A Handcrafted Radiomics-Based Model for the Diagnosis of Usual Interstitial Pneumonia in Patients with Idiopathic Pulmonary Fibrosis.

handcrafted radiomics interstitial lung diseases machine learning usual interstitial pneumonia

Journal

Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269

Informations de publication

Date de publication:
28 Feb 2022
Historique:
received: 25 01 2022
revised: 23 02 2022
accepted: 26 02 2022
entrez: 25 3 2022
pubmed: 26 3 2022
medline: 26 3 2022
Statut: epublish

Résumé

The most common idiopathic interstitial lung disease (ILD) is idiopathic pulmonary fibrosis (IPF). It can be identified by the presence of usual interstitial pneumonia (UIP) via high-resolution computed tomography (HRCT) or with the use of a lung biopsy. We hypothesized that a CT-based approach using handcrafted radiomics might be able to identify IPF patients with a radiological or histological UIP pattern from those with an ILD or normal lungs. A total of 328 patients from one center and two databases participated in this study. Each participant had their lungs automatically contoured and sectorized. The best radiomic features were selected for the random forest classifier and performance was assessed using the area under the receiver operator characteristics curve (AUC). A significant difference in the volume of the trachea was seen between a normal state, IPF, and non-IPF ILD. Between normal and fibrotic lungs, the AUC of the classification model was 1.0 in validation. When classifying between IPF with a typical HRCT UIP pattern and non-IPF ILD the AUC was 0.96 in validation. When classifying between IPF with UIP (radiological or biopsy-proved) and non-IPF ILD, an AUC of 0.66 was achieved in the testing dataset. Classification between normal, IPF/UIP, and other ILDs using radiomics could help discriminate between different types of ILDs via HRCT, which are hardly recognizable with visual assessments. Radiomic features could become a valuable tool for computer-aided decision-making in imaging, and reduce the need for unnecessary biopsies.

Identifiants

pubmed: 35330373
pii: jpm12030373
doi: 10.3390/jpm12030373
pmc: PMC8948773
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : European Research Council
ID : ERC-ADG-2015 694812;
Pays : International
Organisme : H2020 European Research Council
ID : 733008
Organisme : H2020 European Research Council
ID : 766276
Organisme : H2020 European Research Council
ID : 952172
Organisme : H2020 European Research Council
ID : 952013
Organisme : European Research Council
ID : 101034347
Pays : International
Organisme : TRANSCAN Joint Transnational Call 2016
ID : UM 2017-8295
Organisme : Dutch Cancer Society
ID : 12085/2018-2

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Auteurs

Turkey Refaee (T)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands.
Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia.

Benjamin Bondue (B)

Department of Pneumology, Erasme University Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium.

Gaetan Van Simaeys (G)

Department of Nuclear Medicine, Erasme University Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium.

Guangyao Wu (G)

Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China.

Chenggong Yan (C)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands.
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.

Henry C Woodruff (HC)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands.
Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands.

Serge Goldman (S)

Department of Nuclear Medicine, Erasme University Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium.

Philippe Lambin (P)

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands.
Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, 6200 MD Maastricht, The Netherlands.

Classifications MeSH