Evidence map›Paper›PMID 34422651›Full record

ArticleFrontiers in oncology2021

Evaluation of the Radiomics Method for the Prediction of Atypical Adenomatous Hyperplasia in Patients With Subcentimeter Pulmonary Ground-Glass Nodules.

Bin Wang, Preeti Hamal, Xue Meng, Ke Sun, Yang Yang, Yangyang Sun, Xiwen Sun

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2021. 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
0.4field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 3 institutions in 1 country.

Bin WangDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Preeti HamalDepartment of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Xue MengDepartment of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Ke SunDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Yang YangDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Yangyang SunDepartment of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Xiwen SunDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Shanghai East Hospital · CNShanghai Pulmonary Hospital · CNTongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe aimed to develop a prediction model to distinguish atypical adenomatous hyperplasia (AAH) from early lung adenocarcinomas in patients with subcentimeter pulmonary ground-glass nodules (GGNs), which may help avoid aggressive surgical resection for patients with AAH.

methodsSurgically confirmed cases of AAH and lung adenocarcinomas manifesting as GGNs of less than 1 cm were retrospectively collected. A prediction model based on radiomics and clinical features identified from a training set of cases was built to differentiate AAH from lung adenocarcinomas and tested on a validation set.

resultsFour hundred and eighty-five eligible cases were included and randomly assigned to the training (

conclusionThe prediction model based on radiomics and clinical features can help differentiate AAH from adenocarcinomas manifesting as subcentimeter GGNs and may prevent aggressive resection for AAH patients, while reserving this treatment for adenocarcinomas.

Indexed as

forecastinglung neoplasmsradiomicsthoracic surgerytomography; spiral computed

Identifiers

PMID34422651
PMCPMC8374940
OpenAlexW3189960354

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.