Literature DB >> 16777688

Utility of a web-based breast cancer predictive algorithm for adjuvant chemotherapeutic decision making in a multidisciplinary oncology center.

Richard J Epstein1, Thomas W Leung, Joyce Mak, Polly S Cheung.   

Abstract

PURPOSE: 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. PATIENTS AND 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.
CONCLUSION: 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.

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Year:  2006        PMID: 16777688     DOI: 10.1080/07357900600705292

Source DB:  PubMed          Journal:  Cancer Invest        ISSN: 0735-7907            Impact factor:   2.176


  3 in total

Review 1.  Multidisciplinary care of breast cancer patients: a scoping review of multidisciplinary styles, processes, and outcomes.

Authors:  J Shao; M Rodrigues; A L Corter; N N Baxter
Journal:  Curr Oncol       Date:  2019-06-01       Impact factor: 3.677

2.  Factors influencing treatment recommendations in node-negative breast cancer.

Authors:  Elisabeth Edstrom Elder; Sally Baron Hay; Katrina Moore
Journal:  J Oncol Pract       Date:  2011-01       Impact factor: 3.840

3.  Validation of the CancerMath prognostic tool for breast cancer in Southeast Asia.

Authors:  Hui Miao; Mikael Hartman; Helena M Verkooijen; Nur Aishah Taib; Hoong-Seam Wong; Shridevi Subramaniam; Cheng-Har Yip; Ern-Yu Tan; Patrick Chan; Soo-Chin Lee; Nirmala Bhoo-Pathy
Journal:  BMC Cancer       Date:  2016-10-21       Impact factor: 4.430

  3 in total

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