Evidence map›Paper›PMID 41651970›Full record

ArticleCommunications medicine2026

Intraoperative biopsy imaging of lung cancer risk.

Shuhao Qian, Lu Yang, Jia Meng, Lingxi Zhou, Tao Han, Lingmei Chen, Gangqin Xi, Rushan Jiang, Chuncheng Wang, Bo Niu and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2026. 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
–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

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

15 authors.

Shuhao Qian *State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0002-5276-2045
Lu Yang *State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.
Jia MengState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0003-3455-205X
Lingxi ZhouState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.
Tao HanState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.
Lingmei ChenState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.
Gangqin XiSchool of Science, Jimei University, Xiamen, Fujian, China.
Rushan JiangState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.
Chuncheng WangState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.
Bo NiuState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.
Zhihua DingState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China.ORCID http://orcid.org/0000-0003-2554-3741
Ke SunDepartment of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jianping LuDepartment of Pathology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Shuangmu ZhuoSchool of Science, Jimei University, Xiamen, Fujian, China. shuangmuzhuo@gmail.com.ORCID http://orcid.org/0000-0001-5767-5197
Zhiyi LiuState Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, Zhejiang, China. liuzhiyi07@zju.edu.cn.ORCID http://orcid.org/0000-0002-8122-8474

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62275232National Natural Science Foundation of China (National Science Foundation of China) 62375104National Natural Science Foundation of China (National Science Foundation of China) 62575254Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation) LZ25F050007
6 · The paper itself

Abstract

backgroundBefore surgical resection of lung tumor, intraoperative biopsy is needed for cancer diagnosis, while current techniques that guide biopsy have limited performance in tumor identification and boundary determination. Remodeling of extracellular matrix (ECM), mainly collagen and elastin fibers, is an emerging hallmark of tumorigenesis.

methodsHerein, we establish a quantitative multiphoton microscopy (MPM) imaging method for time-efficient, highly-sensitive lung cancer detection via characterization of ECM remodeling. From label-free images of collagen and elastin fibers acquired simultaneously, we construct a similarity coefficient (SC) metric to describe their interaction, and further develop an artificial intelligence (AI)-ECM framework by producing a fiber voxel dictionary via unsupervised learning of morpho-structural features for explainable and visible assessments of cancer risk.

resultsThe application is demonstrated by ex vivo human lung cancer diagnosis with a sensitivity of 99.37%, and recognizing the tumor boundary. The translational potential is further revealed via in vivo imaging of a murine model harboring human lung cancer.

conclusionsThis technology can help surgeons perform more precise biopsies and surgeries by providing explainable visual cues, thus leading to better outcomes for lung cancer patients.

Identifiers

PMID41651970
PMCPMC12988182

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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.