Texture analysis and multiple-instance learning for the classification of malignant lymphomas

Marco Lippi, Stefania Gianotti, Angelo Fama, Massimiliano Casali, Elisa Barbolini, Angela Ferrari, Federica Fioroni, Mauro Iori, Stefano Luminari, Massimo Menga, Francesco Merli, Valeria Trojani, Annibale Versari, Magda Zanelli, Marco Bertolini

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalComputer Methods and Programs in Biomedicine
Publication statusE-pub ahead of print - Oct 23 2019


  • Malignant lymphomas
  • Multiple-instance learning
  • Texture analysis

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics


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