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Prediction of Prognosis of Tongue Squamous Cell Carcinoma Based on Clinical MR Imaging Data Modeling

Authors :
Junjie Liu MD
Lina Song MD
Jingran Zhou MD
Mengxing Yu MD
Yan Hu PHD
Junyan Zhang PHD
Ping Song PHD
Yingjian Ye PHD
Jinsong Wang PHD
Guoyan Feng PHD
Hongyan Guo PHD
Peng An PHD
Source :
Technology in Cancer Research & Treatment, Vol 22 (2023)
Publication Year :
2023
Publisher :
SAGE Publishing, 2023.

Abstract

Objective: Tongue squamous cell carcinoma (TSCC) is one of the most common and poor prognosis head and neck tumors. The purpose of this study is to establish a model for predicting TSCC prognosis based on clinical and MR radiomics data and to develop a nomogram. Methods: A retrospective analysis was performed on the clinical and imaging data of 211 patients with pathologically confirmed TSCC who underwent radical surgery at xx hospital from February 2011 to January 2020. Patients were divided into a study group (recurrence, metastasis, and death, n = 76) and a control group (normal survival, n = 135) according to 1 to 6 years of follow-up. A training set and a test set were established based on a ratio of 7:3 and a time point. In the training set, 3 prediction models (clinical data model, imaging model, and combined model) were established based on the MR radiomics score (Radscore) combined with clinical features. The predictive performance of these models was compared using the Delong curve, and the clinical net benefit of the model was tested using the decision curve. Then, the external validation of the model was performed in the test set, and a nomogram for predicting TSCC prognosis was developed. Results: Univariate analysis confirmed that betel nut consumption, spicy hot pot or pickled food, unclean oral sex, drug use, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), depth of invasion (DOI), low differentiation, clinical stage, and Radscore were factors that affected TSCC prognosis ( P

Details

Language :
English
ISSN :
15330338
Volume :
22
Database :
Directory of Open Access Journals
Journal :
Technology in Cancer Research & Treatment
Publication Type :
Academic Journal
Accession number :
edsdoj.62f0d0dd3766484b93cfce1f323c7cd4
Document Type :
article
Full Text :
https://doi.org/10.1177/15330338231207006