9786203196504 - mri texture analysis for tumor recurrence prediction: a study with pituitary macroadenomas de f. machado, leonardo; c. l. elias, paulla; murta jr., luis otavio (5 resultados)
MRI Texture Analysis for Tumor Recurrence Prediction
Leonardo F. Machado|Paulla C. L. Elias|Luis Otavio Murta Jr.
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Librería: moluna, Greven, Alemaniamoluna
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: Neu. MRI Texture Analysis for Tumor Recurrence Prediction | A study with pituitary macroadenomas | Leonardo F. Machado (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203196504 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osn…abrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The present work proposes the usage of texture features computationally extracted from MRI as imaging biomarkers in the prediction of tumor recurrence in patients with non-functioning pituitary adenomas (NFPA). With this purpose, th…is study analyzed MR images from patients of NFPA retrospectively separated in groups: the recurrent patient group, formed by patients who exhibited tumor recurrence after the first surgical approach, and the stable patient group formed by patients with lesions considered stable. The preoperative MR images were used to extract numerical textural features. Clinical features were also considered in the study. The features were tested through conventional univariate statistical tests and were used to build machine learning prediction models. The findings of the study imply that textural features are useful in the prediction of tumor recurrence after first surgery in NFPA patients. And that the prediction power of those features can be observed with both conventional univariate statistical tests and multivariate analyses through machine learning algorithms. 140 pp. Englisch.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The present work proposes the usage of texture features computationally extracted from MRI as imaging biomarkers in the prediction of tumor recurrence in patients with non-functioning pituitary adenomas (NFPA). With this purpose, this s…tudy analyzed MR images from patients of NFPA retrospectively separated in groups: the recurrent patient group, formed by patients who exhibited tumor recurrence after the first surgical approach, and the stable patient group formed by patients with lesions considered stable. The preoperative MR images were used to extract numerical textural features. Clinical features were also considered in the study. The features were tested through conventional univariate statistical tests and were used to build machine learning prediction models. The findings of the study imply that textural features are useful in the prediction of tumor recurrence after first surgery in NFPA patients. And that the prediction power of those features can be observed with both conventional univariate statistical tests and multivariate analyses through machine learning algorithms.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 62,64
Envío por EUR 61,14Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The present work proposes the usage of texture features computationally extracted from MRI as imaging biomarkers in the prediction of tumor recurrence in patients with non-functioning pituitary adenomas (NFPA). With this purpose, this st…udy analyzed MR images from patients of NFPA retrospectively separated in groups: the recurrent patient group, formed by patients who exhibited tumor recurrence after the first surgical approach, and the stable patient group formed by patients with lesions considered stable. The preoperative MR images were used to extract numerical textural features. Clinical features were also considered in the study. The features were tested through conventional univariate statistical tests and were used to build machine learning prediction models. The findings of the study imply that textural features are useful in the prediction of tumor recurrence after first surgery in NFPA patients. And that the prediction power of those features can be observed with both conventional univariate statistical tests and multivariate analyses through machine learning algorithms.
