A blood-based metabolite panel for distinguishing ovarian cancer from benign pelvic masses.

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Abstract

To assess the contributions of circulating metabolites for improving upon the performance of the Risk of Ovarian Malignancy Algorithm (ROMA) for risk prediction of ovarian cancer (OvCa) among women with ovarian cysts.Metabolomic profiling was performed on an initial set of sera from 101 serous and non-serous OvCa cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites (diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid) was developed for distinguishing early-stage OvCa from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 OvCa cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA was further assessed.A 7-marker metabolite panel (7MetP) developed in the Training Set yielded an AUC of 0.86 (95% CI: 0.76-0.95) for early-stage OvCa in the independent Test Set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84-0.98) for early-stage OvCa in the Test Set, which was improved compared to ROMA alone (0.91 (95% CI: 0.84-0.98); likelihood ratio test p-value:.03). In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs 0.52; 1-sided p<.001) with improved specificity (0.89 vs 0.78; 1-sided p<.001) for early-stage OvCa compared to ROMA alone.A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage OvCa from benign disease to better inform clinical decision making.