Cancer Registry Information Extraction via Transfer Learning (2020)

Abstract: A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.

## introduction cancer is a main cause of mortality worldwide and has been the leading cause of death over several decades in our country. a cancer registry system has been established by taiwan society of cancer registry and supported by ministry of health and welfare (mohw) over 40 years. how to extract massive data concisely and maintain high quality continuously are critical issues and burdens of healthcare system. however, the maintenance of an individual cancer registry from patient healthcare trajectories needs different types of domain knowledge which is pronouncedly both labor-intensive and time-consuming. in addition, how to validate and integrate between different hospitals or between local healthcare resource and national database are crucial topics. to facilitate the integration of models for a specific cancer, applying information technology tools to improve acquisition and classification of patients' healthcare trajectories can enable more accurate phenotyping of cancer information. nevertheless, addressing the issues needs more cooperation both on information technology and medical expertise. in order to assist integration among the institutes, a national project was established under the cancer center support grant program (ccsg) supported by mohw. as the coordinator of this project, we conducted research studies and cooperated with several hospitals to establish a platform to work out a model system based on existing cancer data. one major goal of this project is to apply natural language processing (nlp) techniques to automatically analyze unstructured data including surgical reports, pathology reports, oncology clinical notes, and laboratory findings that may not be easy to acquire or share across hospitals for specific cancers. pathology reports are usually abundant and contain operative findings, general tumor information, pathological assessment, cancer staging, and end-results which need to be extracted and classified clearly. in the pilot study, we focus on tasks including the collection and deidentification of pathology reports, data annotation for developing and evaluating deep learning-based nlp systems to extract cancer registry variables from different hospital sites. to standardize the annotation of pathology material for developing our nlp system, the variables and their definitions were defined by the consensus from expertise committee composed of hospital investigators and annotators. furthermore, we applied transfer learning and conducted experiments to examine the performance of the developed neural networks on the cross-hospital pathology materials to gain insights on how effective and concise transfer learning can be. the results not only enable us to understand which layers of the developed network convey the most important parameters for transfer but also let us know how many annotations are needed for training a system for a new hospital to achieve reasonable performance. ## conclusions in this work, we investigated the feasibility of applying transfer learning via neural networks on the task of extraction cancer registry information from cross-hospital pathology reports. because the writing styles and formats of the pathology reports is different in each hospital, to estimate the requirements of the number of annotated datasets when we migrate from one hospital to the others and iteratively improve the effectiveness of the developed systems, we conducted experiments to quantify the impact of transfer learning on the datasets collected from two hospitals. from the evaluations of the results, we confirmed that when transfer learning is adopted, the model pre-trained on a source hospital can be trained with fewer annotations of the target hospital and achieve satisfactory performance as when the full training set of the target hospital is used. the results suggest us to apply the transfer learning techniques for developing a customized system for a new hospital with only a few annotations. we will develop method to estimate the required numbers of annotations based on the language properties of the narrative reports and the characteristics of the developed neural networks. furthermore, our experiment results also reveal challenges requiring to be addressed including the generalizability and catastrophic forgetting problem, which should be addressed in the future.