Multivariate feature analysis of early-stage laryngeal cancer serum components using surface-enhanced Raman spectroscopy

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 89.33% accuracy for CNN-attention model

• Primary Outcome: Patient-level diagnostic accuracy for early laryngeal cancer detection

• Nnt Or Sensitivity: Sensitivity and specificity not reported - only overall accuracy provided

• Confidence Interval: Not reported

Clinical Application

Limited by requirement for specialized SERS equipment and technical expertise not widely available Would require TGA approval for diagnostic use. Limited availability of SERS technology in Australian laboratories. Could complement existing head and neck cancer pathways if validated High-risk patients requiring early laryngeal cancer screening, particularly those with suspicious symptoms

Abstract

Laryngeal cancer is a common head-and-neck malignant tumor with geographically variable incidence. Its lack of specific early clinical symptoms often causes missed diagnosis, leading to most cases being identified at intermediate/advanced stages and thus poor treatment efficacy and prognosis. Early, accurate screening for high-risk populations is therefore critical to improving survival. This study systematically investigates the clinical application value of surface-enhanced Raman spectroscopy (SERS) combined with deep learning models for serum screening in early laryngeal cancer. It specifically examines the discriminative performance of one-dimensional convolutional neural network (1D-CNN) models, including CNN-attention with a fused attention mechanism and CNN-baseline without an attention mechanism, and compares these with traditional machine learning models.Serum samples were collected from three groups: Early laryngeal cancer, vocal fold polyps, and healthy controls. SERS was used to obtain serum molecular vibration fingerprint spectra. After spectral preprocessing, five models (CNN-attention, CNN-baseline, SVM, AdaBoost, KNN) were established to discriminate the three groups and compare performance. Leave-one-patient-out cross-validation (LOPO-CV) results indicate that the CNN-attention and CNN-baseline models significantly outperform other models in discrimination efficacy. Specifically, the CNN-attention model achieves a patient-level accuracy of 89.33%, while the CNN-baseline model reaches 88.67%. SVM, KNN, and AdaBoost had inferior performance. This study confirms that serum SERS-based 1D-CNN provides an efficient, accurate method for early laryngeal cancer screening, laying a foundation for clinical translation and offering new technical support for early screening.