Improving disease prognostics / prognostication
Individualised prostate cancer management has been severely hampered by a lack of accurate models of survival. Work in Cambridge has developed novel prognostic models to address this and revolutionise how prostate cancer is managed.
Predict prostate for individualised prognostics assessment and the benefit of treatment on survival in early prostate cancer
Prostate cancer incidence is rising in the UK and worldwide with >1.5 million new diagnosis made in the western world alone. This incidence is projected to rise by 69% by 2030.
Over 80% of men will present with non-metastatic disease with more than half classified as low or intermediate-risk using traditional criteria. Trial evidence has shown that many of these men will not gain any survival benefit from immediate radical therapy (surgery or radiotherapy). There is however a huge variance from centre to centre and even within centres in how men are counselled about the need for treatment.
PREDICT: Prostate (https://prostate.predict.nhs.uk) was conceived to address this critical gap and to better inform patients and standardise the decision-making process. Using data from over 12.000 men in 2 international cohorts, we produced a model that was able to contextualise the relative prostate cancer specific and overall risk of mortality for an individual and allow modelling of the potential benefit of treatment on these outcomes (1). It is built around long-term actual survival data and uses well validated statistical modelling.
A short patient focused video of its use and application can be seen opposite:
Since its launch PREDICT: Prostate has been accessed more than 25,000 times from over 110 countries worldwide.
In clinical impact studies we have shown that use of the tool leads to a 30% reduction in the likelihood of clinicians recommending unnecessary treatment (2).
Patients’ perception of the tool is also overwhelmingly positive with 90% finding it useful and would recommend it to others.
The model has been endorsed by National Institute for Health and Clinical excellence (NICE) to help with decision making recognising that it supports a number of their recommendations and new guidelines (NG131).
It is the only resource endorsed by NICE in prostate cancer (www.nice.org.uk/guidance/ng131/resources). A patient benefit trials has also recently concluded and will report soon (http://www.isrctn.com/ISRCTN28468474)
Publications to date
- Thurtle DR, Greenberg DC, Lee LS, Huang HH, Pharoah PD, Gnanapragasam VJ. Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model. PLoS Med. 2019 Mar 12;16(3):e1002758
- Thurtle DR, Jenkins V, Pharoah PD, Gnanapragasam VJ. Understanding of prognosis in non-metastatic prostate cancer: a randomised comparative study of clinician estimates measured against the PREDICT prostate prognostic model. Br J Cancer. 2019 Oct;121(8):715-718
- Thurtle D, Rossi SH, Berry B, Pharoah P, Gnanapragasam VJ. Models predicting survival to guide treatment decision-making in newly diagnosed primary non-metastatic prostate cancer: a systematic review. BMJ Open. 2019 Jun 22;9(6):e029149. doi: 10.1136/bmjopen-2019-029149
- Thurtle D, Bratt O, Stattin P, Pharoah P, Gnanapragasam V. Comparative performance and external validation of the multivariable PREDICT Prostate tool for non-metastatic prostate cancer: a study in 69,206 men from Prostate Cancer data Base Sweden (PCBaSe). BMC Med. 2020 Jun 16;18(1):139. doi: 10.1186/s12916-020-01606-w. PMID: 32539712; PMCID: PMC7296776
- Lee C, Light A, Alaa A, Thurtle D, van der Schaar M, Gnanapragasam VJ. Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database. Lancet Digit Health. 2021 Mar;3(3):e158-e165. doi: 10.1016/S2589-7500(20)30314-9
- David Thurtle, Val Jenkins, Alex Freeman, Mike Pearson, Gabriel Recchia, PriyaTamer, Kelly Leonard, Paul Pharoah, Jonathan Aning, Sanjeev Madaan, Chee Goh, Serena Hilman, Stuart McCracken, Petre Cristian Ilie, Henry Lazarowicz, Vincent Gnanapragasam. Clinical impact of the Predict Prostate risk communication tool in men newly diagnosed with non-metastatic prostate cancer: a multi-centre randomised controlled trial medRxiv 2021.01.24.21249948; doi:https://doi.org/10.1101/2021.01.24.21249948
The Cambridge Prognostic Groups for improved prognostic categorisation of disease mortality at diagnosis in primary non-metastatic prostate cancer
Over 80% of the nearly 1 million men diagnosed with prostate cancer annually worldwide present with localised or locally advanced non-metastatic disease. Risk stratification is the cornerstone for clinical decision making and treatment selection for these men.
Current stratification systems are based on a simplistic 3-tiered system to classify patients as low, intermediate, or high risk. There is, however, significant heterogeneity in outcomes within these standard groupings and many men get under treated and overtreated because of it.
Innovative work in Cambridge has developed a new prognostic model for newly diagnosed prostate cancer to estimate the risk of prostate cancer death (1-2). The Cambridge Prognostic Group (CPG) strata model, now tested in over 86,000 men has an accuracy of >80% in predicting the likelihood of dying from a new diagnosis of prostate cancer.
Independent studies have shown that the Cambridge Prostate Groups (CPG) system consistently outperforms the UK NICE/EAU/AUA/NCCN risk models in terms of its ability to predict which men will die of their disease (3).
The CPG model has been endorsed by the East of England Cancer Alliance for regional implementation and been used by the UK National Prostate cancer Audit (NPCA) to report on treatment patterns in the UK.
The CPG model has now been built into a webtool with the aim of allowing access to any clinician for use in clinics and treatment planning in the UK and Internationally. You can access the online calculator here.
In 2021 the National Prostate Cancer Audit produced a short report on the value of the CPG system after a publication using the national data showed a more accurate description of treatment patterns and trends. Following this they have decided to incorporate the CPG system into the NCA reporting going forwards
The short report can be read here and blog about this : https://www.npca.org.uk/content/uploads/2021/02/NPCA-Short-Report-2021_Using-the-CPG-in-the-NPCA_Final-11.02.21.pdf
As a direct result of this publication the UK National Institute for Clinical Excellence (NICE) issued a notice of a formal review of their current recommended risk stratification models : https://www.nice.org.uk/guidance/ng131/resources/2021-exceptional-surveillance-of-prostate-cancer-diagnosis-and-management-nice-guideline-ng131-9013193965/chapter/Surveillance-decision?tab=evidence#risk-stratification
Publications to date
- Gnanapragasam VJ, Bratt O, Muir K, Lee LS, Huang HH, Stattin P, Lophatananon A. The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: a validation study. BMC Med. 2018 Feb 28;16(1):31. doi: 10.1186/s12916-018-1019-5
- VJ Gnanapragasam, A Lophatananon, KA Wright, KR Muir, A Gavin, DC Greenberg. Improving Clinical Risk Stratification at Diagnosis in Primary Prostate Cancer: A Prognostic Modelling Study. PLoS Medicine 2016 Aug 2;13(8):e1002063.doi: 10.1371/journal.pmed.1002063
- Zelic R, Garmo H, Zugna D, Stattin P, Richiardi L, Akre O, Pettersson A. Predicting Prostate Cancer Death with Different Pretreatment Risk Stratification Tools: A Head-to-head Comparison in a Nationwide Cohort Study. Eur Urol. 2019 Oct 9. pii: S0302-2838(19)30755-9
- Parry MG, Cowling TE, Sujenthiran A, Nossiter J, Berry B, Cathcart P, Aggarwal A, Payne H, van der Meulen J, Clarke NW, Gnanapragasam VJ. Risk stratification for prostate cancer management: value of the Cambridge Prognostic Group classification for assessing treatment allocation. BMC Med. 2020 May 28;18(1):114. doi: 10.1186/s12916-020-01588-9
- Lee C, Light A, Alaa A, Thurtle D, van der Schaar M, Gnanapragasam VJ. Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database. Lancet Digit Health. 2021 Mar;3(3):e158-e165. doi: 10.1016/S2589-7500(20)30314-9.