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A Comparison Between the Online Prediction Models CancerMath and PREDICT as Prognostic Tools in Thai Breast Cancer Patients

Authors Polchai N, Sa-nguanraksa D, Numprasit W, Thumrongtaradol T, O-charoenrat E, O-charoenrat P

Received 14 April 2020

Accepted for publication 28 June 2020

Published 8 July 2020 Volume 2020:12 Pages 5549—5559

DOI https://doi.org/10.2147/CMAR.S258143

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Ahmet Emre Eskazan


Nuanphan Polchai,1 Doonyapat Sa-nguanraksa,1 Warapan Numprasit,1 Thanawat Thumrongtaradol,1 Eng O-charoenrat,2 Pornchai O-charoenrat1

1Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand; 2Faculty of Medical Sciences, University College London, London, UK

Correspondence: Pornchai O-charoenrat
Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, 2 Wanglang Road Bangkoknoi, Bangkok 10700, Thailand
Tel +66 2419 2837
Email pornchai.och@mahidol.ac.th

Background and Purpose: Web-based prognostic calculators have been developed to inform about the use of adjuvant systemic treatments in breast cancer. CancerMath and PREDICT are two examples of web-based prognostic tools that predict patient survival up to 15 years after an initial diagnosis of breast cancer. The aim of this study is to validate the use of CancerMath and PREDICT as prognostic tools in Thai breast cancer patients.
Patients and Methods: A total of 615 patients who underwent surgical treatment for stage I to III breast cancer from 2003 to 2011 at the Division of Head Neck and Breast Surgery, Department of Surgery, Siriraj Hospital, Mahidol University, Thailand were recruited. A model-predicted overall survival rate (OS) and the actual OS of the patients were compared. The efficacy of the model was evaluated using receiver-operating characteristic (ROC) analysis.
Results: For CancerMath, the predicted 5-year OS was 88.9% and the predicted 10-year OS was 78.3% (p< 0.001). For PREDICT, the predicted 5-year OS was 83.1% and the predicted 10-year OS was 72.0% (p< 0.001). The actual observed 5-year OS was 90.8% and the observed 10-year OS was 82.6% (p< 0.001). CancerMath demonstrated better predictive performance than PREDICT in all subgroups for both 5- and 10-year OS. In addition, there was a marked difference between CancerMath and observed survival rates in patients who were older as well as patients who were stage N3. The area under the ROC curve for 5-year OS in CancerMath and 10-year OS was 0.74 (95% CI; 0.65– 0.82) and 0.75 (95% CI; 0.68– 0.82). In the PREDICT group, the area under the ROC curve for 5-year OS was 0.78 (95% CI; 0.71– 0.85) and for 10-year OS, it was 0.78 (95% CI; 0.71– 0.84).
Conclusion: CancerMath and PREDICT models both underestimated the OS in Thai breast cancer patients. Thus, a novel prognostic model for Thai breast cancer patients is required.

Keywords: breast cancer, predictive model, CancerMath, PREDICT, survival

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