Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Medical Imaging
  4. Volume 20, Issue 1
  5. Article
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Background

Radiomics can quantify pulmonary nodule characteristics non-invasively by applying advanced imaging feature algorithms. Radiomic textural features derived from Computed Tomography (CT) imaging are broadly used to predict benign and malignant pulmonary nodules. However, few studies have reported on the radiomics-based identification of nodular Pulmonary Cryptococcosis (PC).

Objective

This study aimed to evaluate the diagnostic and differential diagnostic value of radiomic features extracted from CT images for nodular PC.

Methods

This retrospective analysis included 44 patients with PC (29 males, 15 females), 58 with Tuberculosis (TB) (39 males, 19 females), and 60 with Lung Cancer (LC) (20 males, 40 females) confirmed pathologically. Models 1 (PC . non-PC), 2 (PC . TB), and 3 (PC . LC) were established using radiomic features. Models 4 (PC . TB) and 5 (PC . LC) were established based on radiomic and CT features.

Results

Five radiomic features were predictive of PC . non-PC model, but accuracy and Area Under the Curve (AUC) were 0.49 and 0.472, respectively. In model 2 (PC . TB) involving six radiomic features, the accuracy and AUC were 0.80 and 0.815, respectively. Model 3 (PC . LC) with six radiomic features performed well, with AUC=0.806 and an accuracy of 0.76. Between the PC and TB groups, model 4 combining radiomics, distribution, and PI, showed AUC=0.870. In differentiating PC from LC, the combination of radiomics, distribution, PI, and RBNAV achieved AUC=0.926 and an accuracy of 0.90.

Conclusion

The prediction models based on radiomic features from CT images performed well in discriminating PC from TB and LC. The individualized prediction models combining radiomic and CT features achieved the best diagnostic performance.

© 2024 The Author(s). Published by Bentham Science Publisher.
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.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056302538240522110059
2024-01-01
2025-06-22

  • From This Site

    /content/journals/cmir/10.2174/0115734056302538240522110059
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. PerfectJ.R. DismukesW.E. DromerF. GoldmanD.L. GraybillJ.R. HamillR.J. HarrisonT.S. LarsenR.A. LortholaryO. NguyenM.H. PappasP.G. PowderlyW.G. SinghN. SobelJ.D. SorrellT.C. Clinical practice guidelines for the management of cryptococcal disease: 2010 update by the infectious diseases society of america.Clin. Infect. Dis.201050329132210.1086/64985820047480
     [Google Scholar]
  2. ChangC. SorrellT. ChenS. Pulmonary Cryptococcosis.Semin. Respir. Crit. Care Med.201536568169110.1055/s‑0035‑156289526398535
     [Google Scholar]
  3. TsengH.K. LiuC.P. HoM.W. LuP.L. LoH.J. LinY.H. ChoW.L. ChenY.C. Microbiological, epidemiological, and clinical characteristics and outcomes of patients with cryptococcosis in Taiwan, 1997-2010.PLoS One201384e6192110.1371/journal.pone.006192123613973
     [Google Scholar]
  4. McHughG. MakadzangeA. New approaches to the diagnosis and treatment of cryptococcal meningitis.Semin. Neurol.201434104706010.1055/s‑0034‑137234224715488
     [Google Scholar]
  5. LimperA.H. KnoxK.S. SarosiG.A. AmpelN.M. BennettJ.E. CatanzaroA. DaviesS.F. DismukesW.E. HageC.A. MarrK.A. ModyC.H. PerfectJ.R. StevensD.A. An official American Thoracic Society statement: Treatment of fungal infections in adult pulmonary and critical care patients.Am. J. Respir. Crit. Care Med.201118319612810.1164/rccm.2008‑740ST21193785
     [Google Scholar]
  6. Henao-MartínezA.F. BeckhamJ.D. Cryptococcosis in solid organ transplant recipients.Curr. Opin. Infect. Dis.201528430030710.1097/QCO.000000000000017126098495
     [Google Scholar]
  7. SinghN. AlexanderB.D. LortholaryO. DromerF. GuptaK.L. JohnG.T. del BustoR. KlintmalmG.B. SomaniJ. LyonG.M. PursellK. StosorV. MuňozP. LimayeA.P. KalilA.C. PruettT.L. Garcia-DiazJ. HumarA. HoustonS. HouseA.A. WrayD. OrloffS. DowdyL.A. FisherR.A. HeitmanJ. WagenerM.M. HusainS. Cryptococcus neoformans in organ transplant recipients: impact of calcineurin-inhibitor agents on mortality.J. Infect. Dis.2007195575676410.1086/51143817262720
     [Google Scholar]
  8. PhillipsP. GalanisE. MacDougallL. ChongM.Y. BalshawR. CookV.J. BowieW. SteinerT. HoangL. MorshedM. GhesquiereW. ForrestD.M. RoscoeD. DoyleP. KibseyP.C. ConnollyT. MirzanejadY. ThompsonD. British Columbia Cryptococcus gattii Study Group Longitudinal clinical findings and outcome among patients with Cryptococcus gattii infection in British Columbia.Clin. Infect. Dis.20156091368137625632012
     [Google Scholar]
  9. WangY.Q. HuangZ.X. YuanF. Preliminary study on differential diagnosis of liver cancer and hepatic hemangioma by texture analysis of non-enhanced CT images.Chin J Basews Clin Gen Surg20172402254258
     [Google Scholar]
  10. AertsH.J.W.L. VelazquezE.R. LeijenaarR.T.H. ParmarC. GrossmannP. CarvalhoS. BussinkJ. MonshouwerR. Haibe-KainsB. RietveldD. HoebersF. RietbergenM.M. LeemansC.R. DekkerA. QuackenbushJ. GilliesR.J. LambinP. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nat. Commun.201451400610.1038/ncomms500624892406
     [Google Scholar]
  11. GilliesR.J. KinahanP.E. HricakH. Radiomics: Images Are More than Pictures, They Are Data.Radiology2016278256357710.1148/radiol.201515116926579733
     [Google Scholar]
  12. ZhangL.W. FangM.J. YanY.L. Development and application of radiomics.Zhonghua Fang She Xue Za Zhi20175117577
     [Google Scholar]
  13. LubnerM.G. SmithA.D. SandrasegaranK. SahaniD.V. PickhardtP.J. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.Radiographics20173751483150310.1148/rg.201717005628898189
     [Google Scholar]
  14. El AyachyR. GiraudN. GiraudP. DurduxC. GiraudP. BurgunA. BibaultJ.E. The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.Front. Oncol.20211160359510.3389/fonc.2021.60359534026602
     [Google Scholar]
  15. BinczykF. PrazuchW. BozekP. PolanskaJ. Radiomics and artificial intelligence in lung cancer screening.Transl. Lung Cancer Res.20211021186119910.21037/tlcr‑20‑70833718055
     [Google Scholar]
  16. HunterB. ChenM. RatnakumarP. AlemuE. LoganA. Linton-ReidK. TongD. SenthivelN. BhamaniA. BlochS. KempS.V. BoddyL. JainS. GareebooS. RawalB. DoranS. NavaniN. NairA. BunceC. KayeS. BlackledgeM. AboagyeE.O. DevarajA. LeeR.W. A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules.EBioMedicine20228610434410.1016/j.ebiom.2022.10434436370635
     [Google Scholar]
  17. CuiE.N. YuT. ShangS.J. WangX.Y. JinY.L. DongY. ZhaoH. LuoY.H. JiangX.R. Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans.World J. Clin. Cases20208215203521210.12998/wjcc.v8.i21.520333269256
     [Google Scholar]
  18. DongQ. WenQ. LiN. TongJ. LiZ. BaoX. XuJ. LiD. Radiomics combined with clinical features in distinguishing non-calcifying tuberculosis granuloma and lung adenocarcinoma in small pulmonary nodules.PeerJ202210e1412710.7717/peerj.1412736281359
     [Google Scholar]
  19. Thattaamuriyil PadmakumariL. GuidoG. CarusoD. NacciI. Del GaudioA. ZerunianM. PoliciM. GopalakrishnanR. Sayed MohamedA.K. De SantisD. LaghiA. CioniD. NeriE. The Role of Chest CT Radiomics in Diagnosis of Lung Cancer or Tuberculosis: A Pilot Study.Diagnostics202212373910.3390/diagnostics1203073935328296
     [Google Scholar]
  20. ZhangY. ChuZ. YuJ. ChenX. LiuJ. XuJ. HuangC. PengL. Computed tomography–based radiomics for identifying pulmonary cryptococcosis mimicking lung cancer.Med. Phys.20224995943595210.1002/mp.1578935678964
     [Google Scholar]
  21. ZhangZ. MuX. YuJ. GuanC. ChenB. XieR.M. Establishment and evaluation of a CT-based radiomic model for AIDS-associated pulmonary cryptococcosis.BMC Med. Imaging202222118510.1186/s12880‑022‑00910‑636309647
     [Google Scholar]
  22. ZhaoJ. SunL. SunK. WangT. WangB. YangY. WuC. SunX. Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule.Front. Oncol.20211175984010.3389/fonc.2021.75984034858836
     [Google Scholar]
  23. LiS. ZhangG. YinY. XieQ. GuoX. CaoK. SongQ. GuanJ. ZhouX. One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed.Comput. Med. Imaging Graph.20219410200910.1016/j.compmedimag.2021.10200934741847
     [Google Scholar]
  24. ShenA.J. LiH.J. XiaY.L. CT image features of malignant solitary pulmonary nodules.J Chin Pract Diag Ther2013271212091211
     [Google Scholar]
  25. DoughertyE. HuaJ. SimaC. Performance of feature selection methods.Curr. Genomics200910636537410.2174/13892020978917762920190952
     [Google Scholar]
  26. XuB. LinH.B. ZhouH. XuJ.P. Protective effect of polydatin on a PC12 cell model of oxygen-glucose deprivation.Nan Fang Yi Ke Da Xue Xue Bao20103051041104320501389
     [Google Scholar]
  27. QuY. LiuG. GhimireP. LiaoM. ShiH. YangG. XuL. WangG. Primary pulmonary cryptococcosis: evaluation of CT characteristics in 26 immunocompetent Chinese patients.Acta Radiol.201253666867410.1258/ar.2012.11061222798384
     [Google Scholar]
  28. BrownG.D. DenningD.W. GowN.A.R. LevitzS.M. NeteaM.G. WhiteT.C. Hidden killers: human fungal infections.Sci. Transl. Med.20124165165rv1310.1126/scitranslmed.300440423253612
     [Google Scholar]
  29. ChaeH.D. ParkC.M. ParkS.J. LeeS.M. KimK.G. GooJ.M. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.Radiology2014273128529310.1148/radiol.1413218725102296
     [Google Scholar]
  30. LiuH. WangX.Y. LongX.Y. Research progress and clinical application of tumor heterogeneity based on CT texture analysis.Intl J Med Radiol20163905543548
     [Google Scholar]
  31. ChenL.P. LiuW.F. ZhaoW. Differential diagnosis of granulomatous pulmonary aspergillosis and pulmonary cryptococcosis: a non-enhanced CT images based texture analysis.Chin Comput Med Imag20182405441445
     [Google Scholar]
  32. FanL. FangM.J. DongD. Subtype discrimination of lung adenocarcinoma manifesting as ground glass nodule based on radiomics.Zhonghua Fang She Xue Za Zhi20175112912917
     [Google Scholar]
  33. ZhangT YuanM ZhongY Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics.Clin Radiol.201974178e2378e30
     [Google Scholar]
  34. SheY. ZhangL. ZhuH. DaiC. XieD. XieH. ZhangW. ZhaoL. ZouL. FeiK. SunX. ChenC. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules.Eur. Radiol.201828125121512810.1007/s00330‑018‑5509‑929869172
     [Google Scholar]
  35. WeiK. SuH. ZhouG. ZhangR. CaiP. FanY. XieC. FeiB. ZhangZ. Potential application of radiomics for differentiating solitary pulmonary nodules.OMICS J. Radiol.201652100021810.4172/2167‑7964.100021828261535
     [Google Scholar]
  36. DharaA.K. MukhopadhyayS. DuttaA. GargM. KhandelwalN. A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.J. Digit. Imaging201629446647510.1007/s10278‑015‑9857‑626738871
     [Google Scholar]
  37. DilgerS.K.N. UthoffJ. JudischA. HammondE. MottS.L. SmithB.J. NewellJ.D.Jr HoffmanE.A. SierenJ.C. Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.J. Med. Imaging20152404100410.1117/1.JMI.2.4.04100426870744
     [Google Scholar]
  38. HeL. HuangY. MaZ. LiangC. LiangC. LiuZ. Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule.Sci. Rep.2016613492110.1038/srep3492127721474
     [Google Scholar]
  39. MidyaA. ChakrabortyJ. GönenM. DoR.K.G. SimpsonA.L. Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility.J. Med. Imaging201851110.1117/1.JMI.5.1.01102029487877
     [Google Scholar]
  40. KumarV. GuY. BasuS. BerglundA. EschrichS.A. SchabathM.B. ForsterK. AertsH.J.W.L. DekkerA. FenstermacherD. GoldgofD.B. HallL.O. LambinP. BalagurunathanY. GatenbyR.A. GilliesR.J. Radiomics: the process and the challenges.Magn. Reson. Imaging20123091234124810.1016/j.mri.2012.06.01022898692
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056302538240522110059
Loading
Diagnostic Study of Nodular Pulmonary Cryptococcosis Based on Radiomic Features Captured from CT Images
Curr. Med. Imaging 20, e15734056302538 (2024); https://doi.org/10.2174/0115734056302538240522110059
/content/journals/cmir/10.2174/0115734056302538240522110059
/content/journals/cmir/10.2174/0115734056302538240522110059
Loading

Data & Media loading...

Supplements

Supplementary material is available on the Publisher’s website.

file format download Download

  • Article Type:     Research Article
Keyword(s): Cryptococcosis; CT images; Diagnosis; Prediction model; Pulmonary nodules; Radiomics

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test