Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics

Abstract To investigate the role of quantitative ultrasound (QUS) radiomics to predict treatment response in patients with head and neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). Five spectral parameters, 20 texture, and 80 texture-derivative features were extracted fro...

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Autores principales:	Laurentius Oscar Osapoetra, Archya Dasgupta, Daniel DiCenzo, Kashuf Fatima, Karina Quiaoit, Murtuza Saifuddin, Irene Karam, Ian Poon, Zain Husain, William T. Tran, Lakshmanan Sannachi, Gregory J. Czarnota
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/71fe57975a2c4ff8af62d83d2064dff7
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spelling	oai:doaj.org-article:71fe57975a2c4ff8af62d83d2064dff72021-12-02T17:05:11ZAssessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics10.1038/s41598-021-85221-62045-2322https://doaj.org/article/71fe57975a2c4ff8af62d83d2064dff72021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85221-6https://doaj.org/toc/2045-2322Abstract To investigate the role of quantitative ultrasound (QUS) radiomics to predict treatment response in patients with head and neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). Five spectral parameters, 20 texture, and 80 texture-derivative features were extracted from the index lymph node before treatment. Response was assessed initially at 3 months with complete responders labelled as early responders (ER). Patients with residual disease were followed to classify them as either late responders (LR) or patients with persistent/progressive disease (PD). Machine learning classifiers with leave-one-out cross-validation was used for the development of a binary response-prediction radiomics model. A total of 59 patients were included in the study (22 ER, 29 LR, and 8 PD). A support vector machine (SVM) classifier led to the best performance with accuracy and area under curve (AUC) of 92% and 0.91, responsively to define the response at 3 months (ER vs. LR/PD). The 2-year recurrence-free survival for predicted-ER, LR, PD using an SVM-model was 91%, 78%, and 27%, respectively (p < 0.01). Pretreatment QUS-radiomics using texture derivatives in HNSCC can predict the response to RT with an accuracy of more than 90% with a strong influence on the survival. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.Laurentius Oscar OsapoetraArchya DasguptaDaniel DiCenzoKashuf FatimaKarina QuiaoitMurtuza SaifuddinIrene KaramIan PoonZain HusainWilliam T. TranLakshmanan SannachiGregory J. CzarnotaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Laurentius Oscar Osapoetra Archya Dasgupta Daniel DiCenzo Kashuf Fatima Karina Quiaoit Murtuza Saifuddin Irene Karam Ian Poon Zain Husain William T. Tran Lakshmanan Sannachi Gregory J. Czarnota Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics
description	Abstract To investigate the role of quantitative ultrasound (QUS) radiomics to predict treatment response in patients with head and neck squamous cell carcinoma (HNSCC) treated with radical radiotherapy (RT). Five spectral parameters, 20 texture, and 80 texture-derivative features were extracted from the index lymph node before treatment. Response was assessed initially at 3 months with complete responders labelled as early responders (ER). Patients with residual disease were followed to classify them as either late responders (LR) or patients with persistent/progressive disease (PD). Machine learning classifiers with leave-one-out cross-validation was used for the development of a binary response-prediction radiomics model. A total of 59 patients were included in the study (22 ER, 29 LR, and 8 PD). A support vector machine (SVM) classifier led to the best performance with accuracy and area under curve (AUC) of 92% and 0.91, responsively to define the response at 3 months (ER vs. LR/PD). The 2-year recurrence-free survival for predicted-ER, LR, PD using an SVM-model was 91%, 78%, and 27%, respectively (p < 0.01). Pretreatment QUS-radiomics using texture derivatives in HNSCC can predict the response to RT with an accuracy of more than 90% with a strong influence on the survival. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.
format	article
author	Laurentius Oscar Osapoetra Archya Dasgupta Daniel DiCenzo Kashuf Fatima Karina Quiaoit Murtuza Saifuddin Irene Karam Ian Poon Zain Husain William T. Tran Lakshmanan Sannachi Gregory J. Czarnota
author_facet	Laurentius Oscar Osapoetra Archya Dasgupta Daniel DiCenzo Kashuf Fatima Karina Quiaoit Murtuza Saifuddin Irene Karam Ian Poon Zain Husain William T. Tran Lakshmanan Sannachi Gregory J. Czarnota
author_sort	Laurentius Oscar Osapoetra
title	Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics
title_short	Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics
title_full	Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics
title_fullStr	Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics
title_full_unstemmed	Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics
title_sort	assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/71fe57975a2c4ff8af62d83d2064dff7
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Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics

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