Towards a modular decision support system for radiomics: A case study on rectal cancer

Roberto Gatta, Mauro Vallati*, Nicola Dinapoli, Carlotta Masciocchi, Jacopo Lenkowicz, Davide Cusumano, Calogero Casa, Alessandra Farchione, Andrea Damiani, Johan van Soest, Andre Dekker, Vincenzo Valentini

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Following the personaliied medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics. A number of tools are available for supporting some of the steps of the radiomics process, but there is a lack of approaches which are able to deal with all the steps of the process.

In this paper, we introduce a medical agent-based decision support system capable of handling the whole radiomics process. The proposed system is tested on two independent data sets of patients treated for rectal cancer. Experimental results indicate that the system is able to generate highly performant centre-specific predictive model, and show the issues related to differences in data sets collected by different centres, and how such issues can affect the performance of the generated predictive models.

Original languageEnglish
Pages (from-to)145-153
Number of pages9
JournalArtificial Intelligence in Medicine
Publication statusPublished - May 2019


  • Decision support systems
  • Radiomics
  • Predictive models
  • Image feature analysis

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