Prostate cancer in Pennsylvania: The role of older age at diagnosis, aggressiveness, and environmental risk factors on treatment and mortality using data from the Pennsylvania Cancer Registry

Disparities in Palliative Care Use for Patients With Blood Cancer Who Died in the Hospital

Background: Palliative care can enhance quality of life during a terminal hospitalization. Despite advances in diagnostic and treatment tools, blood cancers lag behind solid malignancies in palliative use. It is not clear what factors affect palliative care use in blood cancer. Methods: We used the 2016 to 2019 National Inpatient Sample to identify demographic and socioeconomic factors associated with receiving palliative care among patients over age 18 with any malignant hematological diagnosis during a terminal hospitalization lasting at least 3 days, excluding those receiving a stem cell transplant. Results: Palliative care use was documented 54% of the time among 49,720 weighted cases (9944 distinct individual hospitalizations), approximately evenly distributed across the years 2016-2019. Palliative care use was lowest in 2016 (51%) and highest in 2018 (58%), and increased with age, reaching 58% for those 80 years and older. Men and women were similarly likely to receive care. Patients of Hispanic ethnicity and African Americans received less palliative care (47% and 49%, respectively), as did those insured by Medicaid (48%), and those admitted to small or rural hospitals (52% and 47%, respectively). Charges for hospitalizations with palliative care were 19% lower than for those without it. Conclusions: This study highlights disparities in palliative care use among blood-cancer patients who died in the hospital. It seems likely that many of the 46% who did not receive palliative care could have benefitted from it. Interventions are likely needed to achieve equitable access to ideal levels of palliative care services in late-stage blood cancer.

Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data

Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors.

Hormonal treatment for newly diagnosed metastatic prostate cancer: a population-based study from the California cancer registry

INTRODUCTION

To evaluate how often men with metastatic prostate cancer (mPC) receive standard of care treatment with androgen deprivation therapy (ADT).

METHODS

Men aged ≥20 years with newly diagnosed mPC (stage IV) between 2010 and 2018 were identified using California Cancer Registry data. Receipt of hormonal therapy as initial cancer treatment was examined by patient/tumor characteristics at time of diagnosis. Chi-square tests and logistic regression, adjusted for covariates, were performed to assess association between receipt of hormonal therapy and patient/tumor characteristics.

RESULTS

We identified 13,680 men with newly diagnosed mPC, of which 3637 had local metastasis (N1) only while 9596 had distant metastasis (M1) with or without N1 disease. 21.8 % (n = 2980) of men did not receive ADT. The highest rate of receiving ADT was among men between ages 75-84 (81.6%) and the lowest rate was in men over 85 (76.0%). Asian men had the largest proportion receiving ADT (n = 962, 81.5%) with remaining subgroups having similar proportion of men receiving ADT (76.8% to 77.2%). Once adjusted for covariates, regression results showed men with a higher Gleason score (8-10) were more likely to receive ADT (OR 2.04, 1.82-2.27, p = < 0.001) as well as men with distant sites of metastatic disease (OR 4.02, 3.62-4.46, p = < 0.001). Men residing in neighborhoods with the lowest socioeconomic status were least likely to receive ADT (OR 0.79, 0.68-0.93, p = 0.0032). No differences in receipt of ADT were observed by race/ethnicity.

DISCUSSION

Despite significant advancements in the treatment of mPC in recent years, over one-fifth of patients did not receive ADT, which is the backbone for all new systemic therapies. This dataset might help address some of the prostate cancer care disparities in California.

Association between Alcohol Intake and Prostate Cancer Mortality and Survival

We conducted a systematic review and meta-analysis to investigate the role of alcohol consumption with the prognosis of prostate cancer (PCa). Published reports were gathered on 15 October 2022, from PUBMED/MEDLINE and EMBASE. We found 19 independent eligible studies on the association between consumption of alcoholic beverages and the risk of fatal PCa (n = 5), PCa mortality (n = 5) in healthy subjects, and PCa patients' survival (n = 7) or surrogates thereof (n = 2). We used random effects meta-analysis to obtain a summary risk estimate (SRE) and 95% confidence intervals (95%CI) for incidence of fatal PCa and PCa mortality. The meta-analysis revealed no association between alcohol consumption and fatal prostate cancer incidence risk in healthy subjects with an indication for publication bias, but omitting the study that mainly increased the between-study heterogeneity, the SRE becomes significant (SRE 1.33, 95%CI 1.12-1.58), and the heterogeneity disappeared (I² = 0%) with no indication of publication bias. No association of alcohol consumption was found with mortality risk in PCa patients (SRE 0.97, 95%CI 0.92-1.03) and PCa mortality risk in healthy subjects (SRE 1.03, 95%CI 0.82-1.30). In conclusion, this study suggests that there is some evidence of an association between high alcohol consumption and an increased risk of incidence of fatal prostate cancer in healthy subjects. Given the inconsistencies this result warrants further confirmation.

The influence of prostate volume on clinical parameters in prostate cancer screening

PURPOSE

The purpose of the study was to evaluate the diagnostic significance of two new and a few clinical markers for prostate cancer (PCa) at various prostate volumes (PV).

METHODS

The study subjects were divided into two groups. Among them, there were 70 cases in the PV ≤30 ml group (benign prostatic hyperplasia [BPH]: 32 cases, PCa: 38 cases) and 372 cases in the PV > 30 ml group (BPH: 277 cases, PCa: 95 cases). SPSS 26.0 and GraphPad Prism 8.0 were used to construct their receiver operating characteristic (ROC) curves for diagnosing PCa and calculating their area under the ROC curve (AUC).

RESULTS

In the PV ≤30 ml group, the diagnostic parameters based on prostate-specific antigen (PSA) had a decreased diagnostic significance for PCa. In the PV > 30 ml group, PSAD (AUC = 0.709), AVR (AVR = Age/PV, AUC = 0.742), and A-PSAD (A-PSAD = Age×PSA/PV, AUC = 0.736) exhibited moderate diagnostic significance for PCa, which was better than PSA-AV (AUC = 0.672), free PSA (FPSA, AUC = 0.509), total PSA (TPSA, AUC = 0.563), (F/T) PSA (AUC = 0.540), and (F/T)/PSAD (AUC = 0.663). Compared with AVR, A-PSAD exhibited similar diagnostic significance for PCa, but higher than PSA density (PSAD).

CONCLUSIONS

Choosing appropriate indicators for different PVs could contribute to the early screening and diagnosis of PCa. The difference in the diagnostic value of two new indicators (A-PSAD and AVR), and PSAD for PCa may require further validation by increasing the sample size.

Stem Cells as Target for Prostate cancer Therapy: Opportunities and Challenges

Cancer stem cells (CSCs) and cells in a cancer stem cell-like (CSCL) state have proven to be responsible for tumor initiation, growth, and relapse in Prostate Cancer (PCa) and other cancers; therefore, new strategies are being developed to target such cellular populations. TLR3 activation-based immunotherapy using Polyinosinic:Polycytidylic acid (PIC) has been proposed to be used as a concomitant strategy to first-line treatment. This strategy is based on the induction of apoptosis and an inflammatory response in tumor cells. In combination with retinoids like 9cRA, this treatment can induce CSCs differentiation and apoptosis. A limitation in the use of this combination is the common decreased expression of TLR3 and its main positive regulator p53. observed in many patients suffering of different cancer types such as PCa. Importantly, human exposure to certain toxicants, such as iAs, not only has proven to enrich CSCs population in an in vitro model of human epithelial prostate cells, but additionally, it can also lead to a decreased p53, TLR3 and RA receptor (RARβ), expression/activation and thus hinder this treatment efficacy. Therefore, here we point out the relevance of evaluating the TLR3 and P53 status in PCa patients before starting an immunotherapy based on the use of PIC +9cRA to determine whether they will be responsive to treatment. Additionally, the use of strategies to overcome the lower TLR3, RARβ or p53 expression in PCa patients, like the inclusion of drugs that increase p53 expression, is encouraged, to potentiate the use of PIC+RA based immunotherapy in these patients.

Clinical research analysis based on prostate cancer screening diagnosis

This study aimed to analyse the clinical characteristics and risk factors of patients with positive prostate biopsy at 4-20 ng/mL of prostate-specific antigen (PSA), construct a new parameter based on this characteristics and assess its diagnostic value for prostate cancer (PCa). Logistic regression analysis was used to clarify the risk factors of PCa, and a new parameter based on the results was constructed. Compare the diagnostic value of various diagnostic parameters for PCa. Logistic multivariate regression analysis revealed that age (OR, 5.269; 95%CI, 2.762-10.050), comorbid diabetes (OR, 2.437; 95%CI, 1.162-5.111), PSA (OR, 2.462; 95%CI, 1.198-5.059) and prostate volume (PV) (OR, 0.227; 95%CI, 0.100-0.516) are risk factors for PCa. The age, PSA and PV of patients were combined to construct a new parameter, that is A-PSAD = (age × total PSA [TPSA])/PV]. The area under the receiver-operating characteristic curve(AUC) of A-PSAD (0.728) for PCa diagnosis was higher than the AUCs of TPSA (0.581), free prostate-specific antigen (0.514), (F/T)PSA (0.535) and PSAD (0.696), with significant differences. Age, history of diabetes, TPSA and PV are risk factors for PCa(PSA:4-20ng/mL); in addition, A-PSAD has a moderate diagnostic value for PCa and may become a new indicator for PCa screening.

The influence of age on prostate cancer screening index

PURPOSE

This study aimed to identify parameters with a higher diagnostic value for early screening of prostate cancer (PCa) at different ages.

MATERIALS AND METHODS

A total of 294 patients were included and divided into two groups according to the age of patients (≤66 and >66 years). Receiver operating characteristic (ROC) curves of total prostate-specific antigen (TPSA), free PSA (FPSA), (F/T)PSA, PSA density (PSAD), PSA-AV score, the ratio of patients' age to prostate volume (AVR) and (F/T)/PSAD were constructed. The area under the ROC curve (AUC) was calculated, and differences in the AUC values among the above-mentioned parameters were compared.

RESULTS

There were 121 patients in the ≤66 years age group (benign prostatic hyperplasia BPH, 103 patients; PCa 18 patients) and 173 patients in the >66 years age group (BPH, 100 patients; PCa, 73 patients). In the ≤66 years age group, the AUC value of AVR for PCa diagnosis was the highest; however, there was no statistically significant difference compared with the AUC values of PSAD and (F/T)/PSAD; compared with TPSA, FPSA, (F/T)PSA and PSA-AV, the differences were statistically significant. In the >66 years age group, the AUC values of PSAD and PSA-AV for PCa diagnosis were higher than those of TPSA, FPSA, (F/T)PSA and (F/T)/PSAD, and the difference was statistically significant; however, the difference was not statistically significant when compared with the AUC value of AVR.

CONCLUSION

In different age groups, screening indices for PCa diagnosis should be selected according to the age of patients.

Obesity as a Risk Factor for Prostate Cancer Mortality: A Systematic Review and Dose-Response Meta-Analysis of 280,199 Patients

Simple Summary

Results from individual studies on the association between obesity and prostate cancer mortality remain inconclusive; additionally, several large cohort studies have recently been conducted. We aimed to systematically review all available evidence and synthetize it using meta-analytic techniques. The results of our study showed that obesity was associated with prostate cancer specific mortality and all-cause mortality. The temporal association was consistent with a dose-response relationship. Our results demonstrated that obesity, a potentially modifiable prognostic factor, was associated with higher prostate cancer mortality. This study improved the evidence regarding the potential impact of lifestyle on improving prostate cancer prognosis. Strategies aimed at maintaining normal, or reducing abnormal, body mass index in diagnosed prostate cancer patients might improve survival. These results should guide urologists, oncologists, patients, policy-makers and primary care providers with respect to evidence-based practice and counselling concerning lifestyle changes after prostate cancer diagnosis.

Abstract

The aim of this study was to systematically review all evidence evaluating obesity as a prognostic factor for PC mortality. Cohort and case-control studies reporting mortality among PC patients stratified by body mass index (BMI) were included. The risk of mortality among obese patients (BMI ≥ 30) was compared with the risk for normal weight (BMI < 25) patients, pooling individual hazard ratios (HR) in random-effects meta-analyses. Reasons for heterogeneity were assessed in subgroup analyses. Dose-response associations for BMI per 5 kg/m² change were assessed. Among 7278 citations, 59 studies (280,199 patients) met inclusion criteria. Obesity was associated with increased PC-specific mortality (HR: 1.19, 95% CI: 1.10–1.28, I²: 44.4%) and all-cause mortality (HR: 1.09, 95% CI: 1.00–1.18, I²: 43.9%). There was a 9% increase (95% CI: 5–12%, I²: 39.4%) in PC-specific mortality and 3% increase (95% CI: 1–5%, I²: 24.3%) in all-cause mortality per 5 kg/m² increase in BMI. In analyses restricted to the higher quality subgroup (NOS ≥ 8), obesity was associated with increased PC-specific mortality (HR: 1.24, 95% CI: 1.14–1.35, I²: 0.0%) and maintained the dose-response relationship (HR: 1.11 per 5 kg/m² increase in BMI, 95% CI: 1.07–1.15, I²: 26.6%). Obesity had a moderate, consistent, temporal, and dose-response association with PC mortality. Weight control programs may have a role in improving PC survival.

Prostate cancer in Pennsylvania: The role of older age at diagnosis, aggressiveness, and environmental risk factors on treatment and mortality using data from the Pennsylvania Cancer Registry

Background

To assess: (a) cancer treatment in prostate cancer survivors (PCS) by age at diagnosis (ADx) and prostate cancer (PC) aggressiveness; (b) potential impact on PC mortality; and (c) these results in the context of environmental/behavioral risk factors on PCS in Pennsylvania.

Methods

Prostate cancer survivors ages ≥40 years were identified from the 2004‐2014 Pennsylvania Cancer Registry (PCR). Demographic/clinical descriptors and PC treatment were extracted from PCR. Prostate cancer aggressiveness was defined by clinical/pathologic Gleason score and tumor stage. Logistic and Cox regression analyses tested associations between treatment received and PC‐specific mortality. County‐level data from the Pennsylvania BRFSS were used to estimate cancer‐related behavioral risk factors (eg, smoking, physical inactivity, fruit/vegetable consumption [FV], alcohol use) and used as covariates.

Results

There were 90 694 PCS ages 40‐105 years (mean age = 66.19 years, SD = 9.25) included. Most were non‐Hispanic white men (83%). Prostate cancer survivors ≥75 years were least likely to receive any treatment but men ages 65‐74 were more likely to receive combined therapies (OR = 1.47; 95% CI 1.28, 1.69) vs PCS ages 40‐54 years, controlling for covariates. Prostate cancer survivors 55‐75+ with aggressive PC who received any treatment vs no definitive treatment had significantly reduced mortality. Men from counties with high obesity and smoking rates were significantly less likely to receive any treatment than men living in counties with lower rates of these risk factors. Prostate cancer survivors who lived in counties with high rates of physical inactivity and had high rates of sufficient FV consumption were slightly more likely to receive cancer treatment vs no definitive treatment compared to men who lived in counties with high rates of physical activity and lower FV consumption.

Conclusions

We observed a general age‐related decline in receipt of treatment. Prostate cancer survivors ages ≥75 years were significantly less likely to get any cancer treatment compared to younger PCS. However, most men with more aggressive disease who received any treatment had greatly reduced PC mortality, regardless of age. Considering environmental/behavioral risk factors may attenuate PC risk and inform treatment options.