Evidence map›Paper›PMID 39720561›Full record

ReviewFrontiers in oncology2024

Predicting lymph node metastasis of clinical T1 non-small cell lung cancer: a brief review of possible methodologies and controversies.

Li Zhang, Feiyue Zhang, Gaofeng Li, Xudong Xiang, Haifeng Liang, Yan 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Predictive value of primary tumorCancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Article
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

6 authors.

Li Zhang *Department of Oncology, the Fifth Affiliated Hospital of Kunming Medical University, Gejiu, China.
Feiyue Zhang *Department of Thoracic Surgery, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Gaofeng LiDepartment of Thoracic Surgery, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Xudong XiangDepartment of Thoracic Surgery, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
Haifeng LiangDepartment of Oncology, the Fifth Affiliated Hospital of Kunming Medical University, Gejiu, China.
Yan ZhangDepartment of Oncology, the Fifth Affiliated Hospital of Kunming Medical University, Gejiu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer and poses a serious threat to human health. Due to the advances in lung cancer screening, more and more clinical T1 NSCLC defined as a tumor with a maximum diameter of 3cm surrounded by lung tissue or visceral pleura have been detected and have achieved favorable treatment outcomes, greatly improving the prognosis of NSCLC patients. However, the preoperative lymph node staging and intraoperative lymph node dissection patterns of operable clinical T1 NSCLC are still subject to much disagreement, as well as the heterogeneity between primary tumors and metastatic lymph nodes poses a challenge in designing effective treatment strategies. This article comprehensively describes the clinical risk factors of clinical T1 NSCLC lymph node metastasis, and its invasive and non-invasive prediction, focusing on the genetic heterogeneity between the primary tumor and the metastatic lymph nodes, which is significant for a thoroughly understanding of the biological behavior of early-stage NSCLC.

Indexed as

clinical T1 non-small cell lung cancerheterogeneitylymph node dissectionlymph node metastasisnoninvasive prediction

Identifiers

PMID39720561
PMCPMC11667114

What OpenQuestion holds

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LicenceCC BY
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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.