JMIR Publications

ABSTRACT

Background:

Cervical nodal metastasis remains one of the strongest determinants of treatment planning and prognosis in oral squamous cell carcinoma. Histopathology remains the reference standard, but it does not provide immediate information at the point of surgical specimen assessment. Elastic scattering spectroscopy (ESS) offers rapid, label-free optical interrogation of tissue microstructure and may support ex vivo nodal assessment.

Objective:

To evaluate the diagnostic accuracy of elastic scattering spectroscopy combined with machine-learning classification for ex vivo detection of cervical lymph node metastasis in patients with oral squamous cell carcinoma.

Methods:

We conducted a single-centre observational ex vivo diagnostic modelling study in patients with oral squamous cell carcinoma undergoing primary tumour resection and neck dissection. Three ESS spectra were acquired from each bivalved lymph node before routine histopathological processing. Internal validation used repeated nested 5-fold cross-validation grouped by patient, so every spectrum and lymph node from a patient remained in the same fold. All preprocessing, scaling, dimensionality reduction, within-model-family configuration and hyperparameter tuning, probability calibration where required, and decision-threshold selection were restricted to training data within the nested validation process; comparisons between candidate model families were exploratory and were based on pooled patient-grouped out-of-fold predictions. Spectrum-level probabilities were aggregated within each lymph node, and histopathology was the reference standard.

Results:

Fifty-two patients were assessed for eligibility, 45 were included, and 280 fresh lymph nodes were assessed by ESS; 96 were pathology positive and 184 were pathology negative. Of 840 acquired spectra, 760 passed quality control. Two hundred and seventy-four of 280 nodes retained at least 2 usable spectra and comprised the analytical cohort; 6 pathology-negative nodes yielded no analysable node-level ESS result. The hybrid model was selected because it had the highest pooled patient-grouped out-of-fold node-level ROC AUC, with an internally validated, exploratory estimate of 0.966 (95% CI 0.929–0.988). The same out-of-fold predictions informed model-family selection and performance reporting; this estimate does not represent independent validation of a prospectively fixed final model. At the cross-validation-derived summary threshold, secondary descriptive operating characteristics were sensitivity 93.8% (95% CI 86.4%-97.6%), specificity 95.5% (95% CI 90.1%-98.3%), accuracy 94.9% (95% CI 90.4%-97.3%), positive predictive value 91.8% (95% CI 82.9%-96.3%), and negative predictive value 96.6% (95% CI 92.4%-98.8%). The Brier score was 0.063, with calibration-in-the-large of –0.03 and a calibration slope of 0.94. Patient-level sensitivity was 96.0% and specificity was 90.0%. Pathology-positive and pathology-negative nodes also showed differences across amplitude-related and shape-related spectral features.

Conclusions:

ESS showed promising ex vivo discrimination of metastatic cervical lymph nodes in this single-centre model-development and internal-validation study. The findings support further investigation of ESS as a potential pathology-triage adjunct rather than a replacement for histopathology.