Mottin, Luc; Goldman, Jean-Philippe; Jäggli, Christoph; Achermann, Rita; Gobeill, Julien; Knafou, Julien; Ehrsam, Julien; Wicky, Alexandre; Gérard, Camille L; Schwenk, Tanja; Charrier, Mélinda; Tsantoulis, Petros; Lovis, Christian; Leichtle, Alexander; Kiessling, Michael K; Michielin, Olivier; Pradervand, Sylvain; Foufi, Vasiliki; Ruch, Patrick (2023). Multilingual RECIST classification of radiology reports using supervised learning. Frontiers in digital health, 5(1195017), p. 1195017. Frontiers Media 10.3389/fdgth.2023.1195017
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OBJECTIVES
The objective of this study is the exploration of Artificial Intelligence and Natural Language Processing techniques to support the automatic assignment of the four Response Evaluation Criteria in Solid Tumors (RECIST) scales based on radiology reports. We also aim at evaluating how languages and institutional specificities of Swiss teaching hospitals are likely to affect the quality of the classification in French and German languages.
METHODS
In our approach, 7 machine learning methods were evaluated to establish a strong baseline. Then, robust models were built, fine-tuned according to the language (French and German), and compared with the expert annotation.
RESULTS
The best strategies yield average F1-scores of 90% and 86% respectively for the 2-classes (Progressive/Non-progressive) and the 4-classes (Progressive Disease, Stable Disease, Partial Response, Complete Response) RECIST classification tasks.
CONCLUSIONS
These results are competitive with the manual labeling as measured by Matthew's correlation coefficient and Cohen's Kappa (79% and 76%). On this basis, we confirm the capacity of specific models to generalize on new unseen data and we assess the impact of using Pre-trained Language Models (PLMs) on the accuracy of the classifiers.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry |
UniBE Contributor: |
Leichtle, Alexander Benedikt (B) |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
2673-253X |
Publisher: |
Frontiers Media |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
03 Jul 2023 09:02 |
Last Modified: |
16 Jul 2023 02:26 |
Publisher DOI: |
10.3389/fdgth.2023.1195017 |
PubMed ID: |
37388252 |
Uncontrolled Keywords: |
RECIST language models narrative text classification radiology reports supervised machine learning |
BORIS DOI: |
10.48350/184295 |
URI: |
https://boris.unibe.ch/id/eprint/184295 |
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