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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9149576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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