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A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study

PURPOSE: To develop and validate a nomogram model combining radiomic features and clinical characteristics to preoperatively predict the risk of early relapse (ER) in advanced sinonasal squamous cell carcinomas (SNSCCs). METHODS: A total of 152 SNSCC patients (clinical stage III-IV) who underwent di...

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Autores principales: Lin, Naier, Yu, Sihui, Lin, Mengyan, Shi, Yiqian, Chen, Wei, Xia, Zhipeng, Cheng, Yushu, Sha, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149576/
https://www.ncbi.nlm.nih.gov/pubmed/35651794
http://dx.doi.org/10.3389/fonc.2022.870935
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author Lin, Naier
Yu, Sihui
Lin, Mengyan
Shi, Yiqian
Chen, Wei
Xia, Zhipeng
Cheng, Yushu
Sha, Yan
author_facet Lin, Naier
Yu, Sihui
Lin, Mengyan
Shi, Yiqian
Chen, Wei
Xia, Zhipeng
Cheng, Yushu
Sha, Yan
author_sort Lin, Naier
collection PubMed
description PURPOSE: To develop and validate a nomogram model combining radiomic features and clinical characteristics to preoperatively predict the risk of early relapse (ER) in advanced sinonasal squamous cell carcinomas (SNSCCs). METHODS: A total of 152 SNSCC patients (clinical stage III-IV) who underwent diffusion-weighted imaging (DWI) were included in this study. The training cohort included 106 patients assessed at the headquarters of our hospital using MR scanner 1. The testing cohort included 46 patients assessed at the branch of our hospital using MR scanner 2. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomic signature (radscore) construction. Multivariable logistic regression analysis was applied to identify independent predictors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). Furthermore, the patients were classified into high- or low-risk ER subgroups according to the optimal cutoff value of the nomogram using X-tile. The recurrence-free survival probability (RFS) of each subgroup was assessed. RESULTS: ER was noted in 69 patients. The radscore included 8 selected radiomic features. The radscore, T stage and surgical margin were independent predictors. The nomogram showed better performance (AUC = 0.92) than either the radscore or the clinical factors in the training cohort (P < 0.050). In the testing cohort, the nomogram showed better performance (AUC = 0.92) than the clinical factors (P = 0.016) and tended to show better performance than the radscore (P = 0.177). The nomogram demonstrated good calibration and clinical utility. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (P < 0.001). CONCLUSIONS: The ADC-based radiomic nomogram model is potentially useful in predicting the risk of ER in advanced SNSCCs.
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spelling pubmed-91495762022-05-31 A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study Lin, Naier Yu, Sihui Lin, Mengyan Shi, Yiqian Chen, Wei Xia, Zhipeng Cheng, Yushu Sha, Yan Front Oncol Oncology PURPOSE: To develop and validate a nomogram model combining radiomic features and clinical characteristics to preoperatively predict the risk of early relapse (ER) in advanced sinonasal squamous cell carcinomas (SNSCCs). METHODS: A total of 152 SNSCC patients (clinical stage III-IV) who underwent diffusion-weighted imaging (DWI) were included in this study. The training cohort included 106 patients assessed at the headquarters of our hospital using MR scanner 1. The testing cohort included 46 patients assessed at the branch of our hospital using MR scanner 2. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomic signature (radscore) construction. Multivariable logistic regression analysis was applied to identify independent predictors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA). Furthermore, the patients were classified into high- or low-risk ER subgroups according to the optimal cutoff value of the nomogram using X-tile. The recurrence-free survival probability (RFS) of each subgroup was assessed. RESULTS: ER was noted in 69 patients. The radscore included 8 selected radiomic features. The radscore, T stage and surgical margin were independent predictors. The nomogram showed better performance (AUC = 0.92) than either the radscore or the clinical factors in the training cohort (P < 0.050). In the testing cohort, the nomogram showed better performance (AUC = 0.92) than the clinical factors (P = 0.016) and tended to show better performance than the radscore (P = 0.177). The nomogram demonstrated good calibration and clinical utility. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (P < 0.001). CONCLUSIONS: The ADC-based radiomic nomogram model is potentially useful in predicting the risk of ER in advanced SNSCCs. Frontiers Media S.A. 2022-05-16 /pmc/articles/PMC9149576/ /pubmed/35651794 http://dx.doi.org/10.3389/fonc.2022.870935 Text en Copyright © 2022 Lin, Yu, Lin, Shi, Chen, Xia, Cheng and Sha https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Lin, Naier
Yu, Sihui
Lin, Mengyan
Shi, Yiqian
Chen, Wei
Xia, Zhipeng
Cheng, Yushu
Sha, Yan
A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study
title A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study
title_full A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study
title_fullStr A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study
title_full_unstemmed A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study
title_short A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study
title_sort clinical-radiomics nomogram based on the apparent diffusion coefficient (adc) for individualized prediction of the risk of early relapse in advanced sinonasal squamous cell carcinoma: a 2-year follow-up study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149576/
https://www.ncbi.nlm.nih.gov/pubmed/35651794
http://dx.doi.org/10.3389/fonc.2022.870935
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