Evidence map›Paper›PMID 39558507›Full record

ArticleThoracic cancer2025

Optical sensor for fast and accurate lung cancer detection with tissue autofluorescence and diffuse reflectance spectroscopy.

Xianbei Yang, Anzhi Chen, Kaicheng U, Sophia Meixuan Zhang, Peihao Wang, Zheng Li, Yi Luo, Yong Cui

Abstract read
In one paragraph

Article in Thoracic cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

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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

8 authors.

Xianbei YangDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Anzhi ChenSchool of Computer Science and Technology, North China University of Technology, Beijing, China.
Kaicheng UDepartment of Computational Biology, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York, USA.
Sophia Meixuan ZhangDepartment of Biological Sciences, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York, USA.
Peihao WangDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Zheng LiDepartment of Agricultural and Resource Economics, College of Agriculture and Life Sciences, North Carolina State University, Raleigh, North Carolina, USA.
Yi LuoDepartment of Thoracic Surgery, University-Town Hospital of Chongqing Medical University, Chongqing, China.ORCID https://orcid.org/0009-0004-6954-5938
Yong CuiDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Funding

Capital's Funds for Health Improvement and Research, CFH 2022-2-2024
6 · The paper itself

Abstract

backgroundCancer is a severe threat to human health, and surgery is a major method of cancer treatment. This study aimed to develop an optical sensor for fast cancer tissue.

methodsThe tissue autofluorescence spectrum and diffuse reflectance spectrum were obtained by using a laboratory-developed optical sensor system. A total of 151 lung tissue samples were used in this ex vivo study.

resultsExperimental results demonstrate that tissue autofluorescence spectroscopy with a 365-nm excitation has better performance than diffuse reflectance spectroscopy, and 63 of 64 test samples (98.4% accuracy) were correctly classified with tissue autofluorescence spectroscopy and our developed data analysis method.

conclusionsOur promising ex vivo study results show that the developed optical sensor system has great promise for future clinical translation for intraoperative lung cancer detection and other applications.

Indexed as

Lung NeoplasmsOptical ImagingHumansSpectrometry, Fluorescenceartificial intelligencediagnosislung canceroptical sensor

Identifiers

PMID39558507
PMCPMC11729394

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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.