PREDICT: model for prediction of survival in localized prostate cancer

Kerkmeijer, Linda G.W.; Monninkhof, Evelyn M.; Oort, Inge M. van; Poel, Henk G. van der; Meerleer, Gert De; Vulpen, Marco van

doi:10.1007/s00345-015-1691-4

Cited by 9 publications

(10 citation statements)

References 32 publications

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“…Sixteen additional studies were identified by reviewing the references of excluded but relevant studies. A total of 12 studies were eligible for inclusion in the final review 21–32. Two of these had not been summarised in previous reviews 26 27.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Resultsmentioning

confidence: 99%

“…A total of 12 studies were eligible for inclusion in the final review 21–32. Two of these had not been summarised in previous reviews 26 27. The PRISMA flow diagram is shown in figure 1, including the reasons for exclusion at full-text screening.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2019

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“…Although these extensions add granularity, they remain too heterogeneous for modern individualised medicine approaches. More recent attempts at developing survival models have focussed solely on men undergoing radical treatment, and have not been appropriately validated [ 11 , 12 ]. The inadequacies of existing models are evident by the fact that the American Joint Committee on Cancer (AJCC) has not endorsed a single prognostic model for nonmetastatic PCa [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model

et al. 2019

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BackgroundPrognostic stratification is the cornerstone of management in nonmetastatic prostate cancer (PCa). However, existing prognostic models are inadequate—often using treatment outcomes rather than survival, stratifying by broad heterogeneous groups and using heavily treated cohorts. To address this unmet need, we developed an individualised prognostic model that contextualises PCa-specific mortality (PCSM) against other cause mortality, and estimates the impact of treatment on survival.Methods and findingsUsing records from the United Kingdom National Cancer Registration and Analysis Service (NCRAS), data were collated for 10,089 men diagnosed with nonmetastatic PCa between 2000 and 2010 in Eastern England. Median follow-up was 9.8 years with 3,829 deaths (1,202 PCa specific). Totals of 19.8%, 14.1%, 34.6%, and 31.5% of men underwent conservative management, prostatectomy, radiotherapy (RT), and androgen deprivation monotherapy, respectively. A total of 2,546 men diagnosed in Singapore over a similar time period represented an external validation cohort. Data were randomly split 70:30 into model development and validation cohorts. Fifteen-year PCSM and non-PCa mortality (NPCM) were explored using separate multivariable Cox models within a competing risks framework. Fractional polynomials (FPs) were utilised to fit continuous variables and baseline hazards. Model accuracy was assessed by discrimination and calibration using the Harrell C-index and chi-squared goodness of fit, respectively, within both validation cohorts. A multivariable model estimating individualised 10- and 15-year survival outcomes was constructed combining age, prostate-specific antigen (PSA), histological grade, biopsy core involvement, stage, and primary treatment, which were each independent prognostic factors for PCSM, and age and comorbidity, which were prognostic for NPCM. The model demonstrated good discrimination, with a C-index of 0.84 (95% CI: 0.82–0.86) and 0.84 (95% CI: 0.80–0.87) for 15-year PCSM in the UK and Singapore validation cohorts, respectively, comparing favourably to international risk-stratification criteria. Discrimination was maintained for overall mortality, with C-index 0.77 (95% CI: 0.75–0.78) and 0.76 (95% CI: 0.73–0.78). The model was well calibrated with no significant difference between predicted and observed PCa-specific (p = 0.19) or overall deaths (p = 0.43) in the UK cohort. Key study limitations were a relatively small external validation cohort, an inability to account for delayed changes to treatment beyond 12 months, and an absence of tumour-stage subclassifications.Conclusions‘PREDICT Prostate’ is an individualised multivariable PCa prognostic model built from baseline diagnostic information and the first to our knowledge that models potential treatment benefits on overall survival. Prognostic power is high despite using only routinely collected clinicopathological information.

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“…Ten-year estimates of prostate cancerspecific mortality have consistently been less than 10%, for both men with lowrisk disease treated with dose-escalated radiation therapy (RT) (1-3) and men with higher-risk disease treated with RT in conjunction with androgen deprivation therapy (ADT) (2-5). Since the rate of death due to prostate cancer is relatively low, the overall risk of death tends to be driven by competing risks (6)(7)(8) (24), and the use of ADT (2-5), owing to past research that showed an effect of these factors on survival.…”

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confidence: 99%

CT Measures of Bone Mineral Density and Muscle Mass Can Be Used to Predict Noncancer Death in Men with Prostate Cancer

McDonald¹,

Swain

Mayhew³

et al. 2017

Radiology

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Purpose To determine if computed tomographic (CT) metrics of bone mineral density and muscle mass can improve the prediction of noncancer death in men with localized prostate cancer. Materials and Methods Institutional review board approval was obtained, with waiver of informed consent. All patients who underwent radiation therapy for localized prostate cancer between 2001 and 2012 with height, weight, and past medical history documented and who underwent CT that included the L4-5 vertebral interspace were included. On a single axial CT section obtained at the mid-L5 level, the mean CT attenuation of the trabecular bone of the L5 vertebral body (L5) was measured. The height-normalized psoas cross-sectional area (Psoas) was measured on a single CT section obtained at the L4-5 vertebral interface. Multivariable Cox proportional hazards models were used to assess effects on noncancer death. By using parameter estimates from an adjusted model, a prognostic index for prediction of noncancer death was generated and compared with age-adjusted Charlson Comorbidity Index (CCI) by using the Harrell c statistic. Results Six hundred fifty-three men met the inclusion criteria. Prostate cancer risk grouping, androgen deprivation, race, age-adjusted CCI, L5, and Psoas were included in a multivariable model. Age-adjusted CCI (hazard ratio [HR] = 1.36, P < .001), L5 (HR = 2.88 for L5 < 105 HU, HR = 1.42 for 105 HU ≤ L5 ≤ 150 HU, P < .001), Psoas (HR = 1.95 for Psoas < 7.5 cm/m, P = .003), and race (HR = 1.68 for African American race, HR = 1.77 for other nonwhite race, P = .019) were independent predictors of noncancer death. The prognostic index yielded a c value of 0.747 for the prediction of noncancer death versus 0.718 for age-adjusted CCI alone. Conclusion L5 and Psoas, which are surrogates for bone mineral density and muscle mass, respectively, were independent predictors of noncancer death. The prognostic index that incorporated these measures with the CCI was associated with improved accuracy for prediction of noncancer death. RSNA, 2016 Online supplemental material is available for this article.

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PREDICT: model for prediction of survival in localized prostate cancer

Cited by 9 publications

References 32 publications

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

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

Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model

CT Measures of Bone Mineral Density and Muscle Mass Can Be Used to Predict Noncancer Death in Men with Prostate Cancer

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