Correction to: Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung.

Caiyue Ren, Jianping Zhang, Ming Qi, Jiangang Zhang, Yingjian Zhang, Shaoli Song, Yun Sun, Jingyi Cheng

Abstract readPublished Erratum

In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2021. 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.

An [European radiology · 2024
Article
Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT.Cancer management and research · 2024
Review
Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics.PloS one · 2024
Article
Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction.World journal of gastroenterology · 2022
Article
The value of combined PET/MRI, CT and clinical metabolic parameters in differentiating lung adenocarcinoma from squamous cell carcinoma.Frontiers in oncology · 2022
Article

4 · The record

Corrections and comments

Erratum for
Machine learning based on clinico-biological features integrated2021

5 · Who and what money

Authors and funding

8 authors.

Caiyue RenDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.

Jianping ZhangDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, 201321, China.

Ming QiDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, 201321, China.

Jiangang ZhangDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.

Yingjian ZhangShanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.

Shaoli SongDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.

Yun SunDepartment of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China. yun.sun@sphic.org.cn.

Jingyi ChengShanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Identifiers

PMID33532911

DOI10.1007/s00259-021-05226-1

PMCPMC8182867

What OpenQuestion holds

Textmetadata

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.