T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting

Insights into Imaging

Raymond H. Mak¹  Jakob Weiss¹  Sander Puts²  André L. A. J. Dekker²  Hugo J. W. L. Aerts³  Simon G. F. Robben⁴  J. Martijn Nobel⁴ 

[1]Artificial Intelligence in Medicine (AIM) Program, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

[2]Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA

[3]Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands

[4]Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Postbox 5800, 6202 AZ, Maastricht, The Netherlands

[5]Artificial Intelligence in Medicine (AIM) Program, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

[6]Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA

[7]Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Postbox 5800, 6202 AZ, Maastricht, The Netherlands

[8]School of Health Professions Education, Maastricht University, Maastricht, The Netherlands

关键词: Radiology;    Reporting;    Natural language processing;    Free-text;    Classification system;   

DOI  :  10.1186/s13244-021-01018-1

来源: Springer

【 摘 要 】

BackgroundIn the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built to quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification. This structuring tool was translated and validated on English radiological free-text reports. A rule-based algorithm to classify T-stage was trained and validated on, respectively, 200 and 225 English free-text radiological reports from diagnostic computed tomography (CT) obtained for staging of patients with lung cancer. The automated T-stage extracted by the algorithm from the report was compared to manual staging. A graphical user interface was built for training purposes to visualize the results of the algorithm by highlighting the extracted concepts and its modifying context.ResultsAccuracy of the T-stage classifier was 0.89 in the validation set, 0.84 when considering the T-substages, and 0.76 when only considering tumor size. Results were comparable with the Dutch results (respectively, 0.88, 0.89 and 0.79). Most errors were made due to ambiguity issues that could not be solved by the rule-based nature of the algorithm.ConclusionsNLP can be successfully applied for staging lung cancer from free-text radiological reports in different languages. Focused introduction of machine learning should be introduced in a hybrid approach to improve performance.

【 授权许可】

CC BY