Hamid Abdollahi1, Shayan Mostafaei2, Susan Cheraghi3, Isaac Shiri4, Seied Rabi Mahdavi5, Anoshirvan Kazemnejad6. 1. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. 2. Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. 3. Department of Radiation Sciences, Allied Medicine Faculty, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran. 4. Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran; Department of Biomedical and Health Informatics, Rajaei Cardiovascular, Medical, Research Center, Iran University of Medical Sciences, Tehran, Iran. 5. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran. Electronic address: mahdavi.r@iums.ac.ir. 6. Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. Electronic address: kazem_an@modares.ac.ir.
Abstract
OBJECTIVES: Immediately or after head-and-neck (H&N) cancer chemoradiotherapy (CRT), patients may undergone significant sensorineural hearing loss (SNHL) which could affect their quality of life. Radiomic feature analysis is proposed to predict SNHL induced by CRT. MATERIAL AND METHODS: 490 image features of 94 cochlea from 47 patients treated with three dimensional conformal RT (3DCRT) for different H&N cancers were extracted from CT images. Different machine learning (ML) algorithms and also least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented on radiomic features for feature selection, classification and prediction. Also, LASSO penalized logistic model was used for outcome modelling. RESULTS: The predictive power of ten ML methods was more than 70% (in accuracy, precision and area under the curve of receiver operating characteristic curve (AUC)). According to the LASSO penalized logistic modelling, 10 of the 490 radiomic features selected as the associated features with SNHL status. All of the 10 features were statistically associated with SNHL (all of adjusted P-values < .001). CONCLUSION: CT radiomic analysis proposed in this study, could help in the prediction of hearing loss induced by chemoradiation. Our study also, demonstrates that combination of radiomic features with clinical and dosimetric variables can model radiotherapy outcome such as SNHL.
OBJECTIVES: Immediately or after head-and-neck (H&N) cancer chemoradiotherapy (CRT), patients may undergone significant sensorineural hearing loss (SNHL) which could affect their quality of life. Radiomic feature analysis is proposed to predict SNHL induced by CRT. MATERIAL AND METHODS: 490 image features of 94 cochlea from 47 patients treated with three dimensional conformal RT (3DCRT) for different H&N cancers were extracted from CT images. Different machine learning (ML) algorithms and also least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented on radiomic features for feature selection, classification and prediction. Also, LASSO penalized logistic model was used for outcome modelling. RESULTS: The predictive power of ten ML methods was more than 70% (in accuracy, precision and area under the curve of receiver operating characteristic curve (AUC)). According to the LASSO penalized logistic modelling, 10 of the 490 radiomic features selected as the associated features with SNHL status. All of the 10 features were statistically associated with SNHL (all of adjusted P-values < .001). CONCLUSION: CT radiomic analysis proposed in this study, could help in the prediction of hearing loss induced by chemoradiation. Our study also, demonstrates that combination of radiomic features with clinical and dosimetric variables can model radiotherapy outcome such as SNHL.
Authors: Kyle J Lafata; Yushi Chang; Chunhao Wang; Yvonne M Mowery; Irina Vergalasova; Donna Niedzwiecki; David S Yoo; Jian-Guo Liu; David M Brizel; Fang-Fang Yin Journal: Med Phys Date: 2021-06-02 Impact factor: 4.506
Authors: Marc van Hoof; Raymond van de Berg; Marly F J A van der Lubbe; Akshayaa Vaidyanathan; Marjolein de Wit; Elske L van den Burg; Alida A Postma; Tjasse D Bruintjes; Monique A L Bilderbeek-Beckers; Patrick F M Dammeijer; Stephanie Vanden Bossche; Vincent Van Rompaey; Philippe Lambin Journal: Radiol Med Date: 2021-11-25 Impact factor: 3.469