Titre :

Automatic Classification of Tumor Response From Radiology Reports With Rule-Based Natural Language Processing Integrated Into the Clinical Oncology Workflow.

Auteur(s) :

Laurent, Gery [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Centre Hospitalier Régional Universitaire [CHU Lille] [CHRU Lille]
Craynest, F. [Auteur]
Thobois, M. [Auteur]
Hajjaji, Nawale [Auteur]
Protéomique, Réponse Inflammatoire, Spectrométrie de Masse (PRISM) - U1192

Titre de la revue :

JCO Clin Cancer Inform

Nom court de la revue :

JCO Clin Cancer Inform

Numéro :

7

Pagination :

e2200139

Date de publication :

2023-02-20

ISSN :

2473-4276

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

PURPOSE Imaging reports in oncology provide critical information about the disease evolution that should be timely shared to tailor the clinical decision making and care coordination of patients with advanced cancer. However, ...
Lire la suite >PURPOSE Imaging reports in oncology provide critical information about the disease evolution that should be timely shared to tailor the clinical decision making and care coordination of patients with advanced cancer. However, tumor response stays unstructured in free-text and underexploited. Natural language processing (NLP) methods can help provide this critical information into the electronic health records (EHR) in real time to assist health care workers. METHODS A rule-based algorithm was developed using SAS tools to automatically extract and categorize tumor response within progression or no progression categories. 2,970 magnetic resonance imaging, computed tomography scan, and positron emission tomography French reports were extracted from the EHR of a large comprehensive cancer center to build a 2,637-document training set and a 603-document validation set. The model was also tested on 189 imaging reports from 46 different radiology centers. A tumor dashboard was created in the EHR using the Timeline tool of the vis.js javascript library. RESULTS An NLP methodology was applied to create an ontology of radiographic terms defining tumor response, mapping text to five main concepts, and application decision rules on the basis of clinical practice RECIST guidelines. The model achieved an overall accuracy of 0.88 (ranging from 0.87 to 0.94), with similar performance on both progression and no progression classification. The overall accuracy was 0.82 on reports from different radiology centers. Data were visualized and organized in a dynamic tumor response timeline. This tool was deployed successfully at our institution both retrospectively and prospectively as part of an automatic pipeline to screen reports and classify tumor response in real time for all metastatic patients. CONCLUSION Our approach provides an NLP-based framework to structure and classify tumor response from the EHR and integrate tumor response classification into the clinical oncology workflow.Lire moins >

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
Inserm
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
• Protéomique, Réponse Inflammatoire, Spectrométrie de Masse (PRISM) - U1192

Date de dépôt :

2023-12-13T03:51:34Z
2024-01-24T15:59:55Z