PACE: A Probabilistic Atlas for Normal Tissue Complication Estimation in Radiation Oncology(24 visite) Palma G, Monti S, Buonanno A, Pacelli R, Cella L
Front Oncol (ISSN: 2234-943xlinking), 2019 Mar 13; 9: 130-130.
Tipo di articolo: Journal Article
Impact factor: 4.137, Impact factor a 5 anni: 0
Url: Non disponibile.
Parole chiave: Normal Tissue Complication Probability, Radiation Therapy, Radiation-Induced Morbidity, Radio-Sensitivity, Voxel-Based Analysis
*** IBB - CNR *** Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy., IRCCS SDN, Naples, Italy., Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, Italy., Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy.,
In radiation oncology, the need for a modern Normal Tissue Complication Probability (NTCP) philosophy to include voxel-based evidence on organ radio-sensitivity (RS) has been acknowledged. Here a new formalism (Probabilistic Atlas for Complication Estimation, PACE) to predict radiation-induced morbidity (RIM) is presented. The adopted strategy basically consists in keeping the structure of a classical, phenomenological NTCP model, such as the Lyman-Kutcher-Burman (LKB), and replacing the dose distribution with a collection of RIM odds, including also significant non-dosimetric covariates, as input of the model framework. The theory was first demonstrated in silico on synthetic dose maps, classified according to synthetic outcomes. PACE was then applied to a clinical dataset of thoracic cancer patients classified for lung fibrosis. LKB models were trained for comparison. Overall, the obtained learning curves showed that the PACE model outperformed the LKB and predicted synthetic outcomes with an accuracy >0.8. On the real patients, PACE performance, evaluated by both discrimination and calibration, was significantly higher than LKB. This trend was confirmed by cross-validation. Furthermore, the capability to infer the spatial pattern of underlying RS map for the analyzed RIM was successfully demonstrated, thus paving the way to new perspectives of NTCP models as learning tools.
37 Records (31 escludendo Abstract e Conferenze). Impact factor totale: 88.341 (76.373 escludendo Abstract e Conferenze). Impact factor a 5 anni totale: 84.71 (72.141 escludendo Abstract e Conferenze).