Evidence map›Paper›PMID 36643621›Full record

Trial reportPeerJ2023

Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study.

Haochuan Zhang, Shixiong Wang, Zhenkai Deng, Yangli Li, Yingying Yang, He Huang

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. 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.

Haochuan ZhangDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Graduate School of Guangzhou Medical University, Shenzhen, China.
Shixiong WangDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Graduate School of Guangzhou Medical University, Shenzhen, China.
Zhenkai DengDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Graduate School of Guangzhou Medical University, Shenzhen, China.
Yangli LiDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Graduate School of Guangzhou Medical University, Shenzhen, China.
Yingying YangDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Graduate School of Guangzhou Medical University, Shenzhen, China.
He HuangDepartment of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Graduate School of Guangzhou Medical University, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (

Indexed as

Adenocarcinoma of LungLung NeoplasmsPrecancerous ConditionsHumansMachine LearningRetrospective StudiesTomography, X-Ray ComputedCT imagesInvasivenessMachine learningRadiomicsSub-centimeter subsolid pulmonary nodules

Identifiers

PMID36643621
PMCPMC9838201

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.