ESMO MAP | Poster | Automatic data processing to identify EGFR mutations in pathology reports of patients with non-small cell lung cancer (NSCLC)
Authors: Betzabel Cajiao Garcia (Groningen, Netherlands) Bart Koopman (Groningen, Netherlands) Vincent De Jager (Groningen, Netherlands) Clara L. Oeste (Leuven, Belgium) Ed Schuuring (Groningen, Netherlands) Anthonie Jan Van der Wekken (Groningen, Netherlands) Stefan Willems (Groningen, Netherlands) Leon Van Kempen (Edegem, Belgium)
Download our poster, as presented at ESMO MAP 2023, and learn more.
Background
Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer. Driver mutations in epidermal growth factor receptor (EGFR), which occur in ∼10-15% of NSCLC, can be targeted by specific therapies. Real-world data can provide valuable information regarding the prevalence of these mutations, including their subtypes. However, despite comprehensive data availability in the Dutch Pathology Registry (PALGA), manual extraction of EGFR mutation status from narrative pathology reports is time-consuming. Therefore, we used machine learning and natural language processing (NLP) to identify pathology reports that state the presence of an EGFR mutation.
Conclusions
NLP algorithms allow rapid data extraction from pathology reports, thereby offering a time-efficient and cost-effective alternative to manual data processing. In turn, this approach enables rapid insight in current biomarker testing rates and prevalence of (actionable) mutations.
Complete the form above to download the poster and discover the methods used and the results obtained.
Click HERE to visit our Lung Cancer Insights overview for more information on the LynxCare datasets within our European hospital network.
Background
Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer. Driver mutations in epidermal growth factor receptor (EGFR), which occur in ∼10-15% of NSCLC, can be targeted by specific therapies. Real-world data can provide valuable information regarding the prevalence of these mutations, including their subtypes. However, despite comprehensive data availability in the Dutch Pathology Registry (PALGA), manual extraction of EGFR mutation status from narrative pathology reports is time-consuming. Therefore, we used machine learning and natural language processing (NLP) to identify pathology reports that state the presence of an EGFR mutation.
Conclusions
NLP algorithms allow rapid data extraction from pathology reports, thereby offering a time-efficient and cost-effective alternative to manual data processing. In turn, this approach enables rapid insight in current biomarker testing rates and prevalence of (actionable) mutations.
Complete the form above to download the poster and discover the methods used and the results obtained.
Click HERE to visit our Lung Cancer Insights overview for more information on the LynxCare datasets within our European hospital network.
