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Prediction model for prostate cancer

by ,
Nov. 16, 2020

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The aim of this project is to create a prediction model for survival from prostate cancer, based on cancer registry data from at least two geographies, starting in Taiwan and the Netherlands. Prediction models can help to improve patient care and have successfully been developed and deployed in clinical practice. Developing a good prognostic model requires data from many patients. Therefore, data from international cancer registries is combined.

This second joint project between the Taiwan Cancer Registry (TCR) and IKNL focusses on developing a prediction model for prostate cancer. Developing a good prognostic model requires data from many patients. Unlike in the Netherlands, prostate cancer in Taiwan is rare (<5500 cases/year). To serve the Taiwanese patient with a well performing prediction model, it may help to combine data from the cancer registries in Taiwan and the Netherlands. Regulations prevent sharing of patient-record data between Taiwan and the Netherlands. Vantage6 allows for development of prediction models on data distributed in a privacy preserving way.

Contacts

Wen-Chung Lee MD PhD

National Taiwan University, TCR

Jing-Rong Jhuang

PhD student (National Taiwan University, TCR)

References

  1. Lu CL, Wang S, Ji Z, Wu Y, Xiong L, Jiang X, Ohno-Machado L. WebDISCO: a web service for distributed cox model learning without patient-level data sharing. J Am Med Inform. Assoc. 2015;22:1212–1219. doi:10.1093/jamia/ocv083
  2. Lowrance WT, Scardino PT. Predictive models for newly diagnosed prostate cancer patients. Rev Urol. 2009;11(3):117–126.
  3. Kerkmeijer LG, Monninkhof EM, van Oort IM, van der Poel HG, de Meerleer G, van Vulpen M. PREDICT: model for prediction of survival in localized prostate cancer. World J Urol. 2016;34(6):789–795. doi:10.1007/s00345-015-1691-4
  4. Huang CC, Chan SY, Lee WC, Chiang CJ, Lu TP, Cheng HC. Development of a prediction model for breast cancer based on the national cancer registry in Taiwan. Breast Cancer Res. 2019;21:92. doi:10.1186/s13058-019-1172-6
  5. Keogh RH, Seaman SR, Barrett JK, Taylor-Robinson D, Szczesniak R. Dynamic Prediction of Survival in Cystic Fibrosis: A Landmarking Analysis Using UK Patient Registry Data. Epidemiology. 2019;30:29–37. doi:10.1097/EDE.0000000000000920

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