Utility of a web-based breast cancer predictive algorithm for adjuvant chemotherapeutic decision making in a multidisciplinary oncology center.
OBJECTIVE
Adjuvant drug therapy can extend survival for breast cancer patients, but the balance between costs and benefits may be difficult to estimate. Software programs have been developed for this purpose and recently have become available online. Here, we describe our experience using a web-based program to support adjuvant decision making at a multidisciplinary breast cancer Tumor Board in a university-affiliated oncology center.


METHODS
One hundred two adjuvant breast cancer cases were discussed by the Tumor Board over a four-month period, with a provisional treatment plan being formulated after each discussion. Program data predicting 10-year risks and benefits were shared with board members after each provisional plan and any change in recommendation was recorded. A user survey was conducted to assess the perceived strengths and weaknesses of the program.


RESULTS
Treatment decisions were changed in 12.7 percent of cases (13/102) after consideration of data from the program. Most of these (76.9 percent) were node-negative ER-positive cases, with the most common reason for change being a lower-than-expected added survival benefit from less intensive chemotherapy regimens (ACx4 or CMF; 81.8 percent). In certain recurrent scenarios, the program was perceived to have limitations that led to retention of the original management plan despite data that might otherwise have favored different treatment. On completion of the study period, clinicians' attitudes to the program ranged from enthusiasm to caution.


CONCLUSIONS
Although not replacing clinical judgement, these findings support the value of this web-based program as a decision making adjunct that can help clinicians to separate risk and benefit, compare the added value of different therapeutic interventions in a given clinical context, and present more balanced information about treatment options to patients.
Epstein
Richard J
RJ
Department of Medicine, The University of Hong Kong, Hong Kong. repstein@hku.hk
Leung
Thomas W
TW
Mak
Joyce
J
Cheung
Polly S
PS
D000328:Adult; D000368:Aged; D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy*; D017024:Chemotherapy, Adjuvant; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D020407:Internet; D008875:Middle Aged; D012984:Software
D000970:Antineoplastic Agents