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Background/Objectives: To explore the prognostic potential of gross tumor volume (GTV)-based delta-radiomic features from CT simulation scans in patients with locally advanced unresectable vulvar cancer. Methods: A total of 21 patients (between 2019 and 2024) undergoing definitive radiotherapy were included, with baseline and post-phase I (after 25 fractions) CT simulation scans analyzed. Radiomic features (n = 107) were extracted from GTVs using PyRadiomics, and delta features were calculated as the relative change between scans. A multi-step selection pipeline (univariable Cox screening (p < 0.10), correlation filtering, and Lasso–Cox) was applied for each endpoint: local control (LC), regional control, distant metastasis-free survival, progression-free survival, and overall survival (OS). Model discrimination was assessed via 500-iteration bootstrapped concordance index (C-index), and calibration was plotted at 24 months. Results: Median follow-up was 50.0 months. The 2-year LC and OS rates were 56.2% and 55.9%, respectively. Final multivariable models retained a sole texture Δ feature for LC (HR = 2.62, 95% CI = 1.05–6.52, p = 0.039; C-index = 0.748) and six Δ features for OS (C-index = 0.864). No features were retained for other endpoints. For LC, increased run-length non-uniformity after phase I predicted poorer control. For OS, increased texture/shape complexity predicted worse survival, whereas increased uniformity predicted better survival. Conclusions: CT-based delta-radiomic features, particularly shape and texture metrics, may predict LC and OS in unresectable vulvar cancer. Despite the small sample size, these findings highlight the potential for delta-radiomics as a noninvasive biomarker for risk stratification. Validation in larger cohorts and exploring potential in adaptive radiotherapy are warranted.
Alzibdeh et al. (Sun,) studied this question.