Related Experiment Video
Updated: May 25, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB® artificial
Florent Le Borgne1, Camille Garnier2, Camille Morisseau1
1Data Factory & Analytics Department, Institut de Cancérologie de l'Ouest, Nantes-Angers, France.
Purpose:
To evaluate the effectiveness of C-LAB®, an artificial intelligence (AI) platform, in extracting, structuring, and centralizing biomarker data from breast cancer pathology reports within the challenging, heterogeneous dataset of the Institut de Cancérologie de l'Ouest (ICO).
Methods:
C-LAB® was deployed at the ICO to analyze HER2 and hormonal receptor data from breast cancer pathology reports. During the development phase, 292 anatomic pathology reports were used to design and refine the rule-based extraction algorithm through an iterative process of monitoring and adjustments. After finalizing the algorithm, it was applied to a total of 2323 anatomic pathology reports. To evaluate the platform's accuracy, performance metrics could only be calculated for a subset of these reports that were also available in the structured National Epidemiological Strategy and Medical Economics (ESME) database. Out of the 2323 pathology reports belonging to 487 patients analyzed by C-LAB®, 666 corresponded to 97 patients present in the ESME database. These reports were used as the gold standard for performance assessment, as ESME provides structured data against which the outputs of the C-LAB® algorithm could be compared.
Results:
C-LAB® achieved over 80% agreement with human extractions (precision, recall, and F1-score) in structuring biomarker data from complex, unstructured pathology reports, despite dataset variability and optical character recognition errors. While the ESME database served as a benchmark, its reliance on single manual data entry without secondary review introduces potential inaccuracies, suggesting the observed performance reflects close alignment between human and algorithmic extractions rather than absolute accuracy. C-LAB® demonstrates significant potential to reduce manual workload, centralize data, and enable scalable, real-time reporting.
Conclusion:
AI technologies like C-LAB® show significant potential in creating accessible and actionable digital factories from complex pathology data, aiding in the precision management of diseases such as breast cancer diagnostics and treatment.
