Contrast-Enhanced CT-Based Deep Learning and Habitat Radiomics for Analysing the Predictive Capability for Oral Squamous Cell Carcinoma

• Published in: International dental journal Vol. 75; no. 5; p. 100914
• Main Authors: Liu, Qilin, Liang, Zhuang, Qi, Xiaoshuang, Yang, Shuwen, Fu, Binyang, Dong, Hui
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.10.2025; Elsevier
• Subjects: Deep learning; Dentistry; Oral squamous cell carcinoma; Precision medicine; Radiomics; Scientific Research Report; CNN; DL; CECT; LNM-DL Model; IBSI; Radiomics; Deep learning; LNM-H Model; Precision medicine; LNM-C Model; LNM-Cli model; LBP; OSCC; ROC; AI; P-C Model; Oral squamous cell carcinoma; SLNB; ROI; GLCM; P-Cli Model; FCN; P-H Model; CLNM; P-DL Model; sentinel lymph node biopsy; Lymph Node Metastasis Deep Learning Model; oral squamous cell cancer; Lymph Node Metastasis Habitat Model; Pathological Typing Deep Learning Model; Pathological Typing Clinical Model; pathological typing combined model; artificial intelligence; cervical lymph node metastasis; Lymph Node Metastasis Clinical Model; fully connected neural network; local binary patterns; receiver operating characteristic; Lymph Node Metastasis Combined model; contrast-enhanced CT; gray-level co-occurrence matrix; regions of interest; convolutional neural network; Imaging Biomarker Standardisation Initiative; Pathological Typing Habitat Model

This study aims to explore a novel approach for predicting cervical lymph node metastasis (CLNM) and pathological subtypes in oral squamous cell carcinoma (OSCC) by comparing deep learning (DL) and habitat analysis models based on contrast-enhanced CT (CECT). A retrospective analysis was conducted u...