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Qi Huang
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P2.09 - Pathology (Not CME Accredited Session) (ID 958)
- Event: WCLC 2018
- Type: Poster Viewing in the Exhibit Hall
- Track:
- Presentations: 1
- Moderators:
- Coordinates: 9/25/2018, 16:45 - 18:00, Exhibit Hall
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P2.09-29 - Automatic Lung Cancer Staging from Medical Reports Using Natural Language Processing (ID 13714)
16:45 - 18:00 | Author(s): Qi Huang
- Abstract
Background
Accurate TNM staging plays an important role in the diagnosis, treatment, and prognosis of lung cancer. In current clinical practice,the staging of lung cancer is usually decided by physicians. We aim to develop an automated lung cancer staging system using machine learning and verify the staging correctness.
a9ded1e5ce5d75814730bb4caaf49419 Method
In this work, we constructed a feature generalizing and automatically extracting model using NLP techniques. The parameters required for Tumor (T), Lymph nodes (N) and Metastases (M) categories of the eighth edition of the International Lung Cancer Research Association (IASLC) TNM staging system were automatically extracted from de-identified electronic medical records of pathology, operation note, CT scan, PET/CT scan, cranial MRI, bone scan, and ultrasound. A technical solution using Bayesian reasoning network was developed for automated staging. The stage was automatically predicted while the reasoning basis was given. All the reports were reviewed by thoracic surgeons to obtain the gold standard for evaluation.
4c3880bb027f159e801041b1021e88e8 Result
Five hundred de-identified reports were collected as training dataset to construct the model by learning from stage given by physicians. Five hundred and thirteen de-identified reports were collected as validation dataset. The current overall recall rate was 96.88%, and the agreement rate between machine prediction and physicians's diagnosis was 93.70%.
8eea62084ca7e541d918e823422bd82e Conclusion
Natural language processing is a useful technique for encoding medical reports in order to detect the TNM descriptors. Automatic lung cancer staging process using Bayesian reasoning network achieve acceptable accuracy. This system is extendable and can be applied to large database processing.
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