Case-based prediction of survival in colorectal cancer patients

OBJECTIVE: To develop an approach to the prediction of survival in patients with colorectal cancer using nearest neighbor analysis and case-based reas oning. STUDY DESIGN: A total of 216 patients with full clinicopathologic records a nd five-year follow-up were the subjects of this study. They were divided i nto It cove database of 162 cases and a test group of 54 cases, with follow up on all patients. When the patient was still alive at the end of the fol low-up period, censored survival time was used. For each of the test cases, the four closest neighbors from the database were retrieved and their medi an survival time recorded and used as the predicted estimate of survival. C ase matching was based on a Euclidean multivariate distance measure for the three best predictor variables: patient age, Dukes stage and tubule config uration . Cases with the smallest distance from the test case were consider ed to be the most similar. The predicted survival times for the test cases were compared with the actual, observed survival in the test cases to deter mine the success of this approach. RESULTS: The results showed reasonable concordance between observed and pre dicted survival figures, although there was a large degree of spread. Class ification of cases into less than or equal to 60 and > 60 months' survival showed a correct classification rate of 63%. For the prediction of survival time, the distribution Of differences between observed and predicted survi val times for the uncensored test cases had a median value of-5 months but also showed a wide dispersion of values. Correlation of observed and predic ted survival times, while not reaching statistical significance at P < .05, did show a strong positive association. CONCLUSION: Case-based approaches to the prediction of survival times in ca ncer patients are important. The results of the current study illustrate th e difficulties in applying this approach to survival data and highlight the complexity of patient information and the inability to accurately predict patient outcome on 17 small subset of clinicopathologic features. While ext ensive work needs to be carried out to improve prediction power, this study illustrates the potential for case-based analyses. The ability to retrieve feature-matched cases from hospital patient databases has clear, independe nt advantages in patient management, but the ability to provide reliable, t argeted prognostic estimates on individual cases should be a common goal in medical research.