Simple delineations cannot substitute full 3d tumor delineations for MR-based radiomics prediction of locoregional control in oropharyngeal cancer. Issue 148 (March 2022)

• Record Type: Journal Article
• Authors: Bos, Paula; van den Brekel, Michiel W.M.; Taghavi, Marjaneh; Gouw, Zeno A.R.; Al-Mamgani, Abrahim; Waktola, Selam; Aerts, Hugo J.W.L.; Beets-Tan, Regina G.H.; Castelijns, Jonas A.; Jasperse, Bas
• Is Part Of: European journal of radiology. Issue 148(2022)
• Publication Date: 2022-03
• Subjects: Head and neck neoplasms -- Oropharyngeal neoplasms -- Magnetic Resonance Imaging -- Outcome prediction -- Radiomics -- Machine learning; AUC Area under the curve -- 95% CI 95% Confidence interval -- CRT Chemoradiation therapy -- DSC Dice Similarity Coefficient -- GTV Gross Tumor Volume -- HD Hausdorff Distance -- HPV Human Papilloma Virus -- ICC Intraclass Correlation Coefficient -- LoG Laplacian of Gaussian -- LRC Locoregional control -- OPSCC Oropharyngeal squamous cell carcinoma; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110167
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Background and purpose: Manual delineation of head and neck tumor contours for radiomics analyses is tedious and time consuming. This study investigates if fast or readily available tumor contours can substitute full tumor contours by an experienced observer for an MR-based radiomics model to predict locoregional control (LRC) in oropharyngeal squamous cell carcinoma (OPSCC) tumors. Materials and methods: Radiomic features were extracted from postcontrast T1-weighted MRIs of 177 OPSCC primary tumors using six different manual delineation strategies. LRC prediction models based on recursive feature elimination combined with logistic regression were built. Models were trained and tested on data from each separate delineation. Additionally, the model derived from segmentations from the experienced reader was tested by each of the alternative delineations. Complementary, this was repeated with removal of size and shape features. Model performance was evaluated using area under the curve (AUC). Results: Prediction performance of the experienced radiologist tumor delineation (AUC: 0.74) was superior compared to all other delineations when trained and tested (AUCs: 0.41–0.56) or trained on experienced delineations and tested (AUC: 0.56–0.67) on alternative segmentations. Removal of size and shape features considerably decreases prediction performance (AUC: 0.54). Applying the model based on expert delineations to spherical or single slice delineations makes prediction worthless since these models predict one class. Conclusion: Fast or readily available contours cannot substitute full expert tumor delineations in radiomics models predictive of LRC in OPSCC.