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

Background:

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

Objective:

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

Methods:

Clinical 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.

Results:

After 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).

Conclusion:

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

2025 © 2025 The Author(s). Published by Bentham Science Publisher. The Author(s)
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-03-14
2025-04-06

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References

  1. Korn D. Bensch K. Liebow A.A. Castleman B. Multiple minute pulmonary tumors resembling chemodectomas. Am. J. Pathol. 1960 37 6 641 672 13753180
    [Google Scholar]
  2. Gaffey M.J. Mills S.E. Askin F.B. Minute pulmonary meningothelial-like nodules. A clinicopathologic study of so-called minute pulmonary chemodectoma. Am. J. Surg. Pathol. 1988 12 3 167 175 10.1097/00000478‑198803000‑00001 2830799
    [Google Scholar]
  3. Mukhopadhyay S. El-Zammar O.A. Katzenstein A.L.A. Pulmonary meningothelial-like nodules: New insights into a common but poorly understood entity. Am. J. Surg. Pathol. 2009 33 4 487 495 10.1097/PAS.0b013e31818b1de7 19047895
    [Google Scholar]
  4. Mizutani E. Tsuta K. Maeshima A.M. Asamura H. Matsuno Y. Minute pulmonary meningothelial-like nodules: Clinicopathologic analysis of 121 patients. Hum. Pathol. 2009 40 5 678 682 10.1016/j.humpath.2008.08.018 19144385
    [Google Scholar]
  5. Zhang Y. Wu J. Zhao Y. Zhang T. Xu H. Chen Y.C. Minute pulmonary meningothelial-like nodules: Associations between computed tomography and pathology features. Quant. Imaging Med. Surg. 2023 13 1 462 470 10.21037/qims‑21‑1250 36620132
    [Google Scholar]
  6. Zhang Y. Wu J. Zhang T. Zhang Q. Chen Y.C. Minute pulmonary meningothelial-like nodules: Rare lesions appearing as diffuse ground-glass nodules with cyst-like morphology. Quant. Imaging Med. Surg. 2021 11 7 3355 3359 10.21037/qims‑20‑676 34249659
    [Google Scholar]
  7. Castoldi M.C. Verrioli A. De Juli E. Vanzulli A. Pulmonary Langerhans cell histiocytosis: The many faces of presentation at initial CT scan. Insights Imaging 2014 5 4 483 492 10.1007/s13244‑014‑0338‑0 24996395
    [Google Scholar]
  8. Park S. Lee S.M. Noh H.N. Hwang H.J. Kim S. Do K.H. Seo J.B. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur. Radiol. 2020 30 9 4883 4892 10.1007/s00330‑020‑06805‑w 32300970
    [Google Scholar]
  9. Zhao F. Fan H. Shan K. Zhou L. Pang Z. Fu C. Yang Z. Wu M. Sun J. Yang X. Huang Z. Predictive efficacy of a radiomics random forest model for identifying pathological subtypes of lung adenocarcinoma presenting as ground-glass nodules. Front. Oncol. 2022 12 872503 10.3389/fonc.2022.872503 35646675
    [Google Scholar]
  10. Dunn B. Pierobon M. Wei Q. Automated classification of lung cancer subtypes using deep learning and CT-scan based radiomic analysis. Bioengineering 2023 10 6 690
    [Google Scholar]
  11. Liu J. Qi L. Wang Y. Li F. Chen J. Cui S. Cheng S. Zhou Z. Li L. Wang J. Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules. Eur Radiol Exp 2024 8 1 8
    [Google Scholar]
  12. Zhou J. Wen Y. Ding R. Liu J. Fang H. Li X. Zhao K. Wan Q. Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions. CI 2024 24 1 14
    [Google Scholar]
  13. Wang Y. Chen H. Chen Y. Zhong Z. Huang H. Sun P. Zhang X. Wan Y. Li L. Ye T. Pan F. Yang L. A semiautomated radiomics model based on multimodal dual-layer spectral CT for preoperative discrimination of the invasiveness of pulmonary ground-glass nodules. J. Thorac. Dis. 2023 15 5 2505 2516 10.21037/jtd‑22‑1605 37324063
    [Google Scholar]
  14. Feng H. Shi G. Xu Q. Ren J. Wang L. Cai X. Radiomics-based analysis of CT imaging for the preoperative prediction of invasiveness in pure ground-glass nodule lung adenocarcinomas. Insights Imaging 2023 14 1 24 10.1186/s13244‑022‑01363‑9 36735104
    [Google Scholar]
  15. Hosny A. Parmar C. Coroller T.P. Grossmann P. Deep Learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med 2018 15 11 e1002711
    [Google Scholar]
  16. Dercle L. Fronheiser M. Lu L. Du S. Hayes W. Leung D.K. Roy A. Wilkerson J. Guo P. Fojo A.T. Schwartz L.H. Zhao B. Identification of non–small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin. Cancer Res. 2020 26 9 2151 2162 10.1158/1078‑0432.CCR‑19‑2942 32198149
    [Google Scholar]
  17. Choi W. Oh J.H. Riyahi S. Liu C.J. Jiang F. Chen W. White C. Rimner A. Mechalakos J.G. Deasy J.O. Lu W. Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer. Med. Phys. 2018 45 4 1537 1549 10.1002/mp.12820 29457229
    [Google Scholar]
  18. Wang S.B. Mao Y.S. Progress in screening and follow-up studies of pulmonary ground glass nodules. Zhonghua Zhong Liu Za Zhi 2022 44 2 123 129 35184455
    [Google Scholar]
  19. MacMahon H. Austin J.H.M. Gamsu G. Herold C.J. Jett J.R. Naidich D.P. Patz E.F. Jr Swensen S.J. Fleischner Society Guidelines for management of small pulmonary nodules detected on CT scans: A statement from the Fleischner Society. Radiology 2005 237 2 395 400 10.1148/radiol.2372041887 16244247
    [Google Scholar]
  20. Kothary N. Lock L. Sze D.Y. Hofmann L.V. Computed tomography-guided percutaneous needle biopsy of pulmonary nodules: Impact of nodule size on diagnostic accuracy. Clin. Lung Cancer 2009 10 5 360 363 10.3816/CLC.2009.n.049 19808195
    [Google Scholar]
  21. Kitami A. Sano F. Hayashi S. Suzuki K. Uematsu S. Kamio Y. Suzuki T. Kadokura M. Omatsu M. Kunimura T. Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules. Surg. Today 2016 46 5 593 598 10.1007/s00595‑015‑1208‑1 26123755
    [Google Scholar]
  22. Chen C.H. Chang C.K. Tu C.Y. Liao W.C. Wu B.R. Chou K.T. Chiou Y.R. Yang S.N. Zhang G. Huang T.C. Radiomic features analysis in computed tomography images of lung nodule classification. PLoS One 2018 13 2 e0192002
    [Google Scholar]
  23. Lambin P. Leijenaar R.T.H. Deist T.M. Peerlings J. de Jong E.E.C. van Timmeren J. Sanduleanu S. Larue R.T.H.M. Even A.J.G. Jochems A. van Wijk Y. Woodruff H. van Soest J. Lustberg T. Roelofs E. van Elmpt W. Dekker A. Mottaghy F.M. Wildberger J.E. Walsh S. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017 14 12 749 762 10.1038/nrclinonc.2017.141 28975929
    [Google Scholar]
  24. Gillies R.J. Kinahan P.E. Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016 278 2 563 577 10.1148/radiol.2015151169 26579733
    [Google Scholar]
  25. Bi W.L. Hosny A. Schabath M.B. Giger M.L. Birkbak N.J. Mehrtash A. Allison T. Arnaout O. Abbosh C. Dunn I.F. Mak R.H. Tamimi R.M. Tempany C.M. Swanton C. Hoffmann U. Schwartz L.H. Gillies R.J. Huang R.Y. Aerts H. Artificial Intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 2019 69 2 127 157
    [Google Scholar]
  26. Aerts H.J.W.L. Velazquez E.R. Leijenaar R.T.H. Parmar C. Grossmann P. Carvalho S. Bussink J. Monshouwer R. Haibe-Kains B. Rietveld D. Hoebers F. Rietbergen M.M. Leemans C.R. Dekker A. Quackenbush J. Gillies R.J. Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014 5 1 4006 10.1038/ncomms5006 24892406
    [Google Scholar]
  27. Wilson R. Devaraj A. Radiomics of pulmonary nodules and lung cancer. Transl. Lung Cancer Res. 2017 6 1 86 91 10.21037/tlcr.2017.01.04 28331828
    [Google Scholar]
  28. Liu Y. Balagurunathan Y. Atwater T. Antic S. Li Q. Walker R.C. Smith G.T. Massion P.P. Schabath M.B. Gillies R.J. Radiological image traits predictive of cancer status in pulmonary nodules. Clin. Cancer Res. 2017 23 6 1442 1449 10.1158/1078‑0432.CCR‑15‑3102 27663588
    [Google Scholar]
  29. Aerts H.J.W.L. Grossmann P. Tan Y. Oxnard G.R. Rizvi N. Schwartz L.H. Zhao B. Defining a radiomic response phenotype: A pilot study using targeted therapy in NSCLC. Sci. Rep. 2016 6 1 33860 10.1038/srep33860 27645803
    [Google Scholar]
  30. Bogowicz M. Vuong D. Huellner M.W. Pavic M. Andratschke N. Gabrys H.S. Guckenberger M. Tanadini-Lang S. CT radiomics and PET radiomics: Ready for clinical implementation? Q J Nucl Med Mol Imaging 2019 63 4 355 370
    [Google Scholar]
/content/journals/cmir/10.2174/0115734056354822250217045544
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Differentiation of Minute Pulmonary Meningothelial-Like Nodules and Adenocarcinoma In situ with CT Radiomics
Bentham Science Publishers , e15734056354822; https://doi.org/10.2174/0115734056354822250217045544
/content/journals/cmir/10.2174/0115734056354822250217045544
/content/journals/cmir/10.2174/0115734056354822250217045544
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  • Article Type:     Research Article
Keywords: adenocarcinoma in situ ; computed tomography ; Radiomics ; minute pulmonary meningothelial-like nodules

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