CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy

Fellows’ Journal Club

This was a retrospective study including 62 patients diagnosed with primary head and neck squamous cellcarcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of thewhole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Three histogram features (geometric mean, harmonic, and fourth moment) and 4 gray-level run-length features (short-run emphasis, gray-level nonuniformity, run-length nonuniformity, and short-run low gray-level emphasis) were significant predictors of outcome.

Abstract

Figure 1 from paper
Representative axial contrast-enhanced CT images (A and C) and corresponding axial section ROI mask-segmented primary tumor (B and D) for 2 different patients with oropharyngeal squamous cell carcinoma. Segmented tumor is from a 75-year-old man (B) with right tonsil squamous cell carcinoma (HPV-positive; smoking, 0 pack-year; tumor volume, 16.4 mL; clinical T-stage, T4) who developed local failure and a 43-year-old woman (D) with squamous cell carcinoma of the right base of tongue (HPV- positive; smoking, 0 pack-year; tumor volume, 20.6 mL; clinical T-stage, T4) who showed local control. Representative texture features of each patients are as follows; for geometric mean, 973.2 (local failure, B) and 906.6 (local control, D); for harmonic mean, 285.1 (local failure, B) and 210.3 (local control, D); for SRE, 0.026 (local failure, B) and 0.043 (local control, D); for GLN, 0.026 (local failure, B) and 0.042 (local control, D); for RLN, 0.019 (local failure, B) and 0.032 (local control, D); and for SRLGE, 479.1 (local failure, B) and 459.1 (local control, D).

BACKGROUND AND PURPOSE

The accurate prediction of prognosis and failure is crucial for optimizing treatment strategies for patients with cancer. The purpose of this study was to assess the performance of pretreatment CT texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma treated with chemoradiotherapy.

MATERIALS AND METHODS

This retrospective study included 62 patients diagnosed with primary head and neck squamous cell carcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of the whole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Receiver operating characteristic analysis was used to identify the optimal threshold of any significant texture parameter. We used multivariate Cox proportional hazards models to examine the association between the CT texture parameter and local failure, adjusting for age, sex, smoking, primary tumor stage, primary tumor volume, and human papillomavirus status.

RESULTS

Twenty-two patients (35.5%) developed local failure, and the remaining 40 (64.5%) showed local control. Multivariate analysis revealed that 3 histogram features (geometric mean [hazard ratio = 4.68, P = .026], harmonic mean [hazard ratio = 8.61, P = .004], and fourth moment [hazard ratio = 4.56, P = .048]) and 4 gray-level run-length features (short-run emphasis [hazard ratio = 3.75, P = .044], gray-level nonuniformity [hazard ratio = 5.72, P = .004], run-length nonuniformity [hazard ratio = 4.15, P = .043], and short-run low gray-level emphasis [hazard ratio = 5.94, P = .035]) were significant predictors of outcome after adjusting for clinical variables.

CONCLUSIONS

Independent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

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CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy
jross
Jeffrey Ross • Mayo Clinic, Phoenix

Dr. Jeffrey S. Ross is a Professor of Radiology at the Mayo Clinic College of Medicine, and practices neuroradiology at the Mayo Clinic in Phoenix, Arizona. His publications include over 100 peer-reviewed articles, nearly 60 non-refereed articles, 33 book chapters, and 10 books. He was an AJNR Senior Editor from 2006-2015, is a member of the editorial board for 3 other journals, and a manuscript reviewer for 10 journals. He became Editor-in-Chief of the AJNR in July 2015. He received the Gold Medal Award from the ASSR in 2013.

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