Chrome Extension
WeChat Mini Program
Use on ChatGLM

Predicting the Prognosis of Oral Tongue Carcinoma Using a Simple Quantitative Measurement Based on Preoperative MR Imaging: Tumor Thickness versus Tumor Volume.

AMERICAN JOURNAL OF NEURORADIOLOGY(2015)

Cited 16|Views5
No score
Abstract
BACKGROUND AND PURPOSE: Several studies indicated that tumor thickness or tumor volume might be helpful predictors for the prognosis of oral tongue squamous cell carcinoma. Our aim was to compare the value of tumor thickness versus tumor volume measurement based on preoperative MR imaging in predicting the prognosis of oral tongue squamous cell carcinoma, especially focusing on lymph node metastases and local recurrence. MATERIALS AND METHODS: Clinical, pathologic, and imaging data of patients with 46 oral tongue squamous cell carcinomas were retrospectively studied. Logistic regression analysis was used to evaluate the prognostic value of tumor thickness and tumor volume based on MR imaging. Receiver operating characteristic analysis was applied for the optimal cutoff value for the identified risk variable for prognosis. RESULTS: A higher intraclass correlation coefficient was achieved for the measurement of tumor thickness compared with tumor volume (0.990 versus 0.972). Multivariate analysis showed that tumor thickness was a significant predictor of lymph node metastases (P = .024), while tumor volume was not a significant predictor of either lymph node metastases or local recurrence (P > .05). Receiver operating characteristic results indicated that setting a tumor thickness of 8.5 mm as a cutoff value could achieve the optimal diagnostic efficiency for predicting lymph node metastases (area under the curve, 0.753; sensitivity, 0.889; specificity, 0.536). CONCLUSIONS: Tumor thickness based on preoperative MR imaging was useful in predicting the prognosis of oral tongue squamous cell carcinoma, especially lymph node metastases, in our patient population, while tumor volume was not.
More
Translated text
Key words
oral tongue carcinoma,preoperative mr imaging,tumor thickness,mr imaging
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined