Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review

Abstract

ObjectivesMen diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models.DesignSystematic literature review, pre-specified and registered on PROSPERO (CRD42018086394).Data sourcesMEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms.Eligibility criteriaInclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis.Data extraction and synthesisTitle, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool.ResultsDatabase searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available.ConclusionFew long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.

Modified J‐CAPRA scoring system in predicting treatment outcomes of metastatic prostate cancer patients undergoing androgen deprivation therapy

“…Standard risk stratification schemata apply for localised disease only. 14,15 The J-CAPRA score estimates risk of progression-free survival and prostate cancer-specific mortality for locally advanced and metastatic prostate cancer patients undergoing ADT. Validation of J-CAPRA score has been performed in numerous academic institutions worldwide.…”

“…With high burden of advanced disease, risk stratification of the M‐CaP population meets unusual challenges. Standard risk stratification schemata apply for localised disease only 14,15 . The J‐CAPRA score estimates risk of progression‐free survival and prostate cancer‐specific mortality for locally advanced and metastatic prostate cancer patients undergoing ADT.…”

<p>Nomogram for the Individualized Prediction of Survival Among Patients with H7N9 Infection</p>

“…10 In addition, a nomogram can accurately predict and estimate the individualized risk of a disease and quantitatively demonstrate a personalized probability for predicting the incidence of disease outcome. 11 To better evaluate the predictors associated with survival outcomes, based on a matched case−control study (1:2 ratios), we retrospectively investigated the sociodemographic, clinical and epidemiological data of 227 laboratoryconfirmed cases of H7N9 VI in Zhejiang province, China, between March 1, 2013, andMay 31, 2019. The objective of this study is to determine predictors for survival for H7N9 patients and create a practical instrument based on results obtained from a multivariable Cox proportional hazard model for use in predicting and evaluating the individualized survival probability in H7N9 patients.…”

Impregnation of Curcumin into a Biodegradable (Poly-lactic-co-glycolic acid, PLGA) Support, to Transfer Its Well Known In Vitro Effect to an In Vivo Prostate Cancer Model

“…Prostate cancer (PCa) is the second largest cancer histotype and the fifth leading cause of cancer-associated deaths in older men worldwide [1,2]. There is evidence to suggest a close relationship between oxidative stress, inflammation, and risk of progressive PCa [3,4].…”