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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.


Jean-Philippe Goldman, Luc Mottin, Jamil Zaghir, Daniel Keszthelyi, Belinda Lokaj, Hugues Turbé, Julien Gobeil, Patrick Ruch, Julien Ehrsam, Christian Lovis. Classification of Oncology Treatment Responses from French Radiology Reports with Supervised Machine Learning. Studies in health technology and informatics. 2022 May 25;294:849-853

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PMID: 35612224

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