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Authors: Nicola Dinapoli 1 ; Anna Rita Alitto 1 ; Mauro Vallati 2 ; Rosa Autorino 1 ; Roberto Gatta 1 ; Luca Boldrini 1 ; Andrea Damiani 1 ; Giovanna Mantini 1 and Vincenzo Valentini 1

Affiliations: 1 Università Cattolica del Sacro Cuore, Italy ; 2 University of Huddersfield, United Kingdom

Keyword(s): Dose Volume Histograms, Radiobiology, Radiotherapy, TCP, NTCP, Vdose, Dvolume.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Evaluation and Use of Healthcare IT ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: In this work a system for analysing radiotherapy treatment planning dose-volume data is proposed. The work starts from the definition of a framework inside a statistical scripting environment (R) used for creating a software package. The analysis of dose-volume data in radiotherapy of malignant tumours is mandatory for evaluating the prescribed treatment and for feedback analysis of outcome, both in the direction of tumour control and in detection of parameters for estimating and predicting toxicity outcome. The statistical analysis of large amount of clinical data can be slowed by the lack of practice in statistical tools needed, by clinicians, to perform such kind of analysis. This is the reason that lead our working group in the creation of such a tool. Finally an example of clinical application of our software is given for the analysis of the outcome of patients undergoing to radiotherapy for prostate cancer.

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Paper citation in several formats:
Dinapoli, N.; Alitto, A.; Vallati, M.; Autorino, R.; Gatta, R.; Boldrini, L.; Damiani, A.; Mantini, G. and Valentini, V. (2016). RadioBio data: A Moddicom Module to Predict Tumor Control Probability and Normal Tissue Complication Probability in Radiotherapy. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - HEALTHINF; ISBN 978-989-758-170-0; ISSN 2184-4305, SciTePress, pages 277-281. DOI: 10.5220/0005693502770281

@conference{healthinf16,
author={Nicola Dinapoli. and Anna Rita Alitto. and Mauro Vallati. and Rosa Autorino. and Roberto Gatta. and Luca Boldrini. and Andrea Damiani. and Giovanna Mantini. and Vincenzo Valentini.},
title={RadioBio data: A Moddicom Module to Predict Tumor Control Probability and Normal Tissue Complication Probability in Radiotherapy},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - HEALTHINF},
year={2016},
pages={277-281},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005693502770281},
isbn={978-989-758-170-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - HEALTHINF
TI - RadioBio data: A Moddicom Module to Predict Tumor Control Probability and Normal Tissue Complication Probability in Radiotherapy
SN - 978-989-758-170-0
IS - 2184-4305
AU - Dinapoli, N.
AU - Alitto, A.
AU - Vallati, M.
AU - Autorino, R.
AU - Gatta, R.
AU - Boldrini, L.
AU - Damiani, A.
AU - Mantini, G.
AU - Valentini, V.
PY - 2016
SP - 277
EP - 281
DO - 10.5220/0005693502770281
PB - SciTePress