Evidence map›Paper›PMID 36685887›Full record

ArticleFrontiers in genetics2022

Relationship between the image characteristics of artificial intelligence and EGFR gene mutation in lung adenocarcinoma.

Guoping Zhou, Shuhua Xu, Xiaoli Liu, Jingjun Ge, Qiyu He, Weikang Cao, Junning Ding, Xinghua Kai

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2022. 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
0.9field-weighted citation impact, top 26% of its field
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, 4 citations in OpenAlex.

  1. Review
  2. Review
  3. Prediction of EGFR mutation status in lung adenocarcinoma based onAmerican journal of nuclear medicine and molecular imaging · 2023
    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

8 authors.

Guoping ZhouDepartment of Cardiothoracic Surgery, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.
Shuhua XuDepartment of Cardiothoracic Surgery, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.
Xiaoli LiuDepartment of Pathology, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.
Jingjun GeDepartment of Radiology Imaging, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.
Qiyu HeDepartment of Cardiothoracic Surgery, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.
Weikang CaoDepartment of Cardiothoracic Surgery, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.
Junning DingDepartment of Cardiothoracic Surgery, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.
Xinghua KaiDepartment of Cardiothoracic Surgery, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung Adenocarcinoma (LUAD) is a kind of Lung Cancer (LCA) with high incidence rate, which is very harmful to human body. It is hidden in the human body and is not easy to be discovered, so it brings great inconvenience to the treatment of LUAD. Artificial Intelligence (AI) technology provides technical support for the diagnosis and treatment of LUAD and has great application space in intelligent medicine. In this paper, 164 patients with primary LUAD who underwent surgery in Hospital A from January 2020 to December 2021 were selected as the study subjects, and the correlation between the imaging characteristics of LUAD and Epidermal Growth Factor Receptor (EGFR) gene mutation was analyzed. Finally, the conclusion was drawn. In terms of the study on the correlation between EGFR mutation of LUAD and the imaging characteristics of Computed Tomography (CT), it was concluded that there were significant differences between the patient's sex, smoking history, pulmonary nodule morphology and the EGFR gene, and there was no significant difference between the patient's tumor size and EGFR gene; in the study of the relationship between EGFR gene mutation and CT signs of LUAD lesions, it was found that there were significant differences between the symptoms of cavity sign, hair prick sign and chest depression sign and EGFR gene, but there was no significant difference between the symptoms of lobulation sign and EGFR gene; in the study of pathological subtype and EGFR gene mutation status of LUAD patients, it was concluded that the pathological subtype was mainly micropapillary. The mutation rate was 44.44%, which was the highest; in terms of CT manifestations of adjacent structures of lung cancer and the study of EGFR gene mutation status, it was found that there was a statistical difference between the tumor with vascular convergence sign and EGFR gene mutation, and pleural effusion, pericardial effusion, pleural thickening and other signs in tumor imaging were not significantly associated with EGFR gene mutation; in terms of the study of CT manifestations of adjacent structures of LCA and EGFR gene mutation status, it was concluded that pleural effusion, pericardial effusion, pleural thickening and other signs in tumor images were not significantly associated with EGFR gene mutation; in terms of analysis and cure of LUAD, it was concluded that the cure rate of patients was relatively high, and only a few people died of ineffective treatment. This paper provided a reference for the field of intelligent medicine and physical health.

Indexed as

artificial intelligenceEGFR gene mutationsimage characteristicsintelligent medicinelung adenocarcinoma

Identifiers

PMID36685887
PMCPMC9846484
OpenAlexW4313586054

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.