Cancer staging provides a basis for planning clinical management, but also allows for meaningful analysis of cancer outcomes and evaluation of cancer care services. Despite this, stage data in cancer registries is often incomplete, inaccurate, or simply not collected. This article describes a prototype software system (Cancer Stage Interpretation System, CSIS) that automatically extracts cancer staging information from medical reports. The system uses text classification techniques to train support vector machines (SVMs) to extract elements of stage listed in cancer staging guidelines. When processing new reports, CSIS identifies sentences relevant to the staging decision, and subsequently assigns the most likely stage. The system was developed using a database of staging data and pathology reports for 710 lung cancer patients, then validated in an independent set of 179 patients against pathologic stage assigned by two independent pathologists. CSIS achieved overall accuracy of 74% for tumor (T) staging and 87% for node (N) staging, and errors were observed to mirror disagreements between human experts.

This article investigates the classification of a patient's lung cancer stage based on analysis of their free-text medical reports. The system uses natural language processing to transform the report text, including identification of UMLS terms and detection of negated findings. The transformed report is then classified using statistical machine learning techniques. A support vector machine is trained for each stage category based on word occurrences in a corpus of histology reports for pathologically staged patients. New reports can be classified according to the most likely stage, allowing the collection of population stage data for analysis of outcomes. While the system could in principle be applied to stage different cancer types, the current work focuses on lung cancer due to data availability. The article presents initial experiments quantifying system performance for T and N staging on a corpus of histology reports from more than 700 lung cancer patients.

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