Samira Abbaspour1,2, Hamid Abdollahi3,4, Hossein Arabalibeik5, Maedeh Barahman6, Amir Mohammad Arefpour6, Pedram Fadavi7, Mohammadreza Ay8,9, Seied Rabi Mahdavi10. 1. Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran. 2. Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran. 3. Department of Radiology Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran. 4. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada. 5. Research Center for Science and Technology in Medicine (RCSTM), Tehran University of Medical Sciences, Tehran, Iran. 6. Department of Radiation Oncology, Firoozgar Hospital, Firoozgar Clinical Research Development Center (FCRDC), Iran University of Medical Sciences, Tehran, Iran. 7. Department of Radiation Oncology, Iran University of Medical Sciences, Tehran, Iran. 8. Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran. mohammadreza_ay@tums.ac.ir. 9. Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences, Tehran, Iran. mohammadreza_ay@tums.ac.ir. 10. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. srmahdavi@hotmail.com.
Abstract
PURPOSE: The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients. METHODS: The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment. RESULTS: The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model's performance, respectively. CONCLUSION: This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.
PURPOSE: The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients. METHODS: The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment. RESULTS: The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model's performance, respectively. CONCLUSION: This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.
Authors: Julien Guiot; Akshayaa Vaidyanathan; Louis Deprez; Fadila Zerka; Denis Danthine; Anne-Noelle Frix; Philippe Lambin; Fabio Bottari; Nathan Tsoutzidis; Benjamin Miraglio; Sean Walsh; Wim Vos; Roland Hustinx; Marta Ferreira; Pierre Lovinfosse; Ralph T H Leijenaar Journal: Med Res Rev Date: 2021-07-26 Impact factor: 12.944
Authors: Andrea Delli Pizzi; Antonio Maria Chiarelli; Piero Chiacchiaretta; Martina d'Annibale; Pierpaolo Croce; Consuelo Rosa; Domenico Mastrodicasa; Stefano Trebeschi; Doenja Marina Johanna Lambregts; Daniele Caposiena; Francesco Lorenzo Serafini; Raffaella Basilico; Giulio Cocco; Pierluigi Di Sebastiano; Sebastiano Cinalli; Antonio Ferretti; Richard Geoffrey Wise; Domenico Genovesi; Regina G H Beets-Tan; Massimo Caulo Journal: Sci Rep Date: 2021-03-08 Impact factor: 4.996