Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma
Predicting treatment failure (TF) in head-and-neck squamous cell carcinoma (HNSCC) patients before treatment can help in selecting a more appropriate treatment approach. We investigated a novel radiodosiomics approach to predict TF prior to chemoradiation in HNSCC patients. Computed tomography (CT)...
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2025-06-01
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| author | Hidemi Kamezawa Hidetaka Arimura |
| author_facet | Hidemi Kamezawa Hidetaka Arimura |
| author_sort | Hidemi Kamezawa |
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| description | Predicting treatment failure (TF) in head-and-neck squamous cell carcinoma (HNSCC) patients before treatment can help in selecting a more appropriate treatment approach. We investigated a novel radiodosiomics approach to predict TF prior to chemoradiation in HNSCC patients. Computed tomography (CT) images, dose distributions (DDs), and clinical data from 172 cases were collected from a public database. The cases were divided into the training (<i>n</i> = 140) and testing (<i>n</i> = 32) datasets. A total of 1027 features, including conventional radiomic (<i>R</i>) features, local binary pattern-based (<i>L</i>) features, and topological (<i>T</i>) features, were extracted from the CT images and DDs of the tumor region. Moreover, deep (<i>D</i>) features were extracted from a deep learning-based prediction model. The Coxnet algorithm was employed to select significant features. Twenty-two treatment failure prediction models were constructed based on Rad-scores. TF prediction models were assessed using the concordance index (C-index) and statistically significant variations in the Kaplan–Meier curves between the two risk groups. The Kaplan–Meier curves of the DD-based <i>T</i> (DD-<i>T</i>) model displayed statistically significant differences. The highest C-index of the testing dataset for this model was 0.760. The proposed radiodosiomics models could potentially demonstrate greater accuracy in anticipating TF before chemoradiation in HNSCC patients. |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-894d960e7fe648598fe13a746cc0bc032025-08-20T03:26:17ZengMDPI AGApplied Sciences2076-34172025-06-011512694110.3390/app15126941Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell CarcinomaHidemi Kamezawa0Hidetaka Arimura1Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, 6-22 Misaki-machi, Omuta 836-8505, Fukuoka, JapanDivision of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Fukuoka, JapanPredicting treatment failure (TF) in head-and-neck squamous cell carcinoma (HNSCC) patients before treatment can help in selecting a more appropriate treatment approach. We investigated a novel radiodosiomics approach to predict TF prior to chemoradiation in HNSCC patients. Computed tomography (CT) images, dose distributions (DDs), and clinical data from 172 cases were collected from a public database. The cases were divided into the training (<i>n</i> = 140) and testing (<i>n</i> = 32) datasets. A total of 1027 features, including conventional radiomic (<i>R</i>) features, local binary pattern-based (<i>L</i>) features, and topological (<i>T</i>) features, were extracted from the CT images and DDs of the tumor region. Moreover, deep (<i>D</i>) features were extracted from a deep learning-based prediction model. The Coxnet algorithm was employed to select significant features. Twenty-two treatment failure prediction models were constructed based on Rad-scores. TF prediction models were assessed using the concordance index (C-index) and statistically significant variations in the Kaplan–Meier curves between the two risk groups. The Kaplan–Meier curves of the DD-based <i>T</i> (DD-<i>T</i>) model displayed statistically significant differences. The highest C-index of the testing dataset for this model was 0.760. The proposed radiodosiomics models could potentially demonstrate greater accuracy in anticipating TF before chemoradiation in HNSCC patients.https://www.mdpi.com/2076-3417/15/12/6941treatment failure predictionhead-and-neck squamous cell carcinomatopologyradiodosiomics |
| spellingShingle | Hidemi Kamezawa Hidetaka Arimura Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma Applied Sciences treatment failure prediction head-and-neck squamous cell carcinoma topology radiodosiomics |
| title | Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma |
| title_full | Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma |
| title_fullStr | Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma |
| title_full_unstemmed | Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma |
| title_short | Radiodosiomics Prediction of Treatment Failures Prior to Chemoradiotherapy in Head-and-Neck Squamous Cell Carcinoma |
| title_sort | radiodosiomics prediction of treatment failures prior to chemoradiotherapy in head and neck squamous cell carcinoma |
| topic | treatment failure prediction head-and-neck squamous cell carcinoma topology radiodosiomics |
| url | https://www.mdpi.com/2076-3417/15/12/6941 |
| work_keys_str_mv | AT hidemikamezawa radiodosiomicspredictionoftreatmentfailurespriortochemoradiotherapyinheadandnecksquamouscellcarcinoma AT hidetakaarimura radiodosiomicspredictionoftreatmentfailurespriortochemoradiotherapyinheadandnecksquamouscellcarcinoma |