Evidence map›Paper›PMID 39845324›Full record

ReviewFrontiers in oncology2024

The optimization and application of photodynamic diagnosis and autofluorescence imaging in tumor diagnosis and guided surgery: current status and future prospects.

Wei Wan, Huiquan Liu, Junrong Zou, Tianpeng Xie, Guoxi Zhang, Weihai Ying, Xiaofeng Zou

Abstract readReview
In one paragraph

Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
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

7 authors.

Wei Wan *The First Clinical College, Gannan Medical University, Ganzhou, Jiangxi, China.
Huiquan Liu *The First Clinical College, Gannan Medical University, Ganzhou, Jiangxi, China.
Junrong Zou *Institute of Urology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Tianpeng XieDepartment of Urology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Guoxi ZhangDepartment of Urology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Weihai YingSchool of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
Xiaofeng ZouThe First Clinical College, Gannan Medical University, Ganzhou, Jiangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photodynamic diagnosis (PDD) and autofluorescence imaging (AFI) are emerging cancer diagnostic technologies that offer significant advantages over traditional white-light endoscopy in detecting precancerous lesions and early-stage cancers; moreover, they hold promising potential in fluorescence-guided surgery (FGS) for tumors. However, their shortcomings have somewhat hindered the clinical application of PDD and AFI. Therefore, it is imperative to enhance the efficacy of PDD and AFI, thereby maximizing their potential for practical clinical use. This article reviews the principles, characteristics, current research status, and advancements of PDD and AFI, focusing on analyzing and discussing the optimization strategies of PDD and AFI in tumor diagnosis and FGS scenarios. Considering the practical and technical feasibility, optimizing PDD and AFI may result in an effective real-time diagnostic tool to guide clinicians in tumor diagnosis and surgical guidance to achieve the best results.

Indexed as

autofluorescence imagingdiagnosisfluorescence-guided surgeryoptimization strategyphotodynamic diagnosisphotosensitizertumor

Identifiers

PMID39845324
PMCPMC11750647

What Socratic 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 Socratic graph.