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Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features from Surgical Pathology Reports by Loor-Torres, Ricardo; Wu, Yuqi; Cabezas, Esteban; Borras, Mariana; Toro-Tobon, David; Duran, Mayra; Zahidy, Misk Al; Chavez, Maria Mateo; Jacome, Cristian Soto; Fan, Jungwei W.; Ospina, Naykky M. Singh; Wu, Yonghui; Brito, Juan P. is a scholarly article available to read on EtoBox.

What is Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features from Surgical Pathology Reports about?

Background We aim to use Natural Language Processing (NLP) to automate the extraction and classification of thyroid cancer risk factors from pathology reports. Methods We analyzed 1,410 surgical pathology reports from adult papillary thyroid cancer patients at Mayo Clinic, Rochester, MN, from 2010 to 2019. Structured and non-structured reports were used to create a consensus-based ground truth dictionary and categorized them into modified recurrence risk levels. Non-structured reports were narrative, while structured reports followed standardized formats. We then developed ThyroPath, a rule-based NLP pipeline, to extract and classify thyroid cancer features into risk categories. Training involved 225 reports (150 structured, 75 unstructured), with testing on 170 reports (120 structured, 50 unstructured) for evaluation. The pipeline's performance was assessed using both strict and lenient criteria for accuracy, precision, recall, and F1-score. Results In extraction tasks, ThyroPath achieved overall strict F-1 scores of 93% for structured reports and 90 for unstructured reports, covering 18 thyroid cancer pathology features. In classification tasks, ThyroPath-extracted information de

Author

Loor-Torres, Ricardo; Wu, Yuqi; Cabezas, Esteban; Borras, Mariana; Toro-Tobon, David; Duran, Mayra; Zahidy, Misk Al; Chavez, Maria Mateo; Jacome, Cristian Soto; Fan, Jungwei W.; Ospina, Naykky M. Singh; Wu, Yonghui; Brito, Juan P.

Published

2024

Language

EN