"Clinicians want prognostic tools that not only aid prognostic classification, but also give quantitative probabilities of survival. We describe a way of generating survival estimates that uses existing survival data and generates survival curves online dynamically.

On the website http://finprog.primed.info a selection of prognostic factors are available for case-match survival estimation... The default selection in the drop down list for each factor is “all,” which means that no selection has been made for the specific factor. The user can enter a prognostic factor profile by selecting any of the categories in the drop down lists. The software then queries the database to retrieve data on patients with matching prognostic profiles and known outcome and calculates a survival curve according to the Kaplan-Meier product-limit method using the actual survival data of all matching patients. The number of patients at risk, the confidence intervals for the Kaplan-Meier estimates, and the median survival time are also displayed. The user can compare two factor profiles by clicking the “two profiles” option. The distribution of patients according to vital status, therapy received, or a specific prognostic factor can also be displayed as a table or a chart (figure). The website also contains basic information about survival statistics and the prognostic factors, including guidelines for selecting variables and interpreting the results. ...

" A website has been published which allows the user to enter information on prognostic factors for a patient with breast cancer, and instantly obtain a survival curve based on out-come data of prior cases with a matching prognostic factor profile. The source for the survival data is a Finnish nation-wide series (the FinProg series) of 2,842 women diagnosed with breast cancer in 1991-2. The purpose of this study was to compare survival estimates based on the FinProg database, with estimates for breast cancer patients from the US, ob-tained from the SEER public-use database and the same time-period. Results show that a reasonable level of agreement between estimates can be reached, by the use of large, unse-lected databases, and that significantly different estimates were obtained in only 2 of 19 analyzed prognostic profiles. The current system could be used to share important knowl-edge on outcome between researchers and clinicians at differ-ent institutions, and be used in the decision-making process concerning treatment of patients with breast cancer. "

" OBJECTIVES: To evaluate the accuracy of an internet-based method for survival estimation in breast cancer. DESIGN: A website was created which allows the user to enter information on prognostic factors for a patient, and instantly obtain a Kaplan-Meler survival curve based on outcome data of prior cases with a matching prognostic factor profile. The source for the survival data is a Finnish nationwide series of 2,842 women diagnosed with breast cancer in 1991-2, comprising 91% of all cases within the selected geographical regions during these two years. MAIN OUTCOME MEASURES: The accuracy of the survival estimates obtained using data from the nationwide series was assessed in an independent, single institution validation series (n = 565), and measured by analysis of calibration and discrimination (the area under the ROC curve). RESULTS: A selection of prognostic factors recommended by the National Institutes of Health (NIH) Consensus Development Panel and the International Consensus Panel on the Treatment of Primary Breast Cancer were made available for case-matching on the website. Kaplan-Meier case-match eight-year estimates of distant disease-free survival (DDFS) based on combinations of tumour size, histologic grade and mode of detection (screen-detected vs. symptomatic) were close to the actual outcomes, e.g. patients in the validation set who were estimated to have a 71-80%, 81-90% and 96-100% DDFS had an actual average DDFS of 76%, 88%, and 100%, respectively. CONCLUSIONS: A web-based case-match system can generate survival curves for user-defined prognostic factor combinations and identify patients with a varying risk for breast cancer recurrence. The system can be linked to other data sets, expanded to accommodate new prognostic factors and used as a source for population-based survival estimates. "