CT Texture Analysis: Defining and Integrating New Biomarkers for Advanced Oncologic Imaging in Precision Medicine: A Comment on “CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy”

M Becker

doi:10.3174/ajnr.a5451

Abstract

The remarkable article by Kuno et al[1][1] in this issue of the American Journal of Neuroradiology raises several questions: Can texture analysis provide reliable biomarkers to predict treatment success in head and neck squamous cell carcinoma (HNSCC)? If so, will the role of the expert clinical

Highlights

The remarkable article by Kuno et al[1] in this issue of the American Journal of Neuroradiology raises several questions: Can texture analysis provide reliable biomarkers to predict treatment success in head and neck squamous cell carcinoma (HNSCC)? If so, will the role of the expert clinical radiologist who visually recognizes and interprets image patterns in combination with the clinical impression soon be obsolete and replaced by an increasingly ubiquitous and cheap computing infrastructure for mathematic image analysis, or will radiologists play an even more important role in the future by integrating these new biomarkers for treatment response with their expert knowledge?
The authors found that 3 histogram features and 4 gray-level run-length features were significant predictors of outcome after adjusting for clinical variables, including smoking history, human papillomavirus (HPV) status, T-stage, and tumor volume.[1]
In supraglottic HNSCC, preradiotherapy CT-based tumor volume obtained by manual contouring allowed stratification of patients into groups with likely and less likely local control.[2]

Summary

Introduction

The remarkable article by Kuno et al[1] in this issue of the American Journal of Neuroradiology raises several questions: Can texture analysis provide reliable biomarkers to predict treatment success in head and neck squamous cell carcinoma (HNSCC)? If so, will the role of the expert clinical radiologist who visually recognizes and interprets image patterns in combination with the clinical impression soon be obsolete and replaced by an increasingly ubiquitous and cheap computing infrastructure for mathematic image analysis, or will radiologists play an even more important role in the future by integrating these new biomarkers for treatment response with their expert knowledge?. The remarkable article by Kuno et al[1] in this issue of the American Journal of Neuroradiology raises several questions: Can texture analysis provide reliable biomarkers to predict treatment success in head and neck squamous cell carcinoma (HNSCC)?

Results

Conclusion