Diagnostic Accuracy of Contrast-Enhanced CT Abdomen in the Differentiation of Tubercular and Malignant Omental or Peritoneal Thickening and the Development of a CT-Based Score
Abstract Background Differentiating tuberculous peritonitis (TBP) from peritoneal carcinomatosis (PC) remains challenging due to overlapping clinical presentations and imaging features. Contrast-enhanced computed tomography (CECT) is routinely used for evaluation; however, individual CT signs lack sufficient diagnostic accuracy. We aimed to assess the diagnostic performance of CECT in differentiating TBP and PC and to develop and validate a practical CT-based predictive score. Materials and Methods This retrospective diagnostic accuracy study included adult patients with omental or peritoneal thickening on CECT who underwent image-guided biopsy or had definitive clinical follow-up. Imaging features were systematically assessed by two radiologists. Multivariable logistic regression was used to identify independent predictors of PC, and a nomogram was constructed using age, gender, omental stranding, omental enhancement, and peritoneal enhancement. Model performance was evaluated using receiver operating characteristic analysis and externally validated in a prospective cohort. Clinically meaningful risk thresholds were derived from predicted probabilities. Results The derivation cohort consisted of 88 patients (52.3% PC), while the validation cohort comprised 48 patients (52.1% PC). Higher age, female gender, omental enhancement, absence of omental stranding, and absence of peritoneal enhancement were independent predictors of PC. The nomogram demonstrated excellent discrimination in the derivation cohort (area under the curve [AUC] 0.92) and strong performance in validation (AUC 0.85). Scores < 75 provided strong rule-out capability for malignancy, while scores ≥ 152 demonstrated excellent rule-in performance. Risk stratification accurately categorized patients into low-, intermediate-, and high-probability groups for malignancy. Conclusion A simple CT-based nomogram combining demographic and key imaging features reliably differentiates TBP from PC. This tool offers an interpretable, clinically actionable approach to guide biopsy prioritization and early management decisions, particularly in tuberculosis-endemic settings.