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

Abstract

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.

Individualized Prediction of Breast Cancer Survival Using Flexible Parametric Survival Modeling: Analysis of a Hospital-Based National Clinical Cancer Registry

“…Furthermore, Adjuvant! and PREDICT, the most well-known prognostic models, performed less accurately in patients from Asian countries, including Malaysia [ 16 , 18 ], South Korea [ 17 ], Taiwan [ 15 ] and Thailand [ 20 ]. The differences in the case-mix and regression coefficients both account for the drop in external performance.…”

“…[ 10 ], BC Nomogram [ 11 ], OPTIONS [ 12 ], CancerMath [ 13 ] and PREDICT [ 14 ]. However, all of these models were originally developed from the populations in Western countries, and some of the models were proven to provide less accurate predictions during validation in other populations, including Asian ones [ 15 , 16 , 17 , 18 , 19 , 20 ]. There was one study that externally validated Western prognostic models in Thailand [ 20 ].…”

“…However, all of these models were originally developed from the populations in Western countries, and some of the models were proven to provide less accurate predictions during validation in other populations, including Asian ones [ 15 , 16 , 17 , 18 , 19 , 20 ]. There was one study that externally validated Western prognostic models in Thailand [ 20 ]. This external validation study concluded that Western models were less accurate in Thai breast cancer patients.…”

A scoping review of interactive and personalized web-based clinical tools to support treatment decision making in breast cancer

“…Moreover, its output is only presented in text form and the other formats including curves, bar charts, pie charts, and pictograms are unavailable. Both ‘PREDICT’ [ 56 ] and ‘CancerMath, Therapy Calculator’ [ 57 ] have been externally validated in various populations [ [58] , [59] , [60] , [71] , [72] , [73] , [74] , [75] ]. A study performed by Karapanagiotis et al.…”

Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

“…The CM performances have been validated in different studies [ 30 , 31 , 32 ], but only in [ 31 ] did the authors consider the package related to the lymph node involvement probability computation on a sample of the population of the South-Eastern Asia. Specifically, in [ 31 ], the authors highlight that the median probability estimated by CM of having a lymph node involvement is equal to 40.6%, underestimating the real one of the samples under study (43.6%).…”