Evidence map›Paper›PMID 37316444›Full record

ArticleZhongguo fei ai za zhi = Chinese journal of lung cancer2023

[Diagnostic Study of Multi-spectral Intelligent Analyzer in Diagnosis of 
the Infiltration Degree of Lung Adenocarcinoma].

Xianbei Yang, Peihao Wang, Qi Qin, Kangshun Guo, Yong Cui, Yi Luo

Open access · greenAbstract readEnglish Abstract
In one paragraph

Article in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Xianbei YangDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Peihao WangDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Qi QinDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Kangshun GuoDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Yong CuiDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Yi LuoDepartment of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Beijing Friendship Hospital · CNCapital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is one of the most common malignant tumors in the world. The accuracy of intraoperative frozen section (FS) in the diagnosis of lung adenocarcinoma infiltration cannot fully meet the clinical needs. The aim of this study is to explore the possibility of improving the diagnostic efficiency of FS in lung adenocarcinoma by using the original multi-spectral intelligent analyzer.

methodsPatients with pulmonary nodules who underwent surgery in the Department of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University from January 2021 to December 2022 were included in the study. The multispectral information of pulmonary nodule tissues and surrounding normal tissues were collected. A neural network model was established and the accuracy of the neural network diagnostic model was verified clinically.

resultsA total of 223 samples were collected in this study, 156 samples of primary lung adenocarcinoma were finally included, and a total of 1,560 sets of multispectral data were collected. The area under the curve (AUC) of spectral diagnosis in the test set (10% of the first 116 cases) of the neural network model was 0.955 (95%CI: 0.909-1.000, P<0.05), and the diagnostic accuracy was 95.69%. In the clinical validation group (the last 40 cases), the accuracy of spectral diagnosis and FS diagnosis were both 67.50% (27/40), and the AUC of the combination of the two was 0.949 (95%CI: 0.878-1.000, P<0.05), and the accuracy was 95.00% (38/40).

conclusionsThe accuracy of the original multi-spectral intelligent analyzer in the diagnosis of lung invasive adenocarcinoma and non-invasive adenocarcinoma is equivalent to that of FS. The application of the original multi-spectral intelligent analyzer in the diagnosis of FS can improve the diagnostic accuracy and reduce the complexity of intraoperative lung cancer surgery plan.
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Indexed as

AdenocarcinomaAdenocarcinoma of LungLung NeoplasmsMultiple Pulmonary NodulesHospitalsHumansInfiltration degreeIntraoperative frozenLung neoplasmsMultispectral analysisNeural network model

Identifiers

PMID37316444
PMCPMC10273151
OpenAlexW4380730300

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LicenceCC BY
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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.