TP8.1.7 The utility of a timeline and episode structured breast cancer data system to study outcomes following neoadjuvant chemotherapy for cases stratified by HER2 status
Author(s) -
Fatima Rahman,
Ellen Copson,
Alan Hales,
David A. Rew
Publication year - 2021
Publication title -
british journal of surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.202
H-Index - 201
eISSN - 1365-2168
pISSN - 0007-1323
DOI - 10.1093/bjs/znab362.069
Subject(s) - medicine , timeline , breast cancer , oncology , medical record , cohort , trastuzumab , cancer , disease , cancer registry , archaeology , history
Background Breast neoplasia displays complex patterns of whole-of-life disease progression, which are difficult to study using legacy data systems. Our timeline- and episode-structured breast cancer data set of 20,000 records allows direct visualisation of the entire documentary record of every patient. The embedded data mining module permits research into a wide range of patient cohorts by pathology, treatment and outcome. Methods We selected the cohort of patients aged between 15 and 75 with HER-2 –ve and HER-2 +ve breast cancer who were treated with neoadjuvant chemotherapy (NAC), with or without anti-HER2 therapy between 2002 and 2019. We also studied the patterns and time intervals (in months) of disease progression and response to treatment from primary diagnosis, through loco-regional recurrence and distant metastasis to final outcome. Results Of 301 women with confirmed early stage breast cancer were treated with NAC over that time, 186 had HER2- and 115 had HER2+ tumours. The patterns and intervals of disease progression, as displayed on the Master Lifetrack, were mapped and measured for every patient. The proportions of patients with Her2+ve tumours receiving trastuzumab and analogues, and the tumour responses to treatment, were audited. The underlying data set was validated by review of the original records. Conclusions The whole-of-life timeline structured cancer data system introduces a new direction for clinical data visualisation, record management and user utility in surgical practice. This study validates the model as a tool for the better understanding of treatment effects and longitudinal behaviours in any selected range of cancer phenotypes.
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