Evidence map›Paper›PMID 37545645›Full record

ReviewWorld journal of radiology2023

Progress of magnetic resonance imaging radiomics in preoperative lymph node diagnosis of esophageal cancer.

Yan-Han Xu, Peng Lu, Ming-Cheng Gao, Rui Wang, Yang-Yang Li, Jian-Xiang Song

Abstract readReview
In one paragraph

Review in World journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Insights into radiomics: a comprehensive review for beginners.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Review
  4. Reassessing the prognostic value of station 108 in the era of neoadjuvant therapy.International journal of surgery (London, England) · 2025
    Article
  5. Review
  6. 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.

Yan-Han XuDepartment of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China.
Peng LuDepartment of Imaging, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China.
Ming-Cheng GaoDepartment of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China.
Rui WangDepartment of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China.
Yang-Yang LiDepartment of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China.
Jian-Xiang SongDepartment of Thoracic Surgery, Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, Yancheng 224000, Jiangsu Province, China. jxsongycsy@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Esophageal cancer, also referred to as esophagus cancer, is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China. Accurately determining the status of lymph nodes is crucial for developing treatment plans, defining the scope of intraoperative lymph node dissection, and ascertaining the prognosis of patients with esophageal cancer. Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging (MRI) have improved the effectiveness of MRI for assessing lymph node involvement, making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting. Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed. The features, such as shape, texture, and waveform, are associated with the cancer phenotype and tumor microenvironment. When these features correlate with the clinical disease outcomes, they form the basis for specific and reliable clinical evidence. This study aimed to review the potential clinical applications of MRI-based radiomics in studying the lymph nodes affected by esophageal cancer. The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer, enabling a more personalized and effectual approach.

Indexed as

Diffusion-weighted imagingDynamic contrast-enhanced imagingEsophageal cancerLymph nodesRadiomics

Identifiers

PMID37545645
PMCPMC10401402

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.