Evidence map›Paper›PMID 38089873›Full record

ArticleFrontiers in medicine2023

Development of an autoantibody panel for early detection of lung cancer in the Chinese population.

Lin Tong, Jiayuan Sun, Xiaoju Zhang, Di Ge, Ying Li, Jian Zhou, Dong Wang, Xin Hu, Hao Liu, Chunxue Bai

Abstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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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

10 authors.

Lin Tong *Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Jiayuan Sun *Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoju Zhang *Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China.
Di Ge *Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Ying LiDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jian ZhouDepartment of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Dong WangGene Tech (Shanghai) Company Limited, Shanghai, China.
Xin HuGene Tech (Shanghai) Company Limited, Shanghai, China.
Hao LiuGene Tech (Shanghai) Company Limited, Shanghai, China.
Chunxue BaiDepartment of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Tumor-associated autoantibodies have been revealed as promising biomarkers for the early detection of lung cancer. This study was designed to develop an autoantibody panel for early detection of lung cancer in the Chinese population. Methods: Recruited prospectively in three clinical centers, the subjects ( Results: A panel of nine autoantibody markers (named as CN9), namely, P53, SOX2, SSX1, HuD, NY-ESO-1, CAGE, MAGE-A4, P62, and CK20, was preferably selected from 14 candidates. The overall specificity, sensitivity, and AUC were 90.5%, 40.8%, and 0.64, respectively. The CN9 panel demonstrated a reasonable detection rate in lung cancer patients at all stages, histological types, sizes of lesions, and risk levels. Its estimated overall accuracy is 85.5% and 90%, with PPV at 0.32 and 0.04, and NPV at 0.93 and 0.99 in the scenario of pulmonary nodules' characterizing and lung cancer screening, respectively. Two risk models were developed within the subgroups of malignant and benign pulmonary nodules in this study. By adding the CN9 result to the Mayo model indicators, it achieved a sensitivity of 41.3% and an AUC of 0.74 at a specificity of 91.3%. By adding the CN9 result to the Brock model indicators, it achieved a sensitivity of 47.7% and an AUC of 0.78 at a specificity of 91.3%. Both were improved compared with either the standalone Mayo or Brock model. Discussion: This multi-center prospective study indicates a panel of nine autoantibody markers that can help in the detection of lung cancer and the classification of pulmonary nodules in the Chinese population.

Indexed as

autoantibodyChinese populationearly detectionlung cancerpulmonary nodule

Identifiers

PMID38089873
PMCPMC10711285

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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.