Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis

To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG). Patients with histologically confirmed TET in the years 2000-2018 were retrospectively in...

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Bibliographic Details
Published in:PloS one 2021-12, Vol.16 (12), p.e0261401-e0261401
Main Authors: Blüthgen, Christian, Patella, Miriam, Euler, André, Baessler, Bettina, Martini, Katharina, von Spiczak, Jochen, Schneiter, Didier, Opitz, Isabelle, Frauenfelder, Thomas
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Algorithms
Analysis
Biology and Life Sciences
Biomarkers
Chemotherapy
Classification
Computed tomography
Computer and Information Sciences
Confidence intervals
CT imaging
Diagnosis
Epithelial tumors
Evaluation
Female
Follow-Up Studies
Histological Techniques - methods
Histology
Hospitals
Humans
Learning algorithms
Machine Learning
Magnetic resonance imaging
Male
Medical imaging
Medical prognosis
Medicine and Health Sciences
Middle Aged
Myasthenia
Myasthenia gravis
Myasthenia Gravis - diagnostic imaging
Myasthenia Gravis - physiopathology
Neoplasm Staging
Neoplasms, Glandular and Epithelial - diagnostic imaging
Neoplasms, Glandular and Epithelial - pathology
Neoplasms, Glandular and Epithelial - surgery
Neuromuscular junctions
Patients
Predictions
Radiomics
Research and Analysis Methods
Retrospective Studies
Thoracic surgery
Thymus
Thymus Neoplasms - diagnostic imaging
Thymus Neoplasms - pathology
Thymus Neoplasms - surgery
Tomography
Tomography, X-Ray Computed - methods
Tumors
Young Adult
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Description
Summary:To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG). Patients with histologically confirmed TET in the years 2000-2018 were retrospectively included, excluding patients with incompatible imaging or other tumors. CT scans were reformatted uniformly, gray values were normalized and discretized. Tumors were segmented manually; 15 scans were re-segmented after 2 weeks by two readers. 1316 radiomic features were calculated (pyRadiomics). Features with low intra-/inter-reader agreement (ICC
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0261401