A non-conformity approach towards post-prostatectomy metastasis estimation using a multicentre prostate cancer database

Christos Chatzichristos, Jose-Felipe Golib-Dzib, Andries Clinckaert, Wouter Everaerts, Maarten De Vos, Martine Lewi
Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR 152:266-285, 2021.

Abstract

Prostate cancer is among the most common type of cancer in men worldwide. Despite the use of clinical indicators, as part of simple rule-based strategies, stratifying patients diagnosed with prostate cancer into risk groups to reliably reflect oncological prognosis remains challenging. Machine Learning (ML) offers the possibility to develop estimation models based on routinely evaluated patient or tumor characteristics. In the present study, the estimation of metastasis in prostate patients after primary treatments (radical prostatectomy) with the aid of Support Vector Machines (SVMs) and Conformal Predictors (CP) was evaluated. We show that the use of ML models can complement classical statistical approaches. Moreover, the application of CP, on top of an underlying ML model, renders a probabilistic outcome that combines the simplicity of a clinical indicator with the precision of a ML approach. The TriNetX Research Network, an electronic health records database with datasets from several United States health care organizations, was used in this study. This approach can be further adapted to support clinical decision making in prostate and other types of cancer.

Cite this Paper

BibTeX
@InProceedings{pmlr-v152-chatzichristos21a, title = {A non-conformity approach towards post-prostatectomy metastasis estimation using a multicentre prostate cancer database}, author = {Chatzichristos, Christos and Golib-Dzib, Jose-Felipe and Clinckaert, Andries and Everaerts, Wouter and De Vos, Maarten and Lewi, Martine}, booktitle = {Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications}, pages = {266--285}, year = {2021}, editor = {Carlsson, Lars and Luo, Zhiyuan and Cherubin, Giovanni and An Nguyen, Khuong}, volume = {152}, series = {Proceedings of Machine Learning Research}, month = {08--10 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v152/chatzichristos21a/chatzichristos21a.pdf}, url = {https://proceedings.mlr.press/v152/chatzichristos21a.html}, abstract = {Prostate cancer is among the most common type of cancer in men worldwide. Despite the use of clinical indicators, as part of simple rule-based strategies, stratifying patients diagnosed with prostate cancer into risk groups to reliably reflect oncological prognosis remains challenging. Machine Learning (ML) offers the possibility to develop estimation models based on routinely evaluated patient or tumor characteristics. In the present study, the estimation of metastasis in prostate patients after primary treatments (radical prostatectomy) with the aid of Support Vector Machines (SVMs) and Conformal Predictors (CP) was evaluated. We show that the use of ML models can complement classical statistical approaches. Moreover, the application of CP, on top of an underlying ML model, renders a probabilistic outcome that combines the simplicity of a clinical indicator with the precision of a ML approach. The TriNetX Research Network, an electronic health records database with datasets from several United States health care organizations, was used in this study. This approach can be further adapted to support clinical decision making in prostate and other types of cancer.} }
Endnote
%0 Conference Paper %T A non-conformity approach towards post-prostatectomy metastasis estimation using a multicentre prostate cancer database %A Christos Chatzichristos %A Jose-Felipe Golib-Dzib %A Andries Clinckaert %A Wouter Everaerts %A Maarten De Vos %A Martine Lewi %B Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2021 %E Lars Carlsson %E Zhiyuan Luo %E Giovanni Cherubin %E Khuong An Nguyen %F pmlr-v152-chatzichristos21a %I PMLR %P 266--285 %U https://proceedings.mlr.press/v152/chatzichristos21a.html %V 152 %X Prostate cancer is among the most common type of cancer in men worldwide. Despite the use of clinical indicators, as part of simple rule-based strategies, stratifying patients diagnosed with prostate cancer into risk groups to reliably reflect oncological prognosis remains challenging. Machine Learning (ML) offers the possibility to develop estimation models based on routinely evaluated patient or tumor characteristics. In the present study, the estimation of metastasis in prostate patients after primary treatments (radical prostatectomy) with the aid of Support Vector Machines (SVMs) and Conformal Predictors (CP) was evaluated. We show that the use of ML models can complement classical statistical approaches. Moreover, the application of CP, on top of an underlying ML model, renders a probabilistic outcome that combines the simplicity of a clinical indicator with the precision of a ML approach. The TriNetX Research Network, an electronic health records database with datasets from several United States health care organizations, was used in this study. This approach can be further adapted to support clinical decision making in prostate and other types of cancer.
APA
Chatzichristos, C., Golib-Dzib, J., Clinckaert, A., Everaerts, W., De Vos, M. & Lewi, M.. (2021). A non-conformity approach towards post-prostatectomy metastasis estimation using a multicentre prostate cancer database. Proceedings of the Tenth Symposium on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 152:266-285 Available from https://proceedings.mlr.press/v152/chatzichristos21a.html.

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