Evidence map›Paper›PMID 35397524›Full record

ArticleBMC cancer2022

Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules.

Han Yang, Hongjie Chen, Guorui Zhang, Hongyi Li, Ran Ni, Yali Yu, Yepeng Zhang, Yongjun Wu, Hong Liu

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Article
  8. 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

9 authors at 2 institutions in 1 country.

Han YangDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Hongjie ChenDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Guorui ZhangDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Hongyi LiGeneral Intensive Care Unit, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Ran NiDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Yali YuDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Yepeng ZhangEmergency Intensive Care Unit, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China.
Yongjun WuCollege of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan Province, China.
Hong LiuDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan Province, China. liuhong925@126.com.ORCID http://orcid.org/0000-0001-5642-429X
First Affiliated Hospital of Zhengzhou University · CNZhengzhou University · CN

Funding

health commission of henan province SB201901016
6 · The paper itself

Abstract

backgroundThe accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening.

methodsFrom May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21-1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve.

resultsA total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS.

conclusionsCAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.

Indexed as

Lung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleAntigens, NeoplasmArtificial IntelligenceBiomarkers, TumorEarly Detection of CancerHumansKeratin-19Tomography, X-Ray Computedantigen CYFRA21.1Antigens, NeoplasmBiomarkers, TumorKeratin-19Circulating genetically abnormal cells (CAC)Computed tomography (CT)Early diagnosisLung cancerPulmonary nodules

Identifiers

PMID35397524
PMCPMC8994303
OpenAlexW4223423331

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.