What type of study is this?

This is a Cohort Study study (also classified as: Validation Study).

December 7, 2025

A Clinically Inspired Deep Learning Framework for Laryngeal Cancer Detection and Malignancy Transformation Risk Assessment

Key Points

TransCL achieved a performance AUC of 0.978, indicating superior accuracy in laryngeal cancer detection.
Results demonstrated a Matthews Correlation Coefficient of 0.843, reflecting strong predictive capabilities for malignant transformation.
Assessment involved a retrospective study of laryngoscopic images to train and validate the deep learning classification network.
Clinical applications of TransCL suggest potential integration into diagnostic workflows, enhancing laryngologists' decision-making.

Abstract

Abstract Objective Laryngeal cancer (LCA) is a prevalent head and neck malignancy. However, laryngoscopic image interpretation remains experience‐dependent, resulting in diagnostic variability, particularly in resource‐limited settings. This study aimed to develop a deep learning model to enhance lesion detection, guide biopsies, and streamline diagnostic processes. Study Design Retrospective study of a classification network. Setting Five medical centers in China. Methods A dataset comprising 16,587 laryngoscopic images from 2585 patients representing 10 distinct laryngeal lesions was established. First, TransCL was developed and validated on multiple independent test sets in comparison with existing models. Second, the model was evaluated on unseen lesion types to assess its generalizability. Third, its ability to predict malignant transformation in benign lesions was assessed against expert annotations. Finally, TransCL predictions were compared with diagnoses made by laryngologists. Results TransCL demonstrated outstanding performance across all test sets, achieving an area under the curve of 0.978 (0.974‐0.982), accuracy of 0.942 (0.938‐0.945), F1‐score of 0.961 (0.959‐0.963), and Matthews Correlation Coefficient of 0.843 (0.831‐0.855) in the internal validation. Performance remained robust in external validations, outperforming existing models. The model demonstrated strong generalization to unseen lesion types and reliably predicted malignant transformation risk in benign lesions. Compared with human experts, TransCL outperformed junior laryngologists and several senior laryngologists, achieving performance achieving performance comparable to that of top senior laryngologists. Conclusions TransCL demonstrated high robustness, accuracy, and generalizability, with diagnostic performance comparable to that of experienced clinicians in LCA detection and lesion risk assessment, suggesting its potential for clinical applications.

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