-
Observational Study
The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.
- Hoong-Seam Wong, Shridevi Subramaniam, Zarifah Alias, Nur Aishah Taib, Gwo-Fuang Ho, Char-Hong Ng, Cheng-Har Yip, Helena M Verkooijen, Mikael Hartman, and Nirmala Bhoo-Pathy.
- From the National Clinical Research Centre (HSW, SS), Level 3, Dermatology Block, Kuala Lumpur Hospital, Jalan Pahang; Department of Surgery (ZA, NAT, CHN, CHY); Department of Oncology (GFH), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Imaging Division (HMV), University Medical Center Utrecht, Utrecht, The Netherlands; Saw Swee Hock School of Public Health (HMV, MH), National University of Singapore; Department of Surgery (MH), Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore; Julius Centre University of Malaya (NBP), Centre for Clinical Epidemiology and Evidence-Based Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; and Julius Center for Health Sciences and Primary Care (NBP), University Medical Center Utrecht, Utrecht, The Netherlands.
- Medicine (Baltimore). 2015 Feb 1; 94 (8): e593e593.
AbstractWeb-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.
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