Development and validation of a predictive model for estimating EGFR mutation probabilities in patients with non-squamous non-small cell lung cancer in New Zealand.

Show simple item record Aye, Phyu en Tin Tin, Sandar en McKeage, Mark en Khwaounjoo, Prashannata en Cavadino, Alana en Elwood, James en 2020-09-24T02:30:18Z en 2020-09-24T02:30:18Z en 2020-07-14 en
dc.identifier.citation BMC cancer 20(1):658 14 Jul 2020 en
dc.identifier.issn 1471-2407 en
dc.identifier.uri en
dc.description.abstract BACKGROUND:Targeted treatment with Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) is superior to systemic chemotherapy in non-small cell lung cancer (NSCLC) patients with EGFR gene mutations. Detection of EGFR mutations is a challenge in many patients due to the lack of suitable tumour specimens for molecular testing or for other reasons. EGFR mutations are more common in female, Asian and never smoking NSCLC patients. METHODS:Patients were from a population-based retrospective cohort of 3556 patients diagnosed with non-squamous non-small cell lung cancer in northern New Zealand between 1 Feb 2010 and 31 July 2017. A total of 1694 patients were tested for EGFR mutations, of which information on 1665 patients was available for model development and validation. A multivariable logistic regression model was developed based on 1176 tested patients, and validated in 489 tested patients. Among 1862 patients not tested for EGFR mutations, 129 patients were treated with EGFR-TKIs. Their EGFR mutation probabilities were calculated using the model, and their duration of benefit and overall survival from the start of EGFR-TKI were compared among the three predicted probability groups: < 0.2, 0.2-0.6, and > 0.6. RESULTS:The model has three predictors: sex, ethnicity and smoking status, and is presented as a nomogram to calculate EGFR mutation probabilities. The model performed well in the validation group (AUC = 0.75). The probability cut-point of 0.2 corresponds 68% sensitivity and 78% specificity. The model predictions were related to outcome in a group of TKI-treated patients with no biopsy testing available (n = 129); in subgroups with predicted probabilities of < 0.2, 0.2-0.6, and > 0.6, median overall survival times from starting EGFR-TKI were 4.0, 5.5 and 18.3 months (p = 0.02); and median times remaining on EGFR-TKI treatment were 2.0, 4.2, and 14.0 months, respectively (p < 0.001). CONCLUSION:Our model may assist clinical decision making for patients in whom tissue-based mutation testing is difficult or as a supplement to mutation testing. en
dc.format.medium Electronic en
dc.language eng en
dc.relation.ispartofseries BMC cancer en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.rights.uri en
dc.title Development and validation of a predictive model for estimating EGFR mutation probabilities in patients with non-squamous non-small cell lung cancer in New Zealand. en
dc.type Journal Article en
dc.identifier.doi 10.1186/s12885-020-07162-z en
pubs.issue 1 en
pubs.begin-page 658 en
pubs.volume 20 en
dc.rights.holder Copyright: The authors en
pubs.publication-status Published en
dc.rights.accessrights en
pubs.subtype research-article en
pubs.subtype Journal Article en
pubs.elements-id 809327 en Medical and Health Sciences en Medical Sciences en Pharmacology en Population Health en Epidemiology & Biostatistics en Science en Science Research en Maurice Wilkins Centre (2010-2014) en
dc.identifier.eissn 1471-2407 en
pubs.record-created-at-source-date 2020-07-16 en
pubs.dimensions-id 32664868 en

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