Cargando…
A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables
BACKGROUND: The efficacy of (18)F-fluorodeoxyglucose ((18)F-FDG) Positron Emission Tomography/Computed Tomography(PET/CT) in evaluating the neck status in clinically node-negative (cN0) oral squamous cell carcinoma(OSCC) patients was still unsatisfying. We tried to develop a prediction model for nod...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074690/ https://www.ncbi.nlm.nih.gov/pubmed/37016465 http://dx.doi.org/10.1186/s40644-023-00552-z |
_version_ | 1785019790100791296 |
---|---|
author | Xu, Feng Peng, Liling Feng, Junyi Zhu, Xiaochun Pan, Yifan Hu, Yuhua Gao, Xin Ma, Yubo He, Yue |
author_facet | Xu, Feng Peng, Liling Feng, Junyi Zhu, Xiaochun Pan, Yifan Hu, Yuhua Gao, Xin Ma, Yubo He, Yue |
author_sort | Xu, Feng |
collection | PubMed |
description | BACKGROUND: The efficacy of (18)F-fluorodeoxyglucose ((18)F-FDG) Positron Emission Tomography/Computed Tomography(PET/CT) in evaluating the neck status in clinically node-negative (cN0) oral squamous cell carcinoma(OSCC) patients was still unsatisfying. We tried to develop a prediction model for nodal metastasis in cN0 OSCC patients by using metabolic and pathological variables. METHODS: Consecutive cN0 OSCC patients with preoperative (18)F-FDG PET/CT, subsequent surgical resection of primary tumor and neck dissection were included. Ninety-five patients who underwent PET/CT scanning in Shanghai ninth people’s hospital were identified as training cohort, and another 46 patients who imaged in Shanghai Universal Medical Imaging Diagnostic Center were selected as validation cohort. Nodal-status-related variables in the training cohort were selected by multivariable regression after using the least absolute shrinkage and selection operator (LASSO). A nomogram was constructed with significant variables for the risk prediction of nodal metastasis. Finally, nomogram performance was determined by its discrimination, calibration, and clinical usefulness. RESULTS: Nodal maximum standardized uptake value(nodal SUVmax) and pathological T stage were selected as significant variables. A prediction model incorporating the two variables was used to plot a nomogram. The area under the curve was 0.871(Standard Error [SE], 0.035; 95% Confidence Interval [CI], 0.787–0.931) in the training cohort, and 0.809(SE, 0.069; 95% CI, 0.666–0.910) in the validation cohort, with good calibration demonstrated. CONCLUSIONS: A prediction model incorporates metabolic and pathological variables has good performance for predicting nodal metastasis in cN0 OSCC patients. However, further studies with large populations are needed to verify our findings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-023-00552-z. |
format | Online Article Text |
id | pubmed-10074690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100746902023-04-06 A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables Xu, Feng Peng, Liling Feng, Junyi Zhu, Xiaochun Pan, Yifan Hu, Yuhua Gao, Xin Ma, Yubo He, Yue Cancer Imaging Research Article BACKGROUND: The efficacy of (18)F-fluorodeoxyglucose ((18)F-FDG) Positron Emission Tomography/Computed Tomography(PET/CT) in evaluating the neck status in clinically node-negative (cN0) oral squamous cell carcinoma(OSCC) patients was still unsatisfying. We tried to develop a prediction model for nodal metastasis in cN0 OSCC patients by using metabolic and pathological variables. METHODS: Consecutive cN0 OSCC patients with preoperative (18)F-FDG PET/CT, subsequent surgical resection of primary tumor and neck dissection were included. Ninety-five patients who underwent PET/CT scanning in Shanghai ninth people’s hospital were identified as training cohort, and another 46 patients who imaged in Shanghai Universal Medical Imaging Diagnostic Center were selected as validation cohort. Nodal-status-related variables in the training cohort were selected by multivariable regression after using the least absolute shrinkage and selection operator (LASSO). A nomogram was constructed with significant variables for the risk prediction of nodal metastasis. Finally, nomogram performance was determined by its discrimination, calibration, and clinical usefulness. RESULTS: Nodal maximum standardized uptake value(nodal SUVmax) and pathological T stage were selected as significant variables. A prediction model incorporating the two variables was used to plot a nomogram. The area under the curve was 0.871(Standard Error [SE], 0.035; 95% Confidence Interval [CI], 0.787–0.931) in the training cohort, and 0.809(SE, 0.069; 95% CI, 0.666–0.910) in the validation cohort, with good calibration demonstrated. CONCLUSIONS: A prediction model incorporates metabolic and pathological variables has good performance for predicting nodal metastasis in cN0 OSCC patients. However, further studies with large populations are needed to verify our findings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-023-00552-z. BioMed Central 2023-04-05 /pmc/articles/PMC10074690/ /pubmed/37016465 http://dx.doi.org/10.1186/s40644-023-00552-z Text en © The Author(s) 2023, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Xu, Feng Peng, Liling Feng, Junyi Zhu, Xiaochun Pan, Yifan Hu, Yuhua Gao, Xin Ma, Yubo He, Yue A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables |
title | A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables |
title_full | A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables |
title_fullStr | A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables |
title_full_unstemmed | A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables |
title_short | A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables |
title_sort | prediction model of nodal metastasis in cn0 oral squamous cell carcinoma using metabolic and pathological variables |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10074690/ https://www.ncbi.nlm.nih.gov/pubmed/37016465 http://dx.doi.org/10.1186/s40644-023-00552-z |
work_keys_str_mv | AT xufeng apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT pengliling apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT fengjunyi apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT zhuxiaochun apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT panyifan apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT huyuhua apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT gaoxin apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT mayubo apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT heyue apredictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT xufeng predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT pengliling predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT fengjunyi predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT zhuxiaochun predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT panyifan predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT huyuhua predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT gaoxin predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT mayubo predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables AT heyue predictionmodelofnodalmetastasisincn0oralsquamouscellcarcinomausingmetabolicandpathologicalvariables |