Survival Significance of Patients With Low Prostate-Specific Antigen and High-Grade Prostate Cancer After Radical Prostatectomy, External Beam Radiotherapy, or External Beam Radiotherapy With Brachytherapy

Biochemical recurrence after chemohormonal therapy followed by robot-assisted radical prostatectomy in very-high-risk prostate cancer patients

Robot-assisted radical prostatectomy (RARP) has become one of the standard radical treatments for prostate cancer (PCa). A retrospective single-center cohort study was conducted on patients with PCa who underwent RARP at Gifu University Hospital between September 2017 and September 2022. In this study, patients were classified into three groups based on the National Comprehensive Cancer Network risk classification: low/intermediate-risk, high-risk, and very-high-risk groups. Patients with high- and very-high-risk PCa who were registered in the study received neoadjuvant chemohormonal therapy prior to RARP. Biochemical recurrence-free survival (BRFS) after RARP in patients with PCa was the primary endpoint of this study. The secondary endpoint was the relationship between biochemical recurrence (BCR) and clinical covariates. We enrolled 230 patients with PCa in our study, with a median follow-up of 17.0 months. When the time of follow-up was over, 19 patients (8.3%) had BCR, and the 2 years BRFS rate for the enrolled patients was 90.9%. Although there was no significant difference in BRFS between the low- and intermediate-risk group and the high/very-high-risk group, the 2 years BRFS rate was 100% in the high-risk group and 68.3% in the very-high-risk group (P = 0.0029). Multivariate analysis showed that positive surgical margins were a significant predictor of BCR in patients with PCa treated with RARP. Multimodal therapies may be necessary to improve the BCR in patients with very-high-risk PCa.

Efficacy and Safety of Neoadjuvant Luteinizing Hormone-Releasing Hormone Antagonist and Tegafur-Uracil Chemohormonal Therapy for High-Risk Prostate Cancer

BACKGROUND

This retrospective single-center cohort study evaluated the efficacy and safety of a combination of neoadjuvant luteinizing hormone-releasing hormone (LHRH) antagonist and tegafur-uracil (UFT) therapy (NCHT) and investigated the medical records of patients with high-risk PCa who underwent robot-assisted radical prostatectomy (RARP). The therapy was followed by RARP for high-risk PCa.

MATERIALS AND METHODS

The enrolled patients were divided into two groups: low-intermediate-risk PCa patients who underwent RARP without neoadjuvant therapy (non-high-risk) and those who underwent NCHT followed by RARP (high-risk group). This study enrolled 227 patients (126: non-high-risk and 101: high-risk group). Patients in the high-risk-group had high-grade cancer compared to those in the non-high-risk-group.

RESULTS

At the median follow-up period of 12.0 months, there were no PCa deaths; two patients (0.9%) died of other causes. Twenty patients developed biochemical recurrence (BCR); the median time until BCR was 9.9 months after surgery. The 2-year biochemical recurrence-free survival rates were 94.2% and 91.1% in the non-high-risk and high-risk-group, respectively (p = 0.465). Grade ≥3 NCHT-related adverse events developed in nine patients (8.9%).

CONCLUSIONS

This study indicates that combining neoadjuvant LHRH antagonists and UFT followed by RARP may improve oncological outcomes in patients with high-risk PCa.

Raised prostate-specific antigen alone may not be a true predictor in high-risk prostate cancer: A retrospective cohort analysis

INTRODUCTION

Prostate-specific antigen (PSA) >20 ng/mL in isolation is a criterion for classification as "high-risk" prostate cancer (PCa). However, among Indian men, PSA elevation is often seen even in the absence of PCa and patients with PSA as the sole criterion for the high-risk disease may have different outcomes from those categorized as high risk due to adverse pathological features. We compared the operative, oncological, and functional outcomes after robot-assisted radical prostatectomy (RARP) in men with high-risk PCa categorized using PSA alone versus clinical and histopathological findings.

MATERIALS AND METHODS

In an Institute Review Board-approved study, men undergoing RARP with high-risk PCa with at least 2-year follow-up were categorized into those with PSA >20 ng/ml being the sole criteria for being high risk (Group A) versus those with Gleason score ≥8 or ≥T2c disease but any PSA level (Group B). The two groups were compared for perioperative, oncological, and functional outcomes.

RESULTS

Fifty-three patients with high-risk disease were included. Twenty-six patients (48.9%) were classified into Group A while 27 patients (50.9%) were classified into Group B. The median PSA was significantly higher in Group A (31 [26-35] ng/ml in Group A vs. 21 [12-34] ng/ml in Group B, P = 0.006) and on histopathology of radical prostatectomy specimen, 24 (92.3%) patients had GG ≤3 disease in Group A versus 10 (37%) patients in Group B (P < 0.001). Patients in both the groups had similar perioperative and continence outcomes. However, Group A had significantly lower biochemical recurrence rate (3/26 [11.5%]) as compared to Group B (11/27 [40.7%]) (P = 0.012).

CONCLUSIONS

PSA >20 ng/ml is the single most common criterion for stratification as high-risk PCa. However, men with PSA >20 ng/ml in isolation, without another adverse criterion, have better outcomes than men with adverse clinical or pathological criteria for high-risk disease.

Estimating the risk of developing secondary hematologic malignancies in patients with T1/T2 prostate cancer undergoing diverse treatment modalities: A large population-based study

Background

Patients with prostate cancer (PC) are at a high risk of developing secondary hematologic malignancies (SHMs) after radiation therapy (RT), while no study has assessed the relationship of different treatment modalities with the occurrence of SHMs after PC at early stage. This study aimed to investigate the risks of developing SHMs in patients with T1/T2 PC undergoing different treatment modalities.

Methods

Patients with T1/T2 PC were identified from the Surveillance, Epidemiology, and End Results database. Competing risk regression (CRR) model was performed to evaluate the hazard ratios (HRs) of developing SHMs. As SHMs scarcely occur, the relative risk (RR) analysis was employed to compare the risks of different treatment modalities associating with the development of SHMs.

Results

The CRR analysis showed that undergoing RT was associated with a higher risk of developing SHMs (external beam radiation therapy [EBRT]: HR = 1.21, 95% confidence interval [CI]: 1.10–1.34; radioactive implant [RI]: HR = 1.20, 95% CI: 1.06–1.36). As for different types of SHMs, EBRT, and RI were correlated with decreased risks of developing CLL (RR = 0.67, 0.72; 95% CI: 0.53–0.85, 0.54–0.96, respectively), but with the increased risks of developing NHL (RR = 1.18, 1.23; 95% CI: 1.02–1.35, 1.05–1.44, respectively); EBRT also showed increased risks of developing acute/ chronic myeloid leukemia (AML/CML, RR = 1.54, 1.56; 95% CI: 1.16–2.03,1.05–2.33, respectively); No increased risk of developing SHMs was detected in patients who only underwent prostatectomy.

Conclusions

Although RT was found to be associated with the increased risks of developing SHMs in patients with T1/T2 PC, this finding cannot be extended to diverse types of SHMs. RT was correlated with the increased risks of the development of NHL, AML, and CML, but with the decreased risk of developing CLL. Prostatectomy did not increase the risk of developing SHMs.

Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model

The majority of patients with prostate cancer die of non-cancer causes of death (COD). It is thus important to accurately predict multi-category COD in these patients. Random forest (RF), a popular machine learning model, has been shown useful for predicting binary cancer-specific deaths. However, its accuracy for predicting multi-category COD in cancer patients is unclear. We included patients in Surveillance, Epidemiology, and End Results-18 cancer registry-program with prostate cancer diagnosed in 2004 (followed-up through 2016). They were randomly divided into training and testing sets with equal sizes. We evaluated prediction accuracies of RF and conventional statistical/multinomial models for 6-category COD by data-encoding types using the 2-fold cross-validation approach. Among 49,864 prostate cancer patients, 29,611 (59.4%) were alive at the end of follow-up, and 5,448 (10.9%) died of cardiovascular disease, 4,607 (9.2%) of prostate cancer, 3,681 (7.4%) of non-prostate cancer, 717 (1.4%) of infection, and 5,800 (11.6%) of other causes. We predicted 6-category COD among these patients with a mean accuracy of 59.1% (n=240, 95% CI, 58.7%-59.4%) in RF models with one-hot encoding, and 50.4% (95% CI, 49.7%-51.0%) in multinomial models. Tumor characteristics, prostate-specific antigen level, and diagnosis confirmation-method were important in RF and multinomial models. In RF models, no statistical differences were found between the accuracies of training versus cross-validation phases, and those of categorical versus one-hot encoding. We here report that RF models can outperform multinomial logistic models (absolute accuracy-difference, 8.7%) in predicting long-term 6-category COD among prostate cancer patients, while pathology diagnosis itself and tumor pathology remain important factors.