title: The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer.
abstract: Web-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.
authors.affiliation: 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.
authors.forename: Hoong-Seam
authors.initials: HS
authors.lastname: Wong
authors.forename: Shridevi
authors.initials: S
authors.lastname: Subramaniam
authors.forename: Zarifah
authors.initials: Z
authors.lastname: Alias
authors.forename: Nur Aishah
authors.initials: NA
authors.lastname: Taib
authors.forename: Gwo-Fuang
authors.initials: GF
authors.lastname: Ho
authors.forename: Char-Hong
authors.initials: CH
authors.lastname: Ng
authors.forename: Cheng-Har
authors.initials: CH
authors.lastname: Yip
authors.forename: Helena M
authors.initials: HM
authors.lastname: Verkooijen
authors.forename: Mikael
authors.initials: M
authors.lastname: Hartman
authors.forename: Nirmala
authors.initials: N
authors.lastname: Bhoo-Pathy
mesh_terms: D000328:Adult; D000368:Aged; D044466:Asian People / Q000706:statistics & numerical data; D001943:Breast Neoplasms / Q000401:mortality* / Q000628:therapy; D015331:Cohort Studies; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008296:Malaysia / Q000453:epidemiology; D008875:Middle Aged