Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features.
Craniopharyngioma
Magnetic resonance imaging
Pituitary adenoma
Texture analysis
Journal
Japanese journal of radiology
ISSN: 1867-108X
Titre abrégé: Jpn J Radiol
Pays: Japan
ID NLM: 101490689
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
21
03
2020
accepted:
14
07
2020
pubmed:
28
7
2020
medline:
4
5
2021
entrez:
26
7
2020
Statut:
ppublish
Résumé
To investigate differences between pituitary adenoma and craniopharyngioma on magnetic resonance imaging (MRI) with image features and three-dimensional texture features. A total of 126 patients diagnosed with pituitary adenoma (N = 63) or craniopharyngioma (N = 63) were enrolled. Qualitative magnetic resonance (MR) image features and texture features of tumors were extracted from preoperative MRI and evaluated using chi-square test or Mann-Whitney U test. Binary logistic regression analyses were performed to assess their abilities as independent diagnostic predictors, and ROC analyses were conducted to evaluate the diagnostic value of significant features. Mann-Whitney U test and ROC analyses were performed to explore the relationship between MR image features and texture features. Five MR image features were suggested to be significantly different between pituitary adenoma and craniopharyngioma. Three texture features from contrast-enhanced T1WI (HISTO-Skewness, GLCM-Contrast and GLCM-Energy), two texture features from T2WI (HISTO-Skewness and GLCM-Contrast) showed significant differences between two types of tumors. Logistic regression analyses suggested GLCM-Energy from contrast-enhanced T1WI, HISTO-Skewness and GLCM-Contrast from T2WI could be taken as independent predictors. Moreover, HISTO-Skewness and GLCM-Contrast from T2WI were found to be significantly related to cystic change. MR image features and texture features were associated with each other, and both types of features represented feasible diagnostic value in discrimination between pituitary adenoma and craniopharyngioma.
Identifiants
pubmed: 32710133
doi: 10.1007/s11604-020-01021-4
pii: 10.1007/s11604-020-01021-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1125-1134Subventions
Organisme : 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University
ID : ZYJC18007
Organisme : Key research and development project of science and technology department of Sichuan Province
ID : 2019YFS0392
Références
Jagannathan J, Kanter AS, Sheehan JP, Jane JA Jr. Laws ER, Jr Benign brain tumors sellar/parasellar tumors. Neurol Clin. 2007;25(4):1231–49. https://doi.org/10.1016/j.ncl.2007.07.003 .
doi: 10.1016/j.ncl.2007.07.003
pubmed: 17964033
Laws ER Jr, Thapar K. Pituitary surgery. Endocrinol Metab Clin North Am. 1999;28(1):119–31.
doi: 10.1016/S0889-8529(05)70059-1
Müller HL, Merchant TE, Warmuth-Metz M, Martinez-Barbera J-P, Puget S. Craniopharyngioma. Nat Rev Dis Primers. 2019;5(1):75. https://doi.org/10.1038/s41572-019-0125-9 .
doi: 10.1038/s41572-019-0125-9
pubmed: 31699993
Davis JR, Farrell WE, Clayton RN. Pituitary tumours. Reproduction. 2001;121(3):363–71.
doi: 10.1530/rep.0.1210363
Muller HL. Craniopharyngioma. Endocr Rev. 2014;35(3):513–43. https://doi.org/10.1210/er.2013-1115 .
doi: 10.1210/er.2013-1115
pubmed: 24467716
Molitch ME. Diagnosis and treatment of pituitary adenomas a review. JAMA. 2017;317(5):516–24. https://doi.org/10.1001/jama.2016.19699 .
doi: 10.1001/jama.2016.19699
pubmed: 28170483
Raman SP, Chen Y, Schroeder JL, Huang P, Fishman EK. CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. Acad Radiol. 2014;21(12):1587–96. https://doi.org/10.1016/j.acra.2014.07.023 .
doi: 10.1016/j.acra.2014.07.023
pubmed: 25239842
pmcid: 4352301
Eliat PA, Olivie D, Saikali S, Carsin B, Saint-Jalmes H, de Certaines JD. Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma. Neurol Res Int. 2012;2012:195176. https://doi.org/10.1155/2012/195176 .
doi: 10.1155/2012/195176
pubmed: 22203901
Lopes R, Ayache A, Makni N, Puech P, Villers A, Mordon S, et al. Prostate cancer characterization on MR images using fractal features. Med Phys. 2011;38(1):83–95. https://doi.org/10.1118/1.3521470 .
doi: 10.1118/1.3521470
pubmed: 21361178
Holli K, Laaperi AL, Harrison L, Luukkaala T, Toivonen T, Ryymin P, et al. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol. 2010;17(2):135–41. https://doi.org/10.1016/j.acra.2009.08.012 .
doi: 10.1016/j.acra.2009.08.012
pubmed: 19945302
Alis D, Bagcilar O, Senli YD, Yergin M, Isler C, Kocer N, et al. Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas. Jpn J Radiol. 2020;38(2):135–43. https://doi.org/10.1007/s11604-019-00902-7 .
doi: 10.1007/s11604-019-00902-7
pubmed: 31741126
Briet C, Salenave S, Bonneville JF, Laws ER, Chanson P. Pituitary apoplexy. Endocr Rev. 2015;36(6):622–45. https://doi.org/10.1210/er.2015-1042 .
doi: 10.1210/er.2015-1042
pubmed: 26414232
Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. Lifex a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–9. https://doi.org/10.1158/0008-5472.can-18-0125 .
doi: 10.1158/0008-5472.can-18-0125
pubmed: 29959149
pmcid: 29959149
Lakhman Y, Veeraraghavan H, Chaim J, Feier D, Goldman DA, Moskowitz CS, et al. Differentiation of uterine leiomyosarcoma from atypical leiomyoma diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis. Eur Radiol. 2017;27(7):2903–15. https://doi.org/10.1007/s00330-016-4623-9 .
doi: 10.1007/s00330-016-4623-9
pubmed: 27921159
Fujima N, Homma A, Harada T, Shimizu Y, Tha KK, Kano S, et al. The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies. Cancer Imaging. 2019;1(1):5. https://doi.org/10.1186/s40644-019-0193-9 .
doi: 10.1186/s40644-019-0193-9
Rodriguez Gutierrez D, Awwad A, Meijer L, Manita M, Jaspan T, Dineen RA, et al. Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors. AJNR Am J Neuroradiol. 2014;35(5):1009–155. https://doi.org/10.3174/ajnr.A3784 .
doi: 10.3174/ajnr.A3784
pubmed: 24309122
Yildiz AE, Oguz KK, Fitoz S. Suprasellar masses in children characteristic MR imaging features. J Neuroradiol. 2016;43(4):246–59. https://doi.org/10.1016/j.neurad.2016.03.009 .
doi: 10.1016/j.neurad.2016.03.009
pubmed: 27131616
Muller HL, Gebhardt U, Faldum A, Warmuth-Metz M, Pietsch T, Pohl F, et al. Xanthogranuloma, Rathke's cyst, and childhood craniopharyngioma results of prospective multinational studies of children and adolescents with rare sellar malformations. J Clin Endocrinol Metab. 2012;97(11):3935–43. https://doi.org/10.1210/jc.2012-2069 .
doi: 10.1210/jc.2012-2069
pubmed: 22969141
Guaraldi F, Prencipe N, di Giacomo V, Scanarini M, Gasco V, Gardiman MP, et al. Association of craniopharyngioma and pituitary adenoma. Endocrine. 2013;44(1):59–655. https://doi.org/10.1007/s12020-013-9892-3 .
doi: 10.1007/s12020-013-9892-3
pubmed: 23377700
Choi SH, Kwon BJ, Na DG, Kim JH, Han MH, Chang KH. Pituitary adenoma craniopharyngioma and Rathke cleft cyst involving both intrasellar and suprasellar regions differentiation using MRI. Clin Radiol. 2007;62(5):453–62.
doi: 10.1016/j.crad.2006.12.001
Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501(7467):338–45. https://doi.org/10.1038/nature12625 .
doi: 10.1038/nature12625
pubmed: 24048066
pmcid: 24048066
Just N. Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer. 2014;111(12):2205–13. https://doi.org/10.1038/bjc.2014.512 .
doi: 10.1038/bjc.2014.512
pubmed: 25268373
pmcid: 4264439
Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New Eng J Med. 2012;366(10):883–92. https://doi.org/10.1056/NEJMoa1113205 .
doi: 10.1056/NEJMoa1113205
pubmed: 22397650
Nardone V, Tini P, Nioche C, Mazzei MA, Carfagno T, Battaglia G, et al. Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med (Torino). 2018;123(6):415–23. https://doi.org/10.1007/s11547-017-0850-7 .
doi: 10.1007/s11547-017-0850-7
Areeckal AS, Jayasheelan N, Kamath J, Zawadynski S, Kocher M, David SS. Early diagnosis of osteoporosis using radiogrammetry and texture analysis from hand and wrist radiographs in Indian population. Osteoporosis International A Journal Established As Result Of Cooperation Between The European Foundation For Osteoporosis And The National Osteoporosis Foundation Of The USA. 2018;29(3):665–73. https://doi.org/10.1007/s00198-017-4328-1 .
doi: 10.1007/s00198-017-4328-1
Li Z, Yu L, Wang X, Yu H, Gao Y, Ren Y, et al. Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Clin breast cancer. 2018;18(4):e621–e627627. https://doi.org/10.1016/j.clbc.2017.11.004 .
doi: 10.1016/j.clbc.2017.11.004
pubmed: 29199085
Lisson CS, Lisson CG, Flosdorf K, Mayer-Steinacker R, Schultheiss M, von Baer A, et al. Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma a pilot study. Eur Radiol. 2018;28(2):468–77. https://doi.org/10.1007/s00330-017-5014-6 .
doi: 10.1007/s00330-017-5014-6
pubmed: 28884356
Verma RK, Wiest R, Locher C, Heldner MR, Schucht P, Raabe A, et al. Differentiating enhancing multiple sclerosis lesions glioblastoma and lymphoma with dynamic texture parameters analysis (DTPA) a feasibility study. Med Phys. 2017;44(8):4000–8. https://doi.org/10.1002/mp.12356 .
doi: 10.1002/mp.12356
pubmed: 28543071