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
PurposeTo 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).MethodsA total of 152 SNSCC patients (clinical stage III-IV) who underwent diffusi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.870935/full |
| _version_ | 1828365185962737664 |
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| author | Naier Lin Sihui Yu Mengyan Lin Yiqian Shi Wei Chen Zhipeng Xia Yushu Cheng Yan Sha |
| author_facet | Naier Lin Sihui Yu Mengyan Lin Yiqian Shi Wei Chen Zhipeng Xia Yushu Cheng Yan Sha |
| author_sort | Naier Lin |
| collection | DOAJ |
| description | PurposeTo 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).MethodsA 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.ResultsER 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).ConclusionsThe ADC-based radiomic nomogram model is potentially useful in predicting the risk of ER in advanced SNSCCs. |
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| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-14T05:27:29Z |
| publishDate | 2022-05-01 |
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| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-aa65328677a4415896aa1407df92e8092022-12-22T02:09:56ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-05-011210.3389/fonc.2022.870935870935A 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 StudyNaier LinSihui YuMengyan LinYiqian ShiWei ChenZhipeng XiaYushu ChengYan ShaPurposeTo 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).MethodsA 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.ResultsER 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).ConclusionsThe ADC-based radiomic nomogram model is potentially useful in predicting the risk of ER in advanced SNSCCs.https://www.frontiersin.org/articles/10.3389/fonc.2022.870935/fullsinonasal cancerrecurrenceapparent diffusion coefficientradiomicsnomogram |
| spellingShingle | Naier Lin Sihui Yu Mengyan Lin Yiqian Shi Wei Chen Zhipeng Xia Yushu Cheng Yan Sha 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 Frontiers in Oncology sinonasal cancer recurrence apparent diffusion coefficient radiomics nomogram |
| 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 | sinonasal cancer recurrence apparent diffusion coefficient radiomics nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.870935/full |
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