Shayan Mostafaei1,2, Hamid Abdollahi3, Shiva Kazempour Dehkordi4, Isaac Shiri5, Abolfazl Razzaghdoust6, Seyed Hamid Zoljalali Moghaddam7, Afshin Saadipoor8, Fereshteh Koosha9, Susan Cheraghi10,11, Seied Rabi Mahdavi7,11. 1. Department of Community Medicine, Faculty of Medicine, Kermanshah University of Medical Sciences, Sorkheh-Ligeh Blvd, Kermanshah, 6714415153, Iran. Shayan.mostafaei@kums.ac.ir. 2. Epidemiology and Biostatistics Unit, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran. Shayan.mostafaei@kums.ac.ir. 3. Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Sciences, Medical University Campus, Haft-Bagh Highway, 7616913555, Kerman, Iran. hamid_rbp@yahoo.com. 4. Department of Cell Systems and Anatomy, School of Medicine, University of Texas Health Science Center, San Antonio, USA. 5. Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland. 6. Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 7. Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. 8. Department of Radiation Oncology, Faculty of Medicine, Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 9. Radiology Technology Department, Allied Medical Faculty, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 10. Department of Radiation Sciences, Allied Medicine Faculty, Iran University of Medical Sciences, Tehran, Iran. 11. Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran.
Abstract
PURPOSE: Radiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancer patients based on computed tomography (CT) radiomics, clinical and dosimetric parameters. METHODS: In this prospective study, prostate cancer patients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. Stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical-radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, - 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic. RESULTS: Sixty-four prostate cancer patients were studied, and 33 and 52 patients developed ≥ grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical-radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical-radiomics models was 0.71, 0.67 and 0.77, respectively. CONCLUSIONS: We have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling.
PURPOSE: Radiomic features, clinical and dosimetric factors have the potential to predict radiation-induced toxicity. The aim of this study was to develop prediction models of radiotherapy-induced toxicities in prostate cancerpatients based on computed tomography (CT) radiomics, clinical and dosimetric parameters. METHODS: In this prospective study, prostate cancerpatients were included, and radiotherapy-induced urinary and gastrointestinal (GI) toxicities were assessed by Common Terminology Criteria for adverse events. For each patient, clinical and dose volume parameters were obtained. Imaging features were extracted from pre-treatment rectal and bladder wall CT scan of patients. Stacking algorithm and elastic net penalized logistic regression were used in order to feature selection and prediction, simultaneously. The models were fitted by imaging (radiomics model) and clinical/dosimetric (clinical model) features alone and in combinations (clinical-radiomics model). Goodness of fit of the models and performance of classifications were assessed using Hosmer and Lemeshow test, - 2log (likelihood) and area under curve (AUC) of the receiver operator characteristic. RESULTS: Sixty-four prostate cancerpatients were studied, and 33 and 52 patients developed ≥ grade 1 GI and urinary toxicities, respectively. In GI modeling, the AUC for clinical, radiomics and clinical-radiomics models was 0.66, 0.71 and 0.65, respectively. To predict urinary toxicity, the AUC for radiomics, clinical and clinical-radiomics models was 0.71, 0.67 and 0.77, respectively. CONCLUSIONS: We have shown that CT imaging features could predict radiation toxicities and combination of imaging and clinical/dosimetric features may enhance the predictive performance of radiotoxicity modeling.
Authors: Issam El Naqa; Sarah L Kerns; James Coates; Yi Luo; Corey Speers; Catharine M L West; Barry S Rosenstein; Randall K Ten Haken Journal: Phys Med Biol Date: 2017-08-01 Impact factor: 3.609
Authors: Hui Wu; Xiaojian Chen; Xin Yang; Yalan Tao; Yunfei Xia; Xiaowu Deng; Cheng Zheng; Jared Robbins; Christopher Schultz; X Allen Li Journal: Int J Radiat Oncol Biol Phys Date: 2018-05-01 Impact factor: 7.038
Authors: Matteo Ferro; Ottavio de Cobelli; Gennaro Musi; Francesco Del Giudice; Giuseppe Carrieri; Gian Maria Busetto; Ugo Giovanni Falagario; Alessandro Sciarra; Martina Maggi; Felice Crocetto; Biagio Barone; Vincenzo Francesco Caputo; Michele Marchioni; Giuseppe Lucarelli; Ciro Imbimbo; Francesco Alessandro Mistretta; Stefano Luzzago; Mihai Dorin Vartolomei; Luigi Cormio; Riccardo Autorino; Octavian Sabin Tătaru Journal: Ther Adv Urol Date: 2022-07-04
Authors: Simon K B Spohn; Alisa S Bettermann; Fabian Bamberg; Matthias Benndorf; Michael Mix; Nils H Nicolay; Tobias Fechter; Tobias Hölscher; Radu Grosu; Arturo Chiti; Anca L Grosu; Constantinos Zamboglou Journal: Theranostics Date: 2021-07-06 Impact factor: 11.556