Evidence map›Paper›PMID 39035744›Full record

ReviewFrontiers in oncology2024

Predictors of early colorectal cancer metastasis to lymph nodes: providing rationale for therapy decisions.

Xu Song, Jun Li, Jiang Zhu, Yun-Fei Kong, Yu-Hang Zhou, Zi-Kun Wang, Jin Zhang

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 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.

  1. Review
  2. Article
  3. 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.

Xu SongDepartment of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Jun LiDepartment of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Jiang ZhuDepartment of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Yun-Fei KongDepartment of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Yu-Hang ZhouDepartment of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Zi-Kun WangDepartment of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
Jin ZhangDepartment of General Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the improvement of national health awareness and the popularization of a series of screening methods, the number of patients with early colorectal cancer is gradually increasing, and accurate prediction of lymph node metastasis of T1 colorectal cancer is the key to determining the optimal therapeutic solutions. Whether patients with T1 colorectal cancer undergoing endoscopic resection require additional surgery and regional lymph node dissection is inconclusive in current guidelines. However, we can be sure that in early colorectal cancer without lymph node metastasis, endoscopic resection alone does not affect the prognosis, and it greatly improves the quality of life and reduces the incidence of surgical complications while preserving organ integrity. Therefore, it is vital to discriminate patients without lymph node metastasis in T1 colorectal cancer, and this requires accurate predictors. This paper briefly explains the significance and shortcomings of traditional pathological factors, then extends and states the new pathological factors, clinical test factors, molecular biomarkers, and the risk assessment models of lymph node metastasis based on artificial intelligence.

Indexed as

additional surgeryartificial intelligencelymph node metastasispredictive factorT1 colorectal cancer

Identifiers

PMID39035744
PMCPMC11257837

What OpenQuestion holds

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Registered trials

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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.