Automatic information extraction from childhood cancer pathology reports. Issue 2 (16th June 2022) - Yoon, Hong-Jun; Peluso, Alina; Durbin, Eric B; Wu, Xiao-Cheng; Stroup, Antoinette; Doherty, Jennifer; Schwartz, Stephen; Wiggins, Charles; Coyle, Linda; Penberthy, Lynne
Abstract: Objectives: The International Classification of Childhood Cancer (ICCC) facilitates the effective classification of a heterogeneous group of cancers in the important pediatric population. However, there has been no development of machine learning models for the ICCC classification. We developed deep learning-based information extraction models from cancer pathology reports based on the ICD-O-3 coding standard. In this article, we describe extending the models to perform ICCC classification. Materials and Methods: We developed 2 models, ICD-O-3 classification and ICCC recoding (Model 1) and direct ICCC classification (Model 2), and 4 scenarios subject to the training sample size. We evaluated these models with a corpus consisting of 29 206 reports with age at diagnosis between 0 and 19 from 6 state cancer registries. Results: Our findings suggest that the direct ICCC classification (Model 2) is substantially better than reusing the ICD-O-3 classification model (Model 1). Applying the uncertainty quantification mechanism to assess the confidence of the algorithm in assigning a code demonstrated that the model achieved a micro-F1 score of 0.987 while abstaining (not sufficiently confident to assign a code) on only 14.8% of ambiguous pathology reports. Conclusions: Our experimental results suggest that the machine learning-based automatic information extraction from childhood cancer pathology reports in the ICCC is a reliable means of supplementing human annotators at state cancer registries by reading and abstracting the majority of the childhood cancer pathology reports accurately and reliably. Lay Summary: ICCC is the coding standard designed to categorize childhood cancers. However, machine learning-based ICCC classification has not been extensively studied, mainly owing to the limited volume of the pediatric cancer corpus; pediatric cancer is much less prevalent than adult cancers. Under the oversight of the National Childhood Cancer Registry project, we developed a deep learning-based text comprehension model for classifying ICCC from childhood cancer pathology reports. We performed a comparison study between (1) classifying ICD-O-3 codes and then recoding into ICCC and (2) classifying ICCC codes directly. We observed that the second approach exhibited a substantially higher accuracy score.We are aware that the low-precision models are not appropriate for this exercise because they will degrade the credibility of the model-based decisions. We applied an uncertainty quantification algorithm to the ICCC classification model. We achieved nearly perfect accuracy scores, while the model passed over 14.8% of ambiguous cases. This result means our machine learning model can serve human annotators at state cancer registries by processing 85.2% of the childhood cancer pathology reports automatically.