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Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma

OBJECTIVE: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: This retrospective study included 285 patients (mean age ± standard deviation,...

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Autores principales: Kim, Minjae, Lee, Jeong Hyun, Joo, Leehi, Jeong, Boryeong, Kim, Seonok, Ham, Sungwon, Yun, Jihye, Kim, NamKug, Chung, Sae Rom, Choi, Young Jun, Baek, Jung Hwan, Lee, Ji Ye, Kim, Ji-hoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614290/
https://www.ncbi.nlm.nih.gov/pubmed/36126954
http://dx.doi.org/10.3348/kjr.2022.0299
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author Kim, Minjae
Lee, Jeong Hyun
Joo, Leehi
Jeong, Boryeong
Kim, Seonok
Ham, Sungwon
Yun, Jihye
Kim, NamKug
Chung, Sae Rom
Choi, Young Jun
Baek, Jung Hwan
Lee, Ji Ye
Kim, Ji-hoon
author_facet Kim, Minjae
Lee, Jeong Hyun
Joo, Leehi
Jeong, Boryeong
Kim, Seonok
Ham, Sungwon
Yun, Jihye
Kim, NamKug
Chung, Sae Rom
Choi, Young Jun
Baek, Jung Hwan
Lee, Ji Ye
Kim, Ji-hoon
author_sort Kim, Minjae
collection PubMed
description OBJECTIVE: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. RESULTS: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62–0.89) and 0.77 (95% CI, 0.65–0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40–0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40–0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39–0.67], p = 0.021) in the external validation set. CONCLUSION: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.
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spelling pubmed-96142902022-11-03 Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma Kim, Minjae Lee, Jeong Hyun Joo, Leehi Jeong, Boryeong Kim, Seonok Ham, Sungwon Yun, Jihye Kim, NamKug Chung, Sae Rom Choi, Young Jun Baek, Jung Hwan Lee, Ji Ye Kim, Ji-hoon Korean J Radiol Neuroimaging and Head & Neck OBJECTIVE: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. RESULTS: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62–0.89) and 0.77 (95% CI, 0.65–0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40–0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40–0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39–0.67], p = 0.021) in the external validation set. CONCLUSION: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment. The Korean Society of Radiology 2022-11 2022-09-16 /pmc/articles/PMC9614290/ /pubmed/36126954 http://dx.doi.org/10.3348/kjr.2022.0299 Text en Copyright © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Neuroimaging and Head & Neck
Kim, Minjae
Lee, Jeong Hyun
Joo, Leehi
Jeong, Boryeong
Kim, Seonok
Ham, Sungwon
Yun, Jihye
Kim, NamKug
Chung, Sae Rom
Choi, Young Jun
Baek, Jung Hwan
Lee, Ji Ye
Kim, Ji-hoon
Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
title Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
title_full Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
title_fullStr Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
title_full_unstemmed Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
title_short Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma
title_sort development and validation of a model using radiomics features from an apparent diffusion coefficient map to diagnose local tumor recurrence in patients treated for head and neck squamous cell carcinoma
topic Neuroimaging and Head & Neck
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614290/
https://www.ncbi.nlm.nih.gov/pubmed/36126954
http://dx.doi.org/10.3348/kjr.2022.0299
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