MRI Texture Analysis for Tumor Recurrence Prediction: A study with pituitary macroadenomas - Softcover
Synopsis
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 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.
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MRI Texture Analysis for Tumor Recurrence Prediction
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LAP LAMBERT Academic Publishing Jan 2021, 2021
ISBN 10: 6203196509
ISBN 13: 9786203196504
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Taschenbuch. Condition: 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, this 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. Seller Inventory # 9786203196504
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MRI Texture Analysis for Tumor Recurrence Prediction
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ISBN 10: 6203196509
ISBN 13: 9786203196504
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MRI Texture Analysis for Tumor Recurrence Prediction
Published by
LAP LAMBERT Academic Publishing Jan 2021, 2021
ISBN 10: 6203196509
ISBN 13: 9786203196504
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: 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 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch. Seller Inventory # 9786203196504
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MRI Texture Analysis for Tumor Recurrence Prediction | A study with pituitary macroadenomas
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203196509
ISBN 13: 9786203196504
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Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: 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 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 119642916
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MRI Texture Analysis for Tumor Recurrence Prediction : A study with pituitary macroadenomas
Published by
LAP LAMBERT Academic Publishing, 2021
ISBN 10: 6203196509
ISBN 13: 9786203196504
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: 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 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. Seller Inventory # 9786203196504