Machine Learning-Based Extraction of Breast Cancer Receptor Status From Bilingual Free-Text Pathology Reports
| Autor: | Lien van Walle, Harlinde De Schutter, Nancy Van Damme, K. Henau, Antoine Pironet, Joris Mattheijssens, Hélène Poirel, Liesbet Van Eycken, Tim Tambuyzer |
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| Rok vydání: | 2021 |
| Předmět: |
Pathology
medicine.medical_specialty Population Estrogen receptor Machine learning computer.software_genre Breast cancer breast cancer medicine Medical diagnosis natural language processing education education.field_of_study business.industry Cancer receptor status QA75.5-76.95 General Medicine Brief Research Report medicine.disease Random forest Cancer registry machine learning Electronic computers. Computer science Medicine Digital Health pathology Personalized medicine Artificial intelligence Public aspects of medicine RA1-1270 business computer |
| Zdroj: | Frontiers in Digital Health Frontiers in Digital Health, Vol 3 (2021) |
| ISSN: | 2673-253X |
| Popis: | As part of its core business of gathering population-based information on new cancer diagnoses, the Belgian Cancer Registry receives free-text pathology reports, describing results of (pre-)malignant specimens. These reports are provided by 82 laboratories and written in 2 national languages, Dutch or French. For breast cancer, the reports characterize the status of estrogen receptor, progesterone receptor, and Erb-b2 receptor tyrosine kinase 2. These biomarkers are related with tumor growth and prognosis and are essential to define therapeutic management. The availability of population-scale information about their status in breast cancer patients can therefore be considered crucial to enrich real-world scientific studies and to guide public health policies regarding personalized medicine. The main objective of this study is to expand the data available at the Belgian Cancer Registry by automatically extracting the status of these biomarkers from the pathology reports. Various types of numeric features are computed from over 1,300 manually annotated reports linked to breast tumors diagnosed in 2014. A range of popular machine learning classifiers, such as support vector machines, random forests and logistic regressions, are trained on this data and compared using their F1 scores on a separate validation set. On a held-out test set, the best performing classifiers achieve F1 scores ranging from 0.89 to 0.92 for the four classification tasks. The extraction is thus reliable and allows to significantly increase the availability of this valuable information on breast cancer receptor status at a population level. |
| Databáze: | OpenAIRE |
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