Classification of Oncology Treatment Responses from French Radiology Reports with Supervised Machine Learning

Jean-Philippe Goldman, Luc Mottin, Jamil Zaghir, Daniel Keszthelyi, Belinda Lokaj, Hugues Turbé, Julien Gobeill, Patrick Ruch, Julien Ehrsam, Christian Lovis

Abstract: The present study shows first attempts to automatically classify oncology treatment responses on the basis of the textual conclusion sections of radiology reports according to the RECIST classification. After a robust and extended manual annotation of 543 conclusion sections (5-to-50-word long), and after the training of several machine learning techniques (from traditional machine learning to deep learning), the best results show an accuracy score of 0.90 for a two-class classification (non-progressive vs. progressive disease) and of 0.82 for a four-class classification (complete response, partial response, stable disease, progressive disease) both with Logistic Regression approach. Some innovative solutions are further suggested to improve these scores in the future.