Evidence map›Paper›PMID 40592356›Full record

ArticleObstetrics & gynecology science2025

Identification of volatile organic compound as a novel modality for cervical cancer detection.

Muhammad Ary Zucha, Dian Kesumapramudya Nurputra, Ardhanu Kusumanto, Shidiq Nur Hidayat, Kuwat Triyana, Widha Apriani Priadi, Nadia Yasmine, Ova Emilia

Abstract read
In one paragraph

Article in Obstetrics & gynecology science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Muhammad Ary ZuchaDepartment of Obstetrics and Gynecology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia.
Dian Kesumapramudya NurputraDepartment of Child Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia.
Ardhanu KusumantoDepartment of Obstetrics and Gynecology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia.
Shidiq Nur HidayatDepartment of Physics, Biomedical Science, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia.
Kuwat TriyanaDepartment of Physics, Biomedical Science, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia.
Widha Apriani PriadiDepartment of Obstetrics and Gynecology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia.
Nadia YasmineFaculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Ova EmiliaDepartment of Obstetrics and Gynecology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia.

Funding

Universitas Gadjah Mada 10529/UN1/P4/PT.01.03
6 · The paper itself

Abstract

objectiveIn this study, we developed a novel detection strategy based on volatile organic compound (VOC) sensing. Persistent infection by human papillomavirus (HPV) may cause biological changes in the cervical epithelium, leading to VOC production.

methodsThis study included 200 urine samples from cervical cancer patients and controls that were HPV-negative. Urine samples were collected and measured using a gas sensor array composed of a matrix of 10 sensors. For each analyzed sample, the instrument produced a vector signal encoding the VOC emitted from the urine (urine prints). The urine prints of cervical cancer patients were differentiated from those of healthy controls.

resultsIdentification of VOC for cervical cancer detection showed reliable accuracy (91% sensitivity, 85% specificity, and 89% accuracy).

conclusionOur.

resultsdemonstrated the applicability of VOC sensing for cervical cancer detection and its potential application in treatment monitoring.

Indexed as

Artificial intelligenceCancer screening testCervical cancerVolatile organic compounds

Identifiers

PMID40592356
PMCPMC12301559

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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.