Evidence map›Paper›PMID 38266403›Full record

ArticleGynecologic oncology2024

A personalized probabilistic approach to ovarian cancer diagnostics.

Dongjo Ban, Stephen N Housley, Lilya V Matyunina, L DeEtte McDonald, Victoria L Bae-Jump, Benedict B Benigno, Jeffrey Skolnick, John F McDonald

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Gynecologic oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 17 citations in OpenAlex.

  1. Trial
  2. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Dongjo BanIntegrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA.
Stephen N HousleyIntegrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA.
Lilya V MatyuninaIntegrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA.
L DeEtte McDonaldIntegrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA.
Victoria L Bae-JumpDepartment of Obstetrics and Gynecology, University of North Carolina, 3009 Old Clinic Building, Chapel Hill, NC 27599, USA.
Benedict B BenignoOvarian Cancer Institute, 1266 W. Paces Ferry Rd NW #339, Atlanta, GA 30327, USA.
Jeffrey SkolnickIntegrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA; Ovarian Cancer Institute, 1266 W. Paces Ferry Rd NW #339, Atlanta, GA 30327, USA; Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA.
John F McDonaldIntegrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA. Electronic address: john.mcdonald@biology.gatech.edu.
Georgia Institute of Technology · USCancer Research Center · USOvarian Cancer Research Alliance · USUniversity of North Carolina at Chapel Hill · US

Funding

Purchase of a GPU cluster for deep learning applications in protein-protein interaction and supercomplex prediction and biochemical literature annotation.R35GM118039 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI JEFFREY SKOLNICK · 2016 to 2026
$5.8M
NIGMS NIH HHS R35 GM118039
6 · The paper itself

Abstract

objectiveThe identification/development of a machine learning-based classifier that utilizes metabolic profiles of serum samples to accurately identify individuals with ovarian cancer.

methodsSerum samples collected from 431 ovarian cancer patients and 133 normal women at four geographic locations were analyzed by mass spectrometry. Reliable metabolites were identified using recursive feature elimination coupled with repeated cross-validation and used to develop a consensus classifier able to distinguish cancer from non-cancer. The probabilities assigned to individuals by the model were used to create a clinical tool that assigns a likelihood that an individual patient sample is cancer or normal.

resultsOur consensus classification model is able to distinguish cancer from control samples with 93% accuracy. The frequency distribution of individual patient scores was used to develop a clinical tool that assigns a likelihood that an individual patient does or does not have cancer.

conclusionsAn integrative approach using metabolomic profiles and machine learning-based classifiers has been employed to develop a clinical tool that assigns a probability that an individual patient does or does not have ovarian cancer. This personalized/probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests and represents a promising new direction in the early detection of ovarian cancer.

Indexed as

Ovarian NeoplasmsFemaleHumansMachine LearningMass SpectrometryMetabolomicsDiagnosticMachine learningMetabolomicsOvarian Cancer

Identifiers

PMID38266403
PMCPMC10960662
OpenAlexW4391155095

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.