ArticleClinical cancer research : an official journal of the American Association for Cancer Research2022
A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Dual Cas12a and multiplex crRNA CRISPR strategy ultrasensitive detection novel circRNA biomarker for the diagnosis of ovarian cancer.BMC cancer · 2025Article
- Decision tree model for predicting ovarian tumor malignancy based on clinical markers and preoperative circulating blood cells.BMC medical informatics and decision making · 2025Article
- Clinical utility of various liquid biopsy samples for the early detection of ovarian cancer: a comprehensive review.Frontiers in oncology · 2025Review
- Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer.Scientific reports · 2024Article
- A metabolite-based liquid biopsy for detection of ovarian cancer.Biomarker research · 2024Article
- Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).Oncology reports · 2024Review
- c-MYC-Driven Polyamine Metabolism in Ovarian Cancer: From Pathogenesis to Early Detection and Therapy.Cancers · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo 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 among women with ovarian cysts. EXPERIMENTAL
designMetabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer 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 ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed.
resultsA 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76-0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84-0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84-0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone.
conclusionsA blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.