Document Type
Article
Publication Date
4-23-2024
Abstract
INTRODUCTION: This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients.
METHODS: Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers.
RESULTS AND DISCUSSION: Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.
Recommended Citation
Ling, Xiao; Alexander, Gregory S.; Molitoris, Jason; Choi, Jinhyuk; Schumaker, Lisa; Tran, Phuoc; Mehra, Ranee; Gaykalova, Daria; and Ren, Lei, "Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights From Oral Cavity Squamous Cell Carcinoma Preoperative CT Scans" (2024). Department of Radiation Oncology Faculty Papers. Paper 193.
https://jdc.jefferson.edu/radoncfp/193
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
38715772
Language
English
Comments
This article, first published by Frontiers Media, is the author's final published version in Frontiers in Oncology, Volume 14, 2024, Article number 1380599.
The published version is available at https://doi.org/10.3389/fonc.2024.1380599.
Copyright © 2024 Ling, Alexander, Molitoris, Choi, Schumaker, Tran, Mehra, Gaykalova and Ren