Texture analysis and multiple-instance learning for the classification of malignant lymphomas
| Autor: | Magda Zanelli, Federica Fioroni, Marco Bertolini, Annibale Versari, Massimiliano Casali, Elisa Barbolini, Marco Lippi, Angela Ferrari, Mauro Iori, Valeria Trojani, Massimo Menga, Stefano Luminari, Francesco Merli, Stefania Gianotti, Angelo Fama |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Support Vector Machine Lymphoma Follicular lymphoma Datasets as Topic Health Informatics Malignant lymphomas Multiple-instance learning Texture analysis Sensitivity and Specificity Machine Learning hemic and lymphatic diseases medicine Humans Multiple-instance learning medicine.diagnostic_test business.industry Malignant lymphomas medicine.disease Computer Science Applications Random forest Support vector machine Texture analysis Positron emission tomography Positron-Emission Tomography Mantle cell lymphoma Radiology Differential diagnosis Tomography X-Ray Computed business Diffuse large B-cell lymphoma Algorithms Software |
| Zdroj: | Computer Methods and Programs in Biomedicine. 185:105153 |
| ISSN: | 0169-2607 |
| Popis: | Background and objectives Malignant lymphomas are cancers of the immune system and are characterized by enlarged lymph nodes that typically spread across many different sites. Many different histological subtypes exist, whose diagnosis is typically based on sampling (biopsy) of a single tumor site, whereas total body examinations with computed tomography and positron emission tomography, though not diagnostic, are able to provide a comprehensive picture of the patient. In this work, we exploit a data-driven approach based on multiple-instance learning algorithms and texture analysis features extracted from positron emission tomography, to predict differential diagnosis of the main malignant lymphomas subtypes. Methods We exploit a multiple-instance learning setting where support vector machines and random forests are used as classifiers both at the level of single VOIs (instances) and at the level of patients (bags). We present results on two datasets comprising patients that suffer from four different types of malignant lymphomas, namely diffuse large B cell lymphoma, follicular lymphoma, Hodgkin’s lymphoma, and mantle cell lymphoma. Results Despite the complexity of the task, experimental results show that, with sufficient data samples, some cancer subtypes, such as the Hodgkin’s lymphoma, can be identified from texture information: in particular, we achieve a 97.0% of sensitivity (recall) and a 94.1% of predictive positive value (precision) on a dataset that consists in 60 patients. Conclusions The presented study indicates that texture analysis features extracted from positron emission tomography, combined with multiple-instance machine learning algorithms, can be discriminating for different malignant lymphomas subtypes. |
| Databáze: | OpenAIRE |
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