Evidence map›Paper›PMID 34123488›Full record

ArticleBiomedical optics express2021

Machine learning-based LIBS spectrum analysis of human blood plasma allows ovarian cancer diagnosis.

Zengqi Yue, Chen Sun, Fengye Chen, Yuqing Zhang, Weijie Xu, Sahar Shabbir, Long Zou, Weiguo Lu, Wei Wang, Zhenwei Xie and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

13 authors.

Zengqi YueSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Chen SunSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Fengye ChenSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Yuqing ZhangSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Weijie XuSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Sahar ShabbirSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Long ZouSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.
Weiguo LuCenter for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China.
Wei WangDepartment of Clinical Laboratory, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China.
Zhenwei XieCenter for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China.
Lanyun ZhouCenter for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China.
Yan LuCenter for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Women's Reproductive Health Key Laboratory of Zhejiang Province, and Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310011, China.
Jin YuSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID https://orcid.org/0000-0003-3924-8858

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early-stage screening and diagnosis of ovarian cancer represent an urgent need in medicine. Usual ultrasound imaging and cancer antigen CA-125 test when prescribed to a suspicious population still require reconfirmations. Spectroscopic analyses of blood, at the molecular and atomic levels, provide useful supplementary tests when coupled with effective information extraction methods. Laser-induced breakdown spectroscopy (LIBS) was employed in this work to record the elemental fingerprint of human blood plasma. A machine learning data treatment process was developed combining feature selection and regression with a back-propagation neural network, resulting in classification models for cancer detection among 176 blood plasma samples collected from patients, including also ovarian cyst and normal cases. Cancer diagnosis sensitivity and specificity of respectively 71.4% and 86.5% were obtained for randomly selected validation samples.

Identifiers

PMID34123488
PMCPMC8176811

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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.