Abstract
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.
| Language | English |
|---|---|
| Pages | 145-153 |
| Number of pages | 9 |
| Journal | Artificial Intelligence in Medicine |
| Volume | 96 |
| Early online date | 4 Oct 2018 |
| DOIs | |
| Publication status | Published - May 2019 |
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Towards a modular decision support system for radiomics : A case study on rectal cancer. / Gatta, Roberto; Vallati, Mauro; Dinapoli, Nicola; Masciocchi, Carlotta; Lenkowicz, Jacopo; Cusumano, Davide; Casà, Calogero; Farchione, Alessandra; Damiani, Andrea; van Soest, Johan; Dekker, Andre; Valentini, Vincenzo.
In: Artificial Intelligence in Medicine, Vol. 96, 05.2019, p. 145-153.Research output: Contribution to journal › Article
TY - JOUR
T1 - Towards a modular decision support system for radiomics
T2 - Artificial Intelligence in Medicine
AU - Gatta, Roberto
AU - Vallati, Mauro
AU - Dinapoli, Nicola
AU - Masciocchi, Carlotta
AU - Lenkowicz, Jacopo
AU - Cusumano, Davide
AU - Casà, Calogero
AU - Farchione, Alessandra
AU - Damiani, Andrea
AU - van Soest, Johan
AU - Dekker, Andre
AU - Valentini, Vincenzo
PY - 2019/5
Y1 - 2019/5
N2 - Following the personalized 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.
AB - Following the personalized 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.
KW - Decision Support Systems
KW - Radiomics
KW - Predictive Models
KW - Image Feature Analysis
UR - http://www.scopus.com/inward/record.url?scp=85054192769&partnerID=8YFLogxK
U2 - 10.1016/j.artmed.2018.09.003
DO - 10.1016/j.artmed.2018.09.003
M3 - Article
VL - 96
SP - 145
EP - 153
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
SN - 0933-3657
ER -