Evidence map›Paper›PMID 36232778›Full record

ArticleInternational journal of molecular sciences2022

Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning.

Mingmei Ji, Jiahui Zhong, Runzhe Xue, Wenhua Su, Yawei Kong, Yiyan Fei, Jiong Ma, Yulan Wang, Lan Mi

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

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
5.8field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Mingmei JiDepartment of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, 220 Handan Road, Shanghai 200433, China.
Jiahui ZhongInstitute of Biomedical Engineering and Technology, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Runzhe XueDepartment of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, 220 Handan Road, Shanghai 200433, China.
Wenhua SuDepartment of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, 220 Handan Road, Shanghai 200433, China.
Yawei KongDepartment of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, 220 Handan Road, Shanghai 200433, China.
Yiyan FeiDepartment of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, 220 Handan Road, Shanghai 200433, China.ORCID 0000-0002-2413-130X
Jiong MaDepartment of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, 220 Handan Road, Shanghai 200433, China.ORCID 0000-0003-3632-4624
Yulan WangDepartment of Gynecology and Obstetrics, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Street, Wuhan 430014, China.
Lan MiDepartment of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, 220 Handan Road, Shanghai 200433, China.ORCID 0000-0002-2320-2225
Fudan University · CNCentral Hospital of Wuhan · CN

Funding

Fudan University-CIOMP Joint Fund FC2020-004Medical Engineering Fund of Fudan University yg2021-022National Key R&D Program of China 2021YFF0502900National Natural Science Foundation of China 62175036, 62175034, 82030106Pioneering Project of Academy for Engineering and Technology, Fudan University gyy2018-001, gyy2018-002Science and Technology Research Program of Shanghai 19DZ2282100Shanghai Key Discipline Construction Plan (2020-2022) GWV-10.1-XK01Shanghai Natural Science Foundation 20ZR1405100, 20ZR1403700, 20JC1410900
6 · The paper itself

Abstract

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.

Indexed as

NADUterine Cervical NeoplasmsEarly Detection of CancerFemaleHumansMicroscopy, FluorescenceNADPNeoplasm Recurrence, LocalPhosphatesUnsupervised Machine LearningNADNADPPhosphatescervical cancerfluorescence lifetime imaging microscopyNAD(P)Hnon-invasive screeningunsupervised machine learning

Identifiers

PMID36232778
PMCPMC9570424
OpenAlexW4298002708

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.