TY - JOUR
T1 - PACE
T2 - A probabilistic atlas for normal tissue complication estimation in radiation oncology
AU - Palma, Giuseppe
AU - Monti, Serena
AU - Buonanno, Amedeo
AU - Pacelli, Roberto
AU - Cella, Laura
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Normal tissue complication probability
KW - Radiation therapy
KW - Radiation-induced morbidity
KW - Radio-sensitivity
KW - Voxel-based analysis
UR - http://www.scopus.com/inward/record.url?scp=85063257193&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063257193&partnerID=8YFLogxK
U2 - 10.3389/fonc.2019.00130
DO - 10.3389/fonc.2019.00130
M3 - Article
AN - SCOPUS:85063257193
VL - 9
JO - Frontiers in Oncology
JF - Frontiers in Oncology
SN - 2234-943X
IS - MAR
M1 - 130
ER -