Abstract
BACKGROUND: Early and accurate triage of adnexal masses remains challenging due to the heterogeneous presentation of ovarian cancer and the fragmented nature of existing diagnostic tools. While several validated algorithms exist-such as NICE NG12, HSE, IOTA Simple Rules, O-RADS v2022, RMI2, and ROMA-each evaluates different aspects of risk, and none provide an integrated, clinically actionable output. We developed MOCRA (Multivariate Ovarian Cancer Risk Assessment), a deterministic, rule-based clinical decision support system (CDSS) designed to harmonize these tools into a unified risk stratification. METHODS: This retrospective, single-center diagnostic accuracy study included 68 analyzable patients with adnexal masses. MOCRA encoded six validated diagnostic algorithms using object-oriented architecture and combined their outputs through transparent precedence rules to produce a four-level risk classification (low, intermediate, high, indeterminate). Diagnostic performance was evaluated against physician-confirmed outcomes (histopathology or ≥ 6-month follow-up). Functional reliability was assessed using predefined test cases, and usability was evaluated by 15 gynecologic oncologists using the Post-Study System Usability Questionnaire (PSSUQ). RESULTS: Among 68 patients (7 malignant, 61 benign), MOCRA achieved an accuracy of 97.1%, sensitivity 100.0%, specificity 96.7%, F1-score 87.5%, and AUC 0.984. No malignancies occurred in the MOCRA low-risk category. Compared with single algorithms, MOCRA reduced false negatives while maintaining high specificity by cross-validating symptom, biomarker, and ultrasound signals. Functional testing confirmed deterministic and stable performance (mean reliability 4.8/5). Usability ratings were uniformly positive (overall PSSUQ score 4.6/5), with clinicians highlighting the interpretability of the four-tier risk level and the clarity provided by side-by-side algorithm outputs. CONCLUSIONS: MOCRA demonstrates strong diagnostic performance and high clinician usability in this pilot evaluation, suggesting that deterministic integration of multiple validated algorithms can improve consistency and reduce missed high-risk cases. However, the small, single-center dataset-particularly the limited number of malignant cases-warrants cautious interpretation. Larger multicenter and prospective studies with extended follow-up are needed to confirm generalizability and real-world clinical impact.