Radiomics in pulmonary neuroendocrine tumours (NETs).
Computed tomography
Ki-67
Lung carcinoids
Radiomics
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
received:
25
01
2022
accepted:
14
04
2022
pubmed:
11
5
2022
medline:
27
5
2022
entrez:
10
5
2022
Statut:
ppublish
Résumé
The aim of this single-centre, observational, retrospective study is to find a correlation using Radiomics between the analysis of CT texture features of primary lesion of neuroendocrine (NET) lung cancer subtypes (typical and atypical carcinoids, large and small cell neuroendocrine carcinoma), Ki-67 index and the presence of lymph nodal mediastinal metastases. Twenty-seven patients (11 males and 16 females, aged between 48 and 81 years old-average age of 70,4 years) with histological diagnosis of pulmonary NET with known Ki-67 status and metastases who have performed pre-treatment CT in our department were included. All examinations were performed with the same CT scan (Sensation 16-slice, Siemens). The study protocol was a baseline scan followed by 70 s delay acquisition after administration of intravenous contrast medium. After segmentation of primary lesions, quantitative texture parameters of first and higher orders were extracted. Statistics nonparametric tests and linear correlation tests were conducted to evaluate the relationship between different textural characteristics and tumour subtypes. Statistically significant (p < 0.05) differences were seen in post-contrast enhanced CT in multiple first and higher-order extracted parameters regarding the correlation with classes of Ki-67 index values. Statistical analysis for direct acquisitions was not significant. Concerning the correlation with the presence of metastases, one histogram feature (Skewness) and one feature included in the Gray-Level Co-occurrence Matrix (ClusterShade) were significant on contrast-enhanced CT only. CT texture analysis may be used as a valid tool for predicting the subtype of lung NET and its aggressiveness.
Identifiants
pubmed: 35538389
doi: 10.1007/s11547-022-01494-5
pii: 10.1007/s11547-022-01494-5
pmc: PMC9130162
doi:
Substances chimiques
Ki-67 Antigen
0
Types de publication
Journal Article
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
609-615Informations de copyright
© 2022. The Author(s).
Références
Capella C, Heitz PU, Hofler H et al (1994) Revised classification of neuroendocrine tumours of the lung, pancreas and gut. Digestion 55(suppl 3):11–23
doi: 10.1159/000201197
Beasley MB, Thunnissen FB, Hasleton PhS et al (2004) Carcinoid tumour. In: Travis WD, Brambilla E, Muller-Harmelink HK et al (eds) Pathology and genetics of tumours of the lung, pleura, thymus and heart. IARC Press, Lyon, pp 59–62
Travis WD, Brambilla E, Burke A et al (2015) Introduction to the 2015 World Health Organization classification of tumors of the lung, pleura, thymus and heart. J Thorac Oncol 10(9):1240–1242
doi: 10.1097/JTO.0000000000000663
Klimstra DS (2016) Pathologic classifcation of neuroendocrine neoplasms. Hematol Oncol Clin North Am 30:1–19
doi: 10.1016/j.hoc.2015.08.005
Klöppel G (2017) Neuroendocrine neoplasms: dichotomy, origin and classification. Visc Med 33:324–330
doi: 10.1159/000481390
Yao JC, Hassan M, Phan A et al (2008) One hundred years after “carcinoid”: epidemiology of and prognostic factors for neuroendocrine tumors in 35,825 cases in the United States. J Clin Oncol 26(18):3063–3072
doi: 10.1200/JCO.2007.15.4377
Hilal T (2017) Current understanding and approach to well differentiated lung neuroendocrine tumors: an update on classifcation and management. Ther Adv Med Oncol 9:189–199
doi: 10.1177/1758834016678149
Chong S, Lee KS, Chung MJ et al (2006) Neuroendocrine tumors of the lung: clinical, pathologic, and imaging findings. Radiographics 26:41–57
doi: 10.1148/rg.261055057
Crocetti E, Paci E (2003) Malignant carcinoids in the USA, SEER 1992–1999. An epidemiological study with 6830 cases. Eur J Cancer Prev 12(3):191–194
doi: 10.1097/00008469-200306000-00004
Devesa SS, Bray F, Vizcaino AP, Parkin DM (2005) International lung cancer trends by histologic type: male: female differences diminishing and adenocarcinoma rates rising. Int J Cancer 117:294–299
doi: 10.1002/ijc.21183
Rosado de Christenson ML, Abbott GF, Kirejczyk WM, Galvin JR, Travis WD (1999) Thoracic carcinoids: radiologic-pathologic correlation. Radiographics 19:707–736
doi: 10.1148/radiographics.19.3.g99ma11707
Malla S, Kumar P, Madhusudhan KS (2020) Radiology of the neuroendocrine neoplasms of the gastrointestinal tract: a comprehensive review. Abdom Radiol. https://doi.org/10.1007/s00261-020-02773-3
Skov BG, Krasnik M, Lantuejoul S, Skov T, Brambilla E (2008) Reclassification of neuroendocrine tumors improves the separation of carcinoids and the prediction of survival. J Thorac Oncol 3(12):1410–1415
doi: 10.1097/JTO.0b013e31818e0dd4
Caplin ME, Baudin E, Ferolla P et al (2015) Pulmonary neuroendocrine (Carcinoid) tumors: European neuroendocrine tumor society expert consensus and recommendations for best practice for typical and atypical pulmonary carcinoid. Ann Oncol 26:1604–1620
doi: 10.1093/annonc/mdv041
Ramirez RA, Chauhan A, Gimenez J et al (2017) Management of pulmonary neuroendocrine tumors. Rev Endocr Metab Disord 18:433–442
doi: 10.1007/s11154-017-9429-9
Jeung MY, Gasser B, Gangi A et al (2002) Bronchial carcinoid tumors of the thorax: spectrum of radiologic findings. Radiographics 22(2):351–365
doi: 10.1148/radiographics.22.2.g02mr01351
Nessi R, Basso Ricci P, Basso Ricci S et al (1991) Bronchial carcinoid tumors: radiologic observations in 49 cases. J Thorac Imaging 6:47–53
doi: 10.1097/00005382-199104000-00011
Hassani C, Varghese BA, Nieva J, Duddalwar V (2019) Radiomics in pulmonary lesion imaging. Am J Roentgenol AJR. https://doi.org/10.2214/ajr.18.20623
Zou J, Lv T, Zhu S et al (2017) Computed tomography and clinical features associated with epidermal growth factor receptor mutation status in stage I/II lung adenocarcinoma. Thorac Cancer 8:260–270
doi: 10.1111/1759-7714.12436
Ravanelli M, Farina d, Morassi M, et al (2013) Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy. Eur Radiol 23:3450–3455
doi: 10.1007/s00330-013-2965-0
Ganeshan B, Goh V, Mandeville HC et al (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336
doi: 10.1148/radiol.12112428
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577
doi: 10.1148/radiol.2015151169
Grazzini G, Danti G, Cozzi D et al (2019) Diagnostic imaging of gastrointestinal neuroendocrine tumours (GI-NETs): relationship between MDCT features and 2010 WHO classification. Radiol Med 124:94–102
doi: 10.1007/s11547-018-0946-8
Abenavoli E, Linguanti F, Briganti V et al (2020) Typical lung carcinoids: review of classification, radiological signs and nuclear imaging findings. Clin Translat Imaging. https://doi.org/10.1007/s40336-020-00364-2
doi: 10.1007/s40336-020-00364-2
Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network. Magn Reson Imaging 30(9):1323–1341 (PMID: 22770690. PMCID: PMC3466397)
doi: 10.1016/j.mri.2012.05.001
Van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Can Res 77(21):e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339
doi: 10.1158/0008-5472.CAN-17-0339
Danti G, Berti V, Abenavoli E et al (2020) Diagnostic imaging of typical lung carcinoids: relationship between MDCT,
doi: 10.1007/s11547-020-01172-4
Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 31(1049):1058
Kamiya A, Murayama S, Kamiya S et al (2014) Kurtosis and skewness assessment of solid lung nodule density histograms: differentiating malignant from benign nodules on CT. Jpn J Radiol 32:14–21
doi: 10.1007/s11604-013-0264-y
Sollini M, Antunovic L, Chiti A, Kirienko M (2019) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 46:2656–2672
doi: 10.1007/s00259-019-04372-x
Isaksson LJ, Raimondi S, Botta F et al (2020) Effects of MRI image normalization techniques in prostate cancer radiomics. Phys Med 71:7–13. https://doi.org/10.1016/j.ejmp.2020.02.007
doi: 10.1016/j.ejmp.2020.02.007
pubmed: 32086149
Peeken JC, Bernhofer M, Wiestler B et al (2018) Radiomics in radiooncology—challenging the medical physicist. Phys Med 48:27–36. https://doi.org/10.1016/j.ejmp.2018.03.012
doi: 10.1016/j.ejmp.2018.03.012
pubmed: 29728226
Neri E, Coppola F, Miele V, Bibbolino C, Grassi R (2020) Artificial intelligence: Who is responsible for diagnosis? Radiol Med 125:517–521
doi: 10.1007/s11547-020-01135-9
Grassi R, Miele V, Giovagnoni A (2019) Artificial intelligence: a challenge for third millennium radiologist. Radiol Med 124:241–242
doi: 10.1007/s11547-019-00990-5
Hermans BCM, Sanduleanu S, Derks JL et al (2020) Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LNEC). Lung Cancer 148:94–99
doi: 10.1016/j.lungcan.2020.08.006