- -

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study

Show simple item record

Files in this item

dc.contributor.author Ortiz-Ramón, Rafael es_ES
dc.contributor.author Larroza-Santacruz, Andrés es_ES
dc.contributor.author Ruiz-España, Silvia es_ES
dc.contributor.author Arana Fernandez De Moya, Estanislao es_ES
dc.contributor.author Moratal, David es_ES
dc.date.accessioned 2020-06-10T03:33:00Z
dc.date.available 2020-06-10T03:33:00Z
dc.date.issued 2018-11 es_ES
dc.identifier.issn 0938-7994 es_ES
dc.identifier.uri http://hdl.handle.net/10251/145873
dc.description.abstract [EN] Objective To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Methods Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. Results In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 +/- 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 +/- 0.054) and melanoma BM (eight features, AUC = 0.936 +/- 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 +/- 0.180). Conclusion Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. es_ES
dc.description.sponsorship This work has been partially funded by the Spanish Ministerio de Economia y Competitividad (MINECO) and FEDER funds under Grant BFU2015-64380-C2-2-R. Rafael Ortiz-Ramon was supported by grant ACIF/2015/078 from the Conselleria d'Educacio, Investigacio, Cultura i Esport of the Valencian Community (Spain). Andres Larroza was supported by grant FPU12/01140 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof European Radiology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Neoplasms es_ES
dc.subject Unknown primary es_ES
dc.subject Magnetic resonance imaging es_ES
dc.subject Image processing es_ES
dc.subject Computer-assisted es_ES
dc.subject Biomarkers es_ES
dc.subject Feasibility studies es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00330-018-5463-6 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU12%2F01140/ES/FPU12%2F01140/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//ACIF%2F2015%2F078/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BFU2015-64380-C2-2-R/ES/ANALISIS DE TEXTURAS EN IMAGEN CEREBRAL MULTIMODAL POR RESONANCIA MAGNETICA PARA UNA DETECCION TEMPRANA DE ALTERACIONES EN LA RED Y BIOMARCADORES DE ENFERMEDAD/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Ortiz-Ramón, R.; Larroza-Santacruz, A.; Ruiz-España, S.; Arana Fernandez De Moya, E.; Moratal, D. (2018). Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. European Radiology. 28(11):4514-4523. https://doi.org/10.1007/s00330-018-5463-6 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00330-018-5463-6 es_ES
dc.description.upvformatpinicio 4514 es_ES
dc.description.upvformatpfin 4523 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 28 es_ES
dc.description.issue 11 es_ES
dc.identifier.pmid 29761357 es_ES
dc.relation.pasarela S\371782 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Gavrilovic IT, Posner JB (2005) Brain metastases: epidemiology and pathophysiology. J Neurooncol 75:5–14 es_ES
dc.description.references Stelzer KJ (2013) Epidemiology and prognosis of brain metastases. Surg Neurol Int 4:S192–S202 es_ES
dc.description.references Soffietti R, Cornu P, Delattre JY et al (2006) EFNS Guidelines on diagnosis and treatment of brain metastases: report of an EFNS Task Force. Eur J Neurol 13:674–681 es_ES
dc.description.references Kaal ECA, Taphoorn MJB, Vecht CJ (2005) Symptomatic management and imaging of brain metastases. J Neurooncol 75:15–20 es_ES
dc.description.references Nayak L, Lee EQ, Wen PY (2012) Epidemiology of brain metastases. Curr Oncol Rep 14:48–54 es_ES
dc.description.references Bartelt S, Lutterbach J (2003) Brain metastases in patients with cancer of unknown primary. J Neurooncol 64:249–253 es_ES
dc.description.references Agazzi S, Pampallona S, Pica A et al (2004) The origin of brain metastases in patients with an undiagnosed primary tumor. Acta Neurochir (Wien) 146:153–157 es_ES
dc.description.references Pekmezci M, Perry A (2013) Neuropathology of brain metastases. Surg Neurol Int 4:245 es_ES
dc.description.references Zakaria R, Das K, Bhojak M et al (2014) The role of magnetic resonance imaging in the management of brain metastases: diagnosis to prognosis. Cancer Imaging 14:1–8 es_ES
dc.description.references Bekaert L, Emery E, Levallet G, Lechapt-Zalcman E (2017) Histopathologic diagnosis of brain metastases: current trends in management and future considerations. Brain Tumor Pathol 34:8–19 es_ES
dc.description.references Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577 es_ES
dc.description.references Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446 es_ES
dc.description.references Yip SSF, Aerts HJWL (2016) Applications and limitations of radiomics. Phys Med Biol 61:R150–R166 es_ES
dc.description.references Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248 es_ES
dc.description.references Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069 es_ES
dc.description.references Kassner A, Thornhill RE (2010) Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol 31:809–816 es_ES
dc.description.references Mahmoud-Ghoneim D, Toussaint G, Constans JM, De Certaines JD (2003) Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging 21:983–987 es_ES
dc.description.references Fetit AE, Novak J, Peet AC, Arvanitis TN (2015) Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumors. NMR Biomed 28:1174–1184 es_ES
dc.description.references Zacharaki EI, Wang S, Chawla S et al (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609–1618 es_ES
dc.description.references Georgiadis P, Cavouras D, Kalatzis I et al (2009) Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods. Magn Reson Imaging 27:120–130 es_ES
dc.description.references Larroza A, Moratal D, Paredes-Sánchez A et al (2015) Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. J Magn Reson Imaging 42:1362–1368 es_ES
dc.description.references Li Z, Mao Y, Li H et al (2016) Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR. Magn Reson Med 76:1410–1419 es_ES
dc.description.references Fink KR, Fink JR (2013) Imaging of brain metastases. Surg Neurol Int 4:S209–S219 es_ES
dc.description.references Larroza A, Bodí V, Moratal D (2016) Texture analysis in magnetic resonance imaging: review and considerations for future applications. In: Assessment of cellular and organ function and dysfunction using direct and derived MRI methodologies. InTech, Rijeka, Croatia, pp 75–106 es_ES
dc.description.references Leite M, Rittner L, Appenzeller S et al (2015) Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging. J Med Imaging 2:14002 es_ES
dc.description.references Mahmoud-Ghoneim D, Alkaabi MK, De Certaines JD, Goettsche F-M (2008) The impact of image dynamic range on texture classification of brain white matter. BMC Med Imaging 8:1–8 es_ES
dc.description.references Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Müller H (2014) Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal 18:176–196 es_ES
dc.description.references Ellingson BM, Bendszus M, Boxerman J et al (2015) Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol 17:1188–1198 es_ES
dc.description.references Mayerhoefer ME, Breitenseher MJ, Kramer J et al (2005) Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging 22:674–680 es_ES
dc.description.references Waugh SA, Lerski RA, Bidaut L, Thompson AM (2011) The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. Med Phys 38:5058–5066 es_ES
dc.description.references Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10:266–277 es_ES
dc.description.references Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91 es_ES
dc.description.references Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50:92–98 es_ES
dc.description.references Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496 es_ES
dc.description.references Kuhn M, Johnson K (2013) Data pre-processing. In: Applied predictive modeling, 1st ed. Springer, New York, NY, pp 27–59 es_ES
dc.description.references Fernández-Delgado M, Cernadas E, Barro S et al (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181 es_ES
dc.description.references Caruana R, Karampatziakis N, Yessenalina A (2008) An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th international conference on Machine learning - ICML ’08. ACM Press, Helsinki, Finland, pp 96–103 es_ES
dc.description.references Kuhn M, Johnson K (2013) Over-fitting and model tuning. In: Applied predictive modeling, 1st ed. Springer, New York, NY, pp 61–92 es_ES
dc.description.references Kuhn M, Johnson K (2013) An introduction to feature selection. In: Applied predictive modeling, 1st ed. Springer, New York, NY, pp 487–519 es_ES
dc.description.references Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci U S A 99:6562–6566 es_ES
dc.description.references Provost F, Domingos P (2003) Tree induction for probability-based ranking. Mach Learn 52:199–215 es_ES
dc.description.references Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26 es_ES
dc.description.references Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach. In: 2017 I.E. 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, pp 1213–1216 es_ES
dc.description.references Ortiz-Ramon R, Larroza A, Arana E, Moratal D (2017) A radiomics evaluation of 2D and 3D MRI texture features to classify brain metastases from lung cancer and melanoma. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Seogwipo, pp 493–496 es_ES
dc.description.references Béresová M, Larroza A, Arana E, et al (2017) 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution. MAGMA 1–10 es_ES
dc.description.references Ahmed A, Gibbs P, Pickles M, Turnbull L (2013) Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 38:89–101 es_ES
dc.description.references Chen W, Giger ML, Li H et al (2007) Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 58:562–571 es_ES


This item appears in the following Collection(s)

Show simple item record