Evidence map›Paper›PMID 40108487›Full record

ArticleCurrent medical imaging2025

Differentiation of Minute Pulmonary Meningothelial-Like Nodules and Adenocarcinoma In situ with CT Radiomics

Yawen Zhang, Leilei Zhou, Jun Yao, Hai Xu, Yu-Chen Chen, Xiaomin Yong

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Article in Current medical imaging, 2025. 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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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yawen ZhangDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Leilei ZhouDepartment of Radiology, Nanjing Pukou People's Hospital, Nanjing, China.
Jun YaoDepartment of Radiology, Nanjing Pukou People's Hospital, Nanjing, China.
Hai XuDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yu-Chen ChenDepartment of Radiology, Nanjing Pukou People's Hospital, Nanjing, China.
Xiaomin YongDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn effective preoperative diagnosis between minute pulmonary meningothelial-like nodules (MPMNs) and adenocarcinoma in situ (AIS) can provide clinicians with appropriate treatment strategies.

objectiveThis study aimed to differentiate MPMNs from AIS via computed tomography (CT) radiomics approaches.

methodsClinical and imaging data from fifty-one patients diagnosed with MPMNs and 55 patients diagnosed with AIS were collected from Jiangsu Province Hospital and Nanjing First Hospital from January 2016 to December 2022. All patients underwent chest CT scans before surgery. All CT images were segmented with ITK-SNAP software, and the radiomics features were further extracted with the Python PyRadiomics package. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the optimal radiomics features for the construction of the model. The ROC curve was used to evaluate the diagnostic efficacy of the model.

resultsAfter feature reduction and selection, 16 radiomics features were selected to construct the radiomics model. In the test set, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the k-nearest neighbor model were 87.5%, 88.9%, 96.9%, 77.8%, and 88.5%, respectively, and the AUC of the ROC curve was 0.969 (95% CI: 0.72-1.00).

conclusionThe CT radiomics model has exhibited high diagnostic value in the differential diagnosis between MPMNs and AIS.

Indexed as

Adenocarcinoma in SituLung NeoplasmsTomography, X-Ray ComputedAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRadiomicsRetrospective StudiesSensitivity and Specificityadenocarcinoma in situcomputed tomography.Least absolute shrinkage and selection operatorminute pulmonary meningothelial-like nodulesRadiomics

Identifiers

PMID40108487
PMCPMC13181191

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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.