Table 1.

Summary of literature on the value of radiomics in the accuracy of staging for laryngeal cancer

Author	Radiomics Software Used	Image Modality	Study Objective	Total Number of Laryngeal Cancer Patients (n)	Primary Treatment	Model Evaluation	Significant Radiomic Features	Conclusion
Wang et al22 a	Pyradiomics	CT	Determine whether CT radiomics could enhance the accuracy of T-staging in advanced laryngeal cancer	211 (TC: 150) (VC: 61)	Surgery	Single Institute Cohort divided into training and validation cohorts	Associated with T-Stage: First order features: Skewness, 2D First Order Mean Shape features: Least Axis Length, Sphericity Wavelet featuresb: LLH - First order Kurtosis, LLH - GLCM IDN, LLH First Order Median, LLL GLCM IMC	Developed a nomogram (combining the radiomic signature and T-stage reported by radiologists) with good accuracy (AUC: 0.892, 95% CI: 0.811–0.974) for T-staging.
Guo et al23	Radcloud Platform & Anaconda3 platform	CT	Determine whether CT radiomics could aid in the prediction of thyroid cartilage invasion from laryngeal and hypopharyngeal cancer	236	Surgery	Single Institute 5-fold Cross-Validation	four shape features seven first order features 5 GLRLM associated features 4 GLCM features 3 GLSZM features	The Radiomics-based models (AUC 0.905, 95% CI 0.863–0.937) were more accurate than a clinical radiologist alone (0.721, 95% CI: 0.663–0.774) in predicting thyroid cartilage invasion

AUC, area under curve; CT, Computed tomography, GLCM, Gray level co-occurrence matrix; GLRLM, gray level run-length matrix; GLSZM, Gray level size zone matrix; IDN, Inverse difference normalized; IMC, Informational measure of correlation; TR, Training cohort; VC, Validation cohort.

^aStudy evaluates laryngeal cancer patients exclusively.

^bFor wavelets where L and H are low- and high-frequency signals, respectively.