Low risk prostate cancer in men &gt; 70 years old: to treat or not to treat

Rice, Kevin R.; Colombo, ML; Wingate, Jonathan; Chen, Y; Cullen, Jennifer; McLeod, D. G.; Brassell, S. A.

doi:10.1590/s1677-55382011000500030

Cited by 7 publications

(14 citation statements)

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“…In addition to tumour characteristics, it is also important to consider competing mortality risks when determining the best treatment options for elderly patients . Some studies have shown that increased age was independently associated with higher risk of BCR, distant metastasis, and disease‐related death in patients treated with RP ; however, there is no abrupt increase in the hazard for BCR associated with a particular age . Thong et al.…”

Section: Introductionmentioning

confidence: 99%

Higher rates of upgrading and upstaging in older patients undergoing radical prostatectomy and qualifying for active surveillance

et al. 2014

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Objective• To determine pathological and oncological outcomes of patients diagnosed with low-risk prostate cancer in two age cohorts who underwent radical prostatectomy (RP) and qualified for active surveillance (AS) according to Prostate Cancer Research International: Active Surveillance (PRIAS) criteria, as AS for low-risk prostate cancer represents an acceptable management strategy especially for older patients. Patients and Methods• In all, 320 patients aged ≥65 years who underwent RP and were eligible for AS according to PRIAS criteria were propensity score matched 1:1 to patients aged <65 years.• Patient characteristics were compared with chi-square, Kruskal-Wallis, and one-way ANOVA tests. Predictors of RP pathological upgrading or upstaging were analysed using logistic regression.• Recurrence-free survival (RFS) and overall survival (OS) were calculated using the Kaplan-Meier method. Predictors of RFS were analysed within Cox regression models. Results• Pathological upgrading and upstaging were significantly higher among older (≥65 years) vs younger (<65 years) patients (53.1% vs 44.1% and 12.2% vs 7.2%, respectively). • Higher prostate-specific antigen levels and increasing age were independent predictors of upgrading among patients aged <65 years.• There were no differences in RFS or OS between the two age groups.• Positive surgical margin status was the only independent predictor of shorter RFS. Conclusions• Patients aged ≥65 years who are eligible for AS by PRIAS criteria have a higher risk of being upgraded and upstaged at RP than those aged <65 years. • These findings should be taken into consideration when discussing treatment options for patients diagnosed with prostate cancer.

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Section: Introductionmentioning

confidence: 99%

Higher rates of upgrading and upstaging in older patients undergoing radical prostatectomy and qualifying for active surveillance

et al. 2014

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“…According to Kevin R. Rice, et al, for low risk PCa in men ≥70 years of age, no significance was found in the comparison between RP and RT. 11 Although baseline characteristics are different in the two groups, interestingly, the results from locally weighed regression (IPTW and SMRW models) suggested variations between the two therapies had little effect on the clinical outcome. For lack of any published or ongoing randomized controlled trial testing the choice of curing cT2 PCa for men aged over 75 years, our observational study could provide important reference to the decision-maker.…”

Section: Discussionmentioning

confidence: 97%

“…Two observational studies discussing the benefit of local treatment (RP or RT) for men ≥70 years of age of low risk and for men ≥75 years of age were performed. 11,12 Despite adjustment for covariates to control measured confounders, there remains residual confounding comparing RT with RP. In view of this, we aimed to provide risk estimates to inform contemporary treatment decision for PCa men aged ≥75 years.…”

Section: Introductionmentioning

confidence: 99%

“…In consideration of worse physical conditions, advanced stage and higher pathological grade compared with the younger, the outcome of RP or RT among old people may differ from others. Two observational studies discussing the benefit of local treatment (RP or RT) for men ≥70 years of age of low risk and for men ≥75 years of age were performed . Despite adjustment for covariates to control measured confounders, there remains residual confounding comparing RT with RP.…”

Section: Introductionmentioning

confidence: 99%

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How to choose proper local treatment in men aged ≥75 years with cT2 localized prostate cancer?

Jin

Qiu

et al. 2019

Cancer Medicine

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Background For localized prostate cancer (PCa), radical prostatectomy (RP) and radiotherapy (RT) are two standard interventions to decrease PCa mortality. Contemporary studies contained the elderly people; analyses focusing on patients over 75 years of age were still lacking. Method In the Surveillance Epidemiology and End Results (SEER) database (2004‐2015), people over 75 years of age with cT2 stage were selected in our research. Multivariable Cox proportional hazard models were used to analyze cancer‐specific mortality (CSM) and overall mortality (OM) after adjustment. The propensity score matching was performed to assume the randomization. An instrument variate (IVA) was used to calculate the unmeasured confounders. Results Radical prostatectomy is superior to RT in OM and CSM after adjustment for covariates (HR = 0.54, 95% CI = 0.47‐0.62, P < 0.001 and HR = 0.30, 95% CI = 0.20‐0.45, P < 0.001, respectively). The cox model after matching indicated similar consequence (OM: HR = 0.53, 95% CI = 0.46‐0.62, P < 0.001; CSM: HR = 0.27, 95% CI = 0.17‐0.43, P < 0.001). In the IVA‐adjusted model, the effect of treatment changed slightly (OM: HR = 0.65, 95% CI = 0.54‐0.78, P < 0.001; CSM: HR = 0.21, 95% CI = 0.12‐0.37, P < 0.001). Subgroup analyses showed that for patients with GS = 7, those received RP obtained the highest risk decline for overall death (HR = 0.41, 95% CI = 0.32‐0.52); and for patients with younger age, those received RP obtained the highest risk decline for CSM (HR = 0.11, 95% CI = 0.01‐0.52). Conclusion Patients over 75 years of age with cT2 stage will obtain more benefit from RP compared with RT, especially for patients with GS = 7 and younger age.

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“…Recently, the Support Vector Machine (SVM) [1,2] is a common method in stock price predictions. However, as the stock market is affected by many factors, i.e.…”

Section: Introductionmentioning

confidence: 99%

Indonesian Stock Prediction using Support Vector Machine (SVM)

2018

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Abstract. This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence) or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK) stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data) and the remainder (20 %) was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

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Low risk prostate cancer in men > 70 years old: to treat or not to treat

Cited by 7 publications

References 0 publications

Higher rates of upgrading and upstaging in older patients undergoing radical prostatectomy and qualifying for active surveillance

Higher rates of upgrading and upstaging in older patients undergoing radical prostatectomy and qualifying for active surveillance

How to choose proper local treatment in men aged ≥75 years with cT2 localized prostate cancer?

Indonesian Stock Prediction using Support Vector Machine (SVM)

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