Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis
| العنوان: | Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis |
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| المؤلفون: | Blüthgen, Christian, Patella, Miriam, Euler, André, Baessler, Bettina, Martini, Katharina, von Spiczak, Jochen, Schneiter, Didier, Opitz, Isabelle, Frauenfelder, Thomas |
| المساهمون: | University of Zurich, Blüthgen, Christian |
| المصدر: | PLoS ONE, Vol 16, Iss 12, p e0261401 (2021) PLoS ONE |
| بيانات النشر: | Public Library of Science (PLoS), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Male, Epidemiology, 10255 Clinic for Thoracic Surgery, Cancer Treatment, Lung and Intrathoracic Tumors, Diagnostic Radiology, Machine Learning, Medicine and Health Sciences, Neoplasms, Glandular and Epithelial, Tomography, Aged, 80 and over, Multidisciplinary, 10042 Clinic for Diagnostic and Interventional Radiology, Radiology and Imaging, Cancer Risk Factors, Histological Techniques, Middle Aged, Tumor Resection, Surgical Oncology, Oncology, Medicine, Female, Anatomy, Algorithms, Research Article, Clinical Oncology, Adult, Computer and Information Sciences, Histology, Imaging Techniques, Science, Immunology, Neuroimaging, Surgical and Invasive Medical Procedures, 610 Medicine & health, Research and Analysis Methods, Autoimmune Diseases, Young Adult, Diagnostic Medicine, Artificial Intelligence, Myasthenia Gravis, Humans, Aged, Neoplasm Staging, Retrospective Studies, 1000 Multidisciplinary, Surgical Resection, Biology and Life Sciences, Cancers and Neoplasms, Thymus Neoplasms, Computed Axial Tomography, Medical Risk Factors, Clinical Immunology, Clinical Medicine, Tomography, X-Ray Computed, Neuroscience, Follow-Up Studies |
| الوصف: | Objectives 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). Methods 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 Results 105 patients undergoing surgery for TET were identified. After applying exclusion criteria, 62 patients (28 female; mean age, 57±14 years; range, 22–82 years) with 34 low-risk TET (LRT; WHO types A/AB/B1), 28 high-risk TET (HRT; WHO B2/B3/C) in early stage (49, TNM stage I-II) or advanced stage (13, TNM III-IV) were included. 14(23%) of the patients had MG. 334(25%) features were excluded after intra-/inter-reader analysis. Discriminatory performance of the random forest classifiers was good for histology(AUC, 87.6%; 95% confidence interval, 76.3–94.3) and TNM stage(AUC, 83.8%; 95%CI, 66.9–93.4) but poor for the prediction of MG (AUC, 63.9%; 95%CI, 44.8–79.5). Conclusions CT-derived radiomic features may be a useful imaging biomarker for TET histology and TNM stage. |
| وصف الملف: | journal.pone.0261401.pdf - application/pdf |
| تدمد: | 1932-6203 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::524bdc572a699237b5ed2a39f547c83dTest https://doi.org/10.1371/journal.pone.0261401Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....524bdc572a699237b5ed2a39f547c83d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19326203 |
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