Texture Analysis Using Preoperative Positron Emission Tomography Images May Predict the Prognosis of Patients With...

Texture analysis is a computer-assisted technique used to measure intratumoral heterogeneity, which is known to have important roles in cancer research. This study aimed to assess the potential prognostic values of textural features extracted from preoperative ¹⁸F-fluorodeoxyglucose positron emission tomography images in patients with resectable oral squamous cell carcinoma.

This retrospective cohort study included patients with oral squamous cell carcinoma who underwent resection surgery. We extracted 31 textural indices from preoperative positron emission tomography images. Overall survival (OS) and disease-free survival (DFS) were chosen as the primary outcome variables, and the primary predictor variables were age, sex, primary tumor location, pathological T and N classification, histologic differentiation, resected margin, perineural and lymphovascular invasion, maximum standardized uptake value, and the 14 textural indices selected in the factor analysis. We analyzed OS and DFS using Kaplan-Meier curves, and the differences between survival curves were determined using a log-rank test. The independent prognostic factors were assessed using the Cox-proportional hazards model.

We enrolled 81 patients (median age, 67.3 years; range, 32 to 88 years). The median follow-up duration was 50.1 months (range, 6.3 to 133.7 months). The univariable and multivariable analyses revealed that higher entropy values (≥1.91) were associated with worse OS (hazard ratio, 21.49; 95% confidence interval, 1.36 to 340.71; P = .03) and DFS (hazard ratio, 50.69; 95% confidence interval, 5.23 to 491.18; P = .001).

This study showed that entropy is a statistically significant prognostic factor of both OS and DFS. Texture analysis using preoperative positron emission tomography images may contribute to risk stratification.