Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models
| العنوان: | Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models |
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| المؤلفون: | Jonas A. Castelijns, Hugo J.W.L. Aerts, Marjaneh Taghavi, Selam Waktola, Regina G. H. Beets-Tan, Abrahim Al-Mamgani, Michiel W. M. van den Brekel, Zeno A R Gouw, Bas Jasperse, Paula Bos |
| المساهمون: | Maxillofacial Surgery (AMC), RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, School Office GROW, Faculteit FHML Centraal, Oral and Maxillofacial Surgery, Oral and Maxillofacial Surgery / Oral Pathology, Radiology and nuclear medicine, CCA - Imaging and biomarkers |
| المصدر: | European Journal of Radiology, 139:109701. Elsevier Ireland Ltd Bos, P, van den Brekel, M W M, Gouw, Z A R, Al-Mamgani, A, Taghavi, M, Waktola, S, Aerts, H J W L, Castelijns, J A, Beets-Tan, R G H & Jasperse, B 2021, ' Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models ', European Journal of Radiology, vol. 139, 109701 . https://doi.org/10.1016/j.ejrad.2021.109701 European journal of radiology, 139:109701. Elsevier Ireland Ltd Bos, P, van den Brekel, M W M, Gouw, Z A R, Al-Mamgani, A, Taghavi, M, Waktola, S, Aerts, H J W L, Castelijns, J A, Beets-Tan, R G H & Jasperse, B 2021, ' Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models ', European Journal of Radiology, vol. 139, 109701, pp. 109701 . https://doi.org/10.1016/j.ejrad.2021.109701 |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Oncology, medicine.medical_specialty, Oropharyngeal neoplasms, Treatment outcome, Logistic regression, Head and neck neoplasms, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Radiomics, NECK-CANCER, SDG 3 - Good Health and Well-being, Internal medicine, Machine learning, medicine, Humans, Radiology, Nuclear Medicine and imaging, HEAD, Retrospective Studies, medicine.diagnostic_test, business.industry, Area under the curve, Cancer, Magnetic resonance imaging, General Medicine, medicine.disease, Primary tumor, Magnetic Resonance Imaging, Confidence interval, 030220 oncology & carcinogenesis, SURVIVAL, business |
| الوصف: | OBJECTIVES: New markers are required to predict chemoradiation response in oropharyngeal squamous cell carcinoma (OPSCC) patients. This study evaluated the ability of magnetic resonance (MR) radiomics to predict locoregional control (LRC) and overall survival (OS) after chemoradiation and aimed to determine whether this has added value to traditional clinical outcome predictors.METHODS: 177 OPSCC patients were eligible for this study. Radiomic features were extracted from the primary tumor region in T1-weighted postcontrast MRI acquired before chemoradiation. Logistic regression models were created using either clinical variables (clinical model), radiomic features (radiomic model) or clinical and radiomic features combined (combined model) to predict LRC and OS 2-years posttreatment. Model performance was evaluated using area under the curve (AUC), 95 % confidence intervals were calculated using 500 iterations of bootstrap. All analyses were performed for the total population and the Human papillomavirus (HPV) negative tumor subgroup.RESULTS: A combined model predicted treatment outcome with a higher AUC (LRC: 0.745 [0.734-0.757], OS: 0.744 [0.735-0.753]) than the clinical model (LRC: 0.607 [0.594-0.620], OS: 0.708 [0.697-0.719]). Performance of the radiomic model was comparable to the combined model for LRC (AUC: 0.740 [0.729-0.750]), but not for OS prediction (AUC: 0.654 [0.646-0.662]). In HPV negative patients, the performance of all models was not sufficient with AUCs ranging from 0.587 to 0.660 for LRC and 0.559 to 0.600 for OS prediction.CONCLUSION: Predictive models that include clinical variables and radiomic tumor features derived from MR images of OPSCC better predict LRC after chemoradiation than models based on only clinical variables. Predictive models that include clinical variables perform better than models based on only radiomic features for the prediction of OS. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 0720-048X |
| DOI: | 10.1016/j.ejrad.2021.109701 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e768cf45b5bf8ae98049b3ea6fd68cbc https://research.vu.nl/en/publications/dff8a25e-b5fc-496d-b712-ff93c21b1b99 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e768cf45b5bf8ae98049b3ea6fd68cbc |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 0720048X |
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| DOI: | 10.1016/j.ejrad.2021.109701 |