and objectiveProstate-specific antigen (PSA) testing is used to follow up prostate cancer (PCa) patients treated with radical prostatectomy (RP). Research on PSA thresholds for identifying PCa patients with biochemical recurrence (BCR) who are at a higher risk of progression yielded inconclusive results. This study aims to investigate the risk of late BCR in PCa patients treated with RP and long postoperative (120 mo) undetectable PSA follow-up, and to identify prognostic factors for late BCR within this patient cohort. MethodsPCa patients treated with curative RP (1992–2012) and free of BCR during the first 120 mo following RP were retrospectively identified within five European tertiary centers; BCR was defined as two consecutive PSA values of ≥0.2 ng/ml. Kaplan-Meier and Cox regression models tested for an association between BCR and patient or tumor characteristics. Key findings and limitationsThe study cohort consisted of 4639 patients, of whom 243 (5.2%) developed BCR at a medium follow-up of 147 mo. Of those with BCR, 23 (9.5%) subsequently developed metastatic progression. In Kaplan-Meier models, BCR-free survival differed according to advanced tumor status. In multivariable Cox regression models, pT stage (pT3a: hazard ratio [HR]: 1.46; pT3b: HR: 2.42), pathological Gleason score (pGS 3 + 4: HR: 1.71; pGS ≥4 + 3: HR: 2.47), surgical margin (R1/Rx: HR: 1.72), and pNx stage (pNx: HR: 0.72) represented independent predictors for BCR (all p < 0.05). Conversely, age, PSA at diagnosis, and year of surgery failed to achieve independent predictor status for BCR. Conclusions and clinical implicationsAmong PCa patients with an uneventful follow-up of at least 10 yr after RP, still one in 20 patients subsequently develop late BCR. Nevertheless, late BCR and subsequent progression to metastasis (0.3%) rates in patients with pT2 stage and pGS ≤3 + 4 were strikingly low, implicating that abandoning follow-up beyond an uneventful period of 10 yr is justifiable within this cohort of patients. Patient summaryIn this study, prostate cancer patients treated with a radical prostatectomy and at least 10 yr of uneventful prostate-specific antigen testing were identified within five European centers. Relying on these patients, the rate of subsequent late biochemical recurrence was calculated and risk factors were identified for biochemical recurrence following 10 yr of uneventful prostate-specific antigen testing.

Combination of Blood Adiponectin and Leptin Levels Is a Predictor of Biochemical Recurrence in Prostate Cancer Invading the Surrounding Adipose Tissue.

Biochemical recurrence is a process that progresses to castration-resistant prostate cancer (CRPC) and prediction of biochemical recurrence is useful in determining early therapeutic intervention and disease treatment. Prostate cancer is surrounded by adipose tissue, which secretes adipokines, affecting cancer progression. This study aimed to investigate the correlation between blood adipokines and CRPC biochemical recurrence. We retrospectively analyzed the clinical data, including preoperative serum adipokine levels, of 99 patients with pT3a pN0 prostate cancer who underwent proctectomy between 2011 and 2019. The primary outcome was biochemical recurrence (prostate-specific antigen: PSA > 0.2). We identified 65 non-recurrences and 34 biochemical recurrences (one progressed to CRPC). The initial PSA level was significantly higher (p = 0.006), but serum adiponectin (p = 0.328) and leptin (p = 0.647) levels and their ratio (p = 0.323) were not significantly different in the biochemical recurrence group compared with the non-recurrence group. In contrast, significantly more biochemical recurrences were observed in the group with adiponectin < 6 μg/mL and Leptin < 4 ng/mL (p = 0.046), initial PSA > 15 ng/mL, clinical Gleason pattern ≥ 4, and positive resection margin. A significant difference was also observed in the multivariate analysis (hazard ratio: 4.04, 95% confidence interval: 1.21-13.5, p = 0.0232). Thus, low preoperative serum adiponectin and high leptin levels were significantly associated with biochemical recurrence in adipose tissue-invasive prostate cancer, suggesting that they may be useful predictors of biochemical recurrence. Further studies with larger cases are needed to increase the validity of this study.

Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction

Prostate cancer (PCa) is the most common malignant tumor in men. Currently, there are few prognosis indicators for predicting PCa outcomes and guiding treatments. Here, we perform comprehensive proteomic profiling of 918 tissue specimens from 306 Chinese patients with PCa using data-independent acquisition mass spectrometry (DIA-MS). We identify over 10,000 proteins and define three molecular subtypes of PCa with significant clinical and proteomic differences. We develop a 16-protein panel that effectively predicts biochemical recurrence (BCR) for patients with PCa, which is validated in six published datasets andone additional 99-biopsy-sample cohort by targeted proteomics. Interestingly, this 16-protein panel effectively predicts BCR across different International Society of Urological Pathology (ISUP) grades and pathological stages and outperforms the D'Amico risk classification system in BCR prediction. Furthermore, double knockout of NUDT5 and SEPTIN8, two components from the 16-protein panel, significantly suppresses the PCa cells to proliferate, invade, and migrate, suggesting the combination of NUDT5 and SEPTIN8 may provide new approaches for PCa treatment.

Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer

PurposeThe aim of this study is to research the value of the texture analysis of primary tumors in pre-treatment [68Ga]Ga-PSMA PET in the prediction of the development of biochemical recurrence (BCR) in prostate cancer patients who underwent definitive therapies. Methods51 patients with prostate adenocarcinoma who had a pre-treatment [68Ga]Ga-PSMA-11 PET/CT and underwent definitive radiotherapy (RT) or radical prostatectomy (RP) were included in the study. Demographics, clinicopathologic features, the presence of BCR, and the last follow-up date of patients were recorded. Textural and conventional PET parameters (maximum standardized uptake value (SUVmax), total lesion-PSMA (TL-PSMA), and PSMA-tumor volume (PSMA-TV)) were obtained from PET/CT images using LifeX program. Parameters were grouped using the Youden index in ROC analysis. Factors predicting the BCR were determined using Cox regression analyses. Results29 (56.9%) patients have received primary curative RT, while the remaining 22 (43.1%) patients have undergone RP. 5 (22.7%) patients with RP and 3 (10.3%) patients with curative RT have developed BCR during the follow-up. INTENSITY-BASED-minimum grey level (P=.050), GLCM-sum variance (P=.019), and GLCM-cluster prominence (P=.050) were associated with BCR in univariate analysis. INTENSITY-BASED-minimum grey level (P=.009) and GLCM-sum variance (P=.004) were found as independent predictors of BCR in the multivariate analysis. ConclusionTumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET is associated with a high risk of BCR in PCa patients who underwent definitive therapies.

Comparison of oncological outcomes between extended and no pelvic lymph node dissection in patients with high- or very high-risk prostate cancer: a multi-institutional study

BackgroundDespite providing valuable staging and prognostic information, the therapeutic benefit of pelvic lymph node dissection (PLND) remains uncertain. We sought to assess the effect of extended PLND (ePLND) on the biochemical recurrence (BCR) of patients with National Comprehensive Cancer Net (NCCN) high- or very high-risk prostate cancer treated via robot-assisted radical prostatectomy (RARP). MethodsWe used a multi-institutional database (six centers) to assess 989 patients who underwent RARP from 2014 to 2022 with or without ePLND, among which 699 patients underwent BCR analysis. We performed 1:1 propensity score matching to account for potential differences between the two groups and compared them in terms of BCR-free survival. Cox's regression models were used to test the effect of ePLND on BCR. ResultsA total of 585 patients underwent ePLND and 404 did not. A median of 19 lymph nodes was removed in the ePLND cohort. After propensity score matching, no significant differences in BCR-free survival were observed between the two cohorts (HR 1.108, 95% CI 0.776–1.582, p = 0.556). Multivariable Cox's regression models adjusted for the preoperative and postoperative tumor characteristics revealed that PLND was not an independent predictor of BCR. ConclusionNo significant differences in BCR-free survival were observed between NCCN high- or very high-risk prostate cancer patients who underwent PLND during RARP and those who did not. The therapeutic utility of PLND thus remains unclear.

Predictive Factors for Early Biochemical Recurrence Following Robot-assisted Radical Prostatectomy.

The primary objective of this study was to identify predictors for biochemical recurrence (BCR) within 2 years following robot-assisted radical prostatectomy (RARP). Identifying predictors will enable insights that enhance personalized patient management and facilitate the ongoing refinement of postoperative therapy strategies. This retrospective study included patients undergoing RARP from September 2014 to January 2021. Exclusion criteria were preoperative endocrine therapy, BCR beyond 2 years post-surgery, and incomplete postoperative data. Multivariate analyses were conducted to evaluate predictors of BCR, focusing on preoperative prostate-specific antigen (PSA) level, pathological tumor (pT) stage, Gleason score (GS), extraprostatic extension (EPE), and surgical margin status. Among 374 patients, 40 experienced BCR within 2 years. Significant predictors of early BCR included initial PSA level ≥10 ng/ml, pT3 or greater, GS ≥8, EPE, and positive surgical margins (RM1). Multivariate analysis identified pT3 or higher, GS ≥8, and RM1 as independent risk factors for early BCR. Early BCR after RARP is significantly associated with advanced pathological stage, high GS, and positive surgical margins. These findings emphasize the need for tailored postoperative management strategies and highlight the importance of precision in surgical technique to improve patient outcomes.

ML Models Built Using Clinical Parameters and Radiomic Features Extracted from 18F-Choline PET/CT for the Prediction of Biochemical Recurrence after Metastasis-Directed Therapy in Patients with Oligometastatic Prostate Cancer.

Oligorecurrent patients (≤5 lesions) at 18F-choline PET/CT and treated with MDT were collected. A per-patient and per-lesion analysis was performed, using 2-year biochemical recurrence (BCR) after MDT as the standard of reference. Clinical parameters and radiomic features (RFts) extracted from 18F-choline PET/CT were used for training five ML Models for both CT and PET images. The performance metrics were calculated (i.e., Area Under the Curve-AUC; Classification Accuracy-CA). A total of 46 metastases were selected and segmented in 29 patients. BCR after MDT occurred in 20 (69%) patients after 2 years of follow-up. In total, 73 and 33 robust RFTs were selected from CT and PET datasets, respectively. PET ML Models showed better performances than CT Models for discriminating BCR after MDT, with Stochastic Gradient Descent (SGD) being the best model (AUC = 0.95; CA = 0.90). ML Models built using clinical parameters and CT and PET RFts extracted via 18F-choline PET/CT can accurately predict BCR after MDT in oligorecurrent PCa patients. If validated externally, ML Models could improve the selection of oligorecurrent PCa patients for treatment with MDT.

Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.

Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.

Limited prognostic role of routine serum markers (AP, CEA, LDH and NSE) in oligorecurrent prostate cancer patients undergoing PSMA-radioguided surgery

IntroductionWe evaluated the prognostic role of pre-salvage prostate-specific membrane antigen–radioguided surgery (PSMA-RGS) serum levels of alkaline phosphatase (AP), carcinoembryonic antigen (CEA), lactate dehydrogenase (LDH), and neuron-specific enolase (NSE).Materials and methodsPatients who consecutively underwent PSMA-RGS for prostate cancer (PCa) oligorecurrence between January 2019 and January 2022 were selected. Biomarkers were assessed one day before surgery. Cox regression and logistic regression models tested the relationship between biochemical recurrence-free survival (BFS), 6- and 12-month biochemical recurrence (BCR), and several independent variables, including biomarkers.Results153 consecutive patients were analyzed. In the univariable Cox regression analysis, none of the biomarkers achieved predictor status (AP: hazard ratio [HR] = 1.03, 95% CI 0.99, 1.01; p = 0.19; CEA: HR = 1.73, 95% CI 0.94, 1.21; p = 0.34; LDH: HR = 1.01, 95% CI 1.00, 1.01; p = 0.05; NSE: HR = 1.02, 95% CI 0.98, 1.06; p = 0.39). The only independent predictor of BFS was the number of positive lesions on PSMA PET (HR = 1.17, 95% CI 1.02, 1.30; p = 0.03).The number of positive lesions was confirmed as independent predictor for BCR within 6 and 12 months (BCR < 6 months: odds ratio [OR] = 1.1, 95% CI 1.0, 1.3; p = 0.04; BCR < 12 months: OR = 1.1, 95% CI 1.0, 1.3; p = 0.04).ConclusionThe assessment of AP, CEA, LDH, and NSE before salvage PSMA-RGS showed no prognostic impact. Further studies are needed to identify possible predictors that will optimize patient selection for salvage PSMA-RGS.

Long-Term Oncological Outcomes after Nerve-Sparing Robot-Assisted Radical Prostatectomy for High-Risk Localized Prostate Cancer: A Single-Center, Two-Arm Prospective Study.

To compare the oncological outcomes of patients with high-risk localized prostate cancer undergoing nerve-sparing and non-nerve-sparing robot-assisted radical prostatectomy (RARP). Between November 2002 and December 2018, we prospectively recorded the data of patients undergoing RARP for high-risk localized prostate cancer (PCa) at our tertiary referral center. NSS (nerve-sparing surgery) was carefully offered on the basis of the preoperative clinical characteristics of the patients and an intraoperative assessment. The patients were stratified into two groups: nerve-sparing and non-nerve-sparing groups (yes/no). Radical prostatectomies were performed by 10 surgeons with a robot-assisted technique using a daVinci® surgical system. The primary oncological outcome evaluated was biochemical recurrence (BCR). The secondary oncological outcomes assessed were positive surgical margins (PSMs) and cancer-specific survival (CSS). A total of 779 patients were included in the study: 429 (55.1%) underwent NSS while 350 (44.9%) underwent non-NSS. After a mean (±SD) follow-up of 192 (±14) months, 328 (42.1%) patients developed BCR; no significant difference was found between the NSS and non-NSS groups (156 vs. 172; p = 0.09). Both our univariable and multivariable analyses found that the nerve-sparing approach was not a predictor of BCR (p > 0.05). Kaplan-Mayer survival curves for BCR showed no significant difference among the non-NSS, unilateral NSS, and bilateral NSS groups (log rank test = 0.6). PSMs were reported after RARPs for 254 (32.6%) patients, with no significant difference between the NSS and non-NSS group (143 vs. 111; p = 0.5). In the subgroup of 15 patients who died during the follow-up period, mean (±SD) CSS was 70.5 (±26.1) months, with no significant difference between the NSS and non-NSS groups (mean CSS: 70.3 vs. 70.7 months). NSS does not appear to negatively impact the oncological outcomes of patients with high-risk PCa. Randomized clinical trials are needed to confirm our promising findings.