A CT-based radiomics model for predicting lymph node metastasis in hepatic alveolar echinococcosis patients to support lymph node dissection.
Hepatic alveolar echinococcosis
Lymph node metastasis
Machine learning
Radiomics
Journal
European journal of medical research
ISSN: 2047-783X
Titre abrégé: Eur J Med Res
Pays: England
ID NLM: 9517857
Informations de publication
Date de publication:
07 Aug 2024
07 Aug 2024
Historique:
received:
25
01
2024
accepted:
27
07
2024
medline:
8
8
2024
pubmed:
8
8
2024
entrez:
7
8
2024
Statut:
epublish
Résumé
Hepatic alveolar echinococcosis (AE) is a severe zoonotic parasitic disease, and accurate preoperative prediction of lymph node (LN) metastasis in AE patients is crucial for disease management, but it remains an unresolved challenge. The aim of this study was to establish a radiomics model for the preoperative prediction of LN metastasis in hepatic AE patients. A total of 100 hepatic AE patients who underwent hepatectomy and hepatoduodenal ligament LN dissection at Qinghai Provincial People's Hospital between January 2016 and August 2023 were included in the study. The patients were randomly divided into a training set and a validation set at an 8:2 ratio. Radiomic features were extracted from three-dimensional images of the hepatoduodenal ligament LNs delineated on arterial phase computed tomography (CT) scans of hepatic AE patients. Least absolute shrinkage and selection operator (LASSO) regression was applied for data dimensionality reduction and feature selection. Multivariate logistic regression analysis was performed to develop a prediction model, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A total of 7 radiomics features associated with LN status were selected using LASSO regression. The classification performances of the training set and validation set were consistent, with area under the operating characteristic curve (AUC) values of 0.928 and 0.890, respectively. The model also demonstrated good stability in subsequent validation. In this study, we established and evaluated a radiomics-based prediction model for LN metastasis in patients with hepatic AE using CT imaging. Our findings may provide a valuable reference for clinicians to determine the occurrence of LN metastasis in hepatic AE patients preoperatively, and help guide the implementation of individualized surgical plans to improve patient prognosis.
Sections du résumé
BACKGROUND
BACKGROUND
Hepatic alveolar echinococcosis (AE) is a severe zoonotic parasitic disease, and accurate preoperative prediction of lymph node (LN) metastasis in AE patients is crucial for disease management, but it remains an unresolved challenge. The aim of this study was to establish a radiomics model for the preoperative prediction of LN metastasis in hepatic AE patients.
METHODS
METHODS
A total of 100 hepatic AE patients who underwent hepatectomy and hepatoduodenal ligament LN dissection at Qinghai Provincial People's Hospital between January 2016 and August 2023 were included in the study. The patients were randomly divided into a training set and a validation set at an 8:2 ratio. Radiomic features were extracted from three-dimensional images of the hepatoduodenal ligament LNs delineated on arterial phase computed tomography (CT) scans of hepatic AE patients. Least absolute shrinkage and selection operator (LASSO) regression was applied for data dimensionality reduction and feature selection. Multivariate logistic regression analysis was performed to develop a prediction model, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
RESULTS
RESULTS
A total of 7 radiomics features associated with LN status were selected using LASSO regression. The classification performances of the training set and validation set were consistent, with area under the operating characteristic curve (AUC) values of 0.928 and 0.890, respectively. The model also demonstrated good stability in subsequent validation.
CONCLUSION
CONCLUSIONS
In this study, we established and evaluated a radiomics-based prediction model for LN metastasis in patients with hepatic AE using CT imaging. Our findings may provide a valuable reference for clinicians to determine the occurrence of LN metastasis in hepatic AE patients preoperatively, and help guide the implementation of individualized surgical plans to improve patient prognosis.
Identifiants
pubmed: 39113113
doi: 10.1186/s40001-024-01999-x
pii: 10.1186/s40001-024-01999-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
409Subventions
Organisme : KunLun talents High-end Innovation and Entrepreneurship Talent Program
ID : Youth Talent character [2021] No.13
Organisme : KunLun talents High-end Innovation and Entrepreneurship Talent Program
ID : Youth Talent character [2021] No.13
Organisme : KunLun talents High-end Innovation and Entrepreneurship Talent Program
ID : Youth Talent character [2021] No.13
Organisme : KunLun talents High-end Innovation and Entrepreneurship Talent Program
ID : Youth Talent character [2021] No.13
Organisme : KunLun talents High-end Innovation and Entrepreneurship Talent Program
ID : Youth Talent character [2021] No.13
Organisme : KunLun talents High-end Innovation and Entrepreneurship Talent Program
ID : Youth Talent character [2021] No.13
Informations de copyright
© 2024. The Author(s).
Références
Brunetti E, Kern P, Vuitton DA. Expert consensus for the diagnosis and treatment of cystic and alveolar echinococcosis in humans. Acta Trop. 2010;114(1):1–16.
doi: 10.1016/j.actatropica.2009.11.001
pubmed: 19931502
Torgerson PR, Keller K, Magnotta M, Ragland N. The global burden of alveolar echinococcosis. PLoS Negl Trop Dis. 2010;4(6): e722.
doi: 10.1371/journal.pntd.0000722
pubmed: 20582310
pmcid: 2889826
Ohtani O, Ohtani Y. Lymph circulation in the liver. Anat Rec (Hoboken). 2008;291(6):643–52.
doi: 10.1002/ar.20681
pubmed: 18484610
Wen H, Vuitton L, Tuxun T, Li J, Vuitton DA, Zhang W, et al. Echinococcosis: advances in the 21st Century. Clin Microbiol Rev. 2019. https://doi.org/10.1128/CMR.00075-18 .
doi: 10.1128/CMR.00075-18
pubmed: 30760475
pmcid: 6431127
Li C, Zhang Y, Pang M, Zhang Y, Hu C, Fan H. Metabolic mechanism and pharmacological study of albendazole in secondary hepatic alveolar echinococcosis (HAE) model rats. Antimicrob Agents Chemother. 2024;68(5): e0144923.
doi: 10.1128/aac.01449-23
pubmed: 38501660
Buttenschoen K, Kern P, Reuter S, Barth TF. Hepatic infestation of Echinococcus multilocularis with extension to regional lymph nodes. Langenbecks Arch Surg. 2009;394(4):699–704.
doi: 10.1007/s00423-009-0481-0
pubmed: 19373487
Shen S, Kong J, Zhao J, Wang W. Outcomes of different surgical resection techniques for end-stage hepatic alveolar echinococcosis with inferior vena cava invasion. HPB. 2019;21(9):1219–29.
doi: 10.1016/j.hpb.2018.10.023
pubmed: 30782476
Wang J, Xing Y, Ren B, Xie WD, Wen H, Liu WY. Alveolar echinococcosis: correlation of imaging type with PNM stage and diameter of lesions. Chin Med J (Engl). 2011;124(18):2824–8.
pubmed: 22167824
Jiang Y, Li J, Wang J, Xiao H, Li T, Liu H, et al. Assessment of vascularity in hepatic alveolar echinococcosis: comparison of quantified dual-energy CT with histopathologic parameters. PLoS ONE. 2016;11(2): e0149440.
doi: 10.1371/journal.pone.0149440
pubmed: 26901164
pmcid: 4762698
Graeter T, Kratzer W, Oeztuerk S, Haenle MM, Mason RA, Hillenbrand A, et al. Proposal of a computed tomography classification for hepatic alveolar echinococcosis. World J Gastroenterol. 2016;22(13):3621–31.
doi: 10.3748/wjg.v22.i13.3621
pubmed: 27053854
pmcid: 4814648
Bian Y, Zheng Z, Fang X, Jiang H, Zhu M, Yu J, et al. Artificial intelligence to predict lymph node metastasis at CT in pancreatic ductal adenocarcinoma. Radiology. 2023;306(1):160–9.
doi: 10.1148/radiol.220329
pubmed: 36066369
Crombé A, Lucchesi C, Bertolo F, Kind M, Spalato-Ceruso M, Toulmonde M, et al. Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis. NPJ Precis Oncol. 2024;8(1):129.
doi: 10.1038/s41698-024-00616-8
pubmed: 38849448
pmcid: 11161510
Meng X, Xu H, Liang Y, Liang M, Song W, Zhou B, et al. Enhanced CT-based radiomics model to predict natural killer cell infiltration and clinical prognosis in non-small cell lung cancer. Front Immunol. 2023;14:1334886.
doi: 10.3389/fimmu.2023.1334886
pubmed: 38283362
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures. They Data Radiology. 2016;278(2):563–77.
pubmed: 26579733
Alhamzawi R, Ali HTM. The Bayesian adaptive lasso regression. Math Biosci. 2018;303:75–82.
doi: 10.1016/j.mbs.2018.06.004
pubmed: 29920251
Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34(18):2157–64.
doi: 10.1200/JCO.2015.65.9128
pubmed: 27138577
O’brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41(5):673–90.
doi: 10.1007/s11135-006-9018-6
Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008;8:53.
doi: 10.1186/1472-6947-8-53
pubmed: 19036144
pmcid: 2611975
Kramer AA, Zimmerman JE. Assessing the calibration of mortality benchmarks in critical care: the Hosmer-Lemeshow test revisited. Crit Care Med. 2007;35(9):2052–6.
doi: 10.1097/01.CCM.0000275267.64078.B0
pubmed: 17568333
Eckert J, Thompson RC, Mehlhorn H. Proliferation and metastases formation of larval Echinococcus multilocularis .I. Animal model, macroscopical and histological findings. Z Parasitenkd. 1983;69(6):737–48.
doi: 10.1007/BF00927423
pubmed: 6659651
Buttenschoen K, Gruener B, Carli Buttenschoen D, Reuter S, Henne-Bruns D, Kern P. Palliative operation for the treatment of alveolar echinococcosis. Langenbecks Arch Surg. 2009;394(1):199–204.
doi: 10.1007/s00423-008-0367-6
pubmed: 18575882
Bresson-Hadni S, Blagosklonov O, Knapp J, Grenouillet F, Sako Y, Delabrousse E, et al. Should possible recurrence of disease contraindicate liver transplantation in patients with end-stage alveolar echinococcosis? a 20-year follow-up study. Liver Transpl. 2011;17(7):855–65.
doi: 10.1002/lt.22299
pubmed: 21455928
Hillenbrand A, Beck A, Kratzer W, Graeter T, Barth TFE, Schmidberger J, et al. Impact of affected lymph nodes on long-term outcome after surgical therapy of alveolar echinococcosis. Langenbecks Arch Surg. 2018;403(5):655–62.
doi: 10.1007/s00423-018-1687-9
pubmed: 29909530
Wang F, Zhang B, Wu X, Liu L, Fang J, Chen Q, et al. Radiomic nomogram improves preoperative T category accuracy in locally advanced laryngeal carcinoma. Front Oncol. 2019;9:1064.
doi: 10.3389/fonc.2019.01064
pubmed: 31681598
pmcid: 6803547
Liu W, Delabrousse É, Blagosklonov O, Wang J, Zeng H, Jiang Y, et al. Innovation in hepatic alveolar echinococcosis imaging: best use of old tools, and necessary evaluation of new ones. Parasite. 2014;21:74.
doi: 10.1051/parasite/2014072
pubmed: 25531446
pmcid: 4273719
Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, et al. Introduction to radiomics. J Nucl Med. 2020;61(4):488–95.
doi: 10.2967/jnumed.118.222893
pubmed: 32060219
pmcid: 9374044
Liu C, Ding J, Spuhler K, Gao Y, Serrano Sosa M, Moriarty M, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2019;49(1):131–40.
doi: 10.1002/jmri.26224
pubmed: 30171822
Wang Q, Cui Y, Ren L, Wang H, Wang Z, Wang H, et al. Suspected regional lymph node metastasis in hepatic alveolar echinococcosis: a case report. Iran J Parasitol. 2020;15(1):138–41.
pubmed: 32489386
pmcid: 7244840
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.
doi: 10.1016/j.ejca.2011.11.036
pubmed: 22257792
pmcid: 4533986
Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR. Interpretation of radiomics features-a pictorial review. Comput Methods Programs Biomed. 2022;215: 106609.
doi: 10.1016/j.cmpb.2021.106609
pubmed: 34990929
Qiu H, Yang X, Shen S, Wang W. Relevance of regional lymph node invasion in radical hepatectomy and lymphadenectomy for alveolar echinococcosis. Asian J Surg. 2022;45(1):490–2.
doi: 10.1016/j.asjsur.2021.08.069
pubmed: 34635418
Li T, Ito A, Nakaya K, Qiu J, Nakao M, Zhen R, et al. Species identification of human echinococcosis using histopathology and genotyping in northwestern China. Trans R Soc Trop Med Hyg. 2008;102(6):585–90.
doi: 10.1016/j.trstmh.2008.02.019
pubmed: 18396303
Reinehr M, Micheloud C, Grimm F, Kronenberg PA, Grimm J, Beck A, et al. Pathology of echinococcosis: a morphologic and immunohistochemical study on 138 specimens with focus on the differential diagnosis between cystic and alveolar echinococcosis. Am J Surg Pathol. 2020;44(1):43–54.
doi: 10.1097/PAS.0000000000001374
pubmed: 31567204
Ali-Khan Z, Siboo R, Gomersall M, Faucher M. Cystolytic events and the possible role of germinal cells in metastasis in chronic alveolar hydatidosis. Ann Trop Med Parasitol. 1983;77(5):497–512.
doi: 10.1080/00034983.1983.11811742
pubmed: 6660955
Feng X, Qi X, Yang L, Duan X, Fang B, Gongsang Q, et al. Human cystic and alveolar echinococcosis in the Tibet autonomous region (TAR) China. J Helminthol. 2015;89(6):671–9.
doi: 10.1017/S0022149X15000656
pubmed: 26271332
pmcid: 4700907
Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61(13):R150-166.
doi: 10.1088/0031-9155/61/13/R150
pubmed: 27269645
pmcid: 4927328