Purpose To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset. Materials and methods 90 patients (pts) treated for head-neck cancer (H&N) formed a multicenter data-set. 41 H&N pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG). Using deformable image registration (DIR) and GPU, patients’ conditions were analyzed automatically. Support Vector Machines (SVM) was used for time-series evaluation. “Inadequate” class identified patients that might benefit from replanning. Double-blind evaluation by two radiation oncologists (ROs) was carried out to validate day/week selected for re-planning by the classifier. Results The cohort was affected by PG mean reduction of 23.7 ± 8.8%. During the first 3 weeks, 86.7% cases show PG deformation aligned with predefined tolerance, thus not requiring re-planning. From 4th week, an increased number of pts would potentially benefit from re-planning: a mean of 58% of cases, with an inter-center variability of 8.3%, showed “inadequate” conditions. 11% of cases showed “bias” due to DIR and script failure; 6% showed “warning” output due to potential positioning issues. Comparing re-planning suggested by tool with recommended by ROs, the 4th week seems the most favorable time in 70% cases. Conclusions SVM and decision-making tool was applied to overcome ART challenges. Pts would benefit from ART and ideal time for re-planning intervention was identified in this retrospective analysis.

A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation / Guidi, G; Maffei, N; Meduri, B; D'Angelo, E; Mistretta, Gm; Ceroni, P; Ciarmatori, A; Bernabei, A; Maggi, S; Cardinali, M; Morabito, Ve; Rosica, F; Malara, S; Savini, A; Orlandi, G; D'Ugo, C; Bunkheila, F; Bono, M; Lappi, S; Blasi, C; Lohr, F; Costi, T. - In: PHYSICA MEDICA. - ISSN 1120-1797. - 32:12(2016), pp. 1659-1666. [10.1016/j.ejmp.2016.10.005]

A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation

Lohr F;
2016

Abstract

Purpose To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset. Materials and methods 90 patients (pts) treated for head-neck cancer (H&N) formed a multicenter data-set. 41 H&N pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG). Using deformable image registration (DIR) and GPU, patients’ conditions were analyzed automatically. Support Vector Machines (SVM) was used for time-series evaluation. “Inadequate” class identified patients that might benefit from replanning. Double-blind evaluation by two radiation oncologists (ROs) was carried out to validate day/week selected for re-planning by the classifier. Results The cohort was affected by PG mean reduction of 23.7 ± 8.8%. During the first 3 weeks, 86.7% cases show PG deformation aligned with predefined tolerance, thus not requiring re-planning. From 4th week, an increased number of pts would potentially benefit from re-planning: a mean of 58% of cases, with an inter-center variability of 8.3%, showed “inadequate” conditions. 11% of cases showed “bias” due to DIR and script failure; 6% showed “warning” output due to potential positioning issues. Comparing re-planning suggested by tool with recommended by ROs, the 4th week seems the most favorable time in 70% cases. Conclusions SVM and decision-making tool was applied to overcome ART challenges. Pts would benefit from ART and ideal time for re-planning intervention was identified in this retrospective analysis.
17-ott-2016
32
12
1659
1666
A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation / Guidi, G; Maffei, N; Meduri, B; D'Angelo, E; Mistretta, Gm; Ceroni, P; Ciarmatori, A; Bernabei, A; Maggi, S; Cardinali, M; Morabito, Ve; Rosica, F; Malara, S; Savini, A; Orlandi, G; D'Ugo, C; Bunkheila, F; Bono, M; Lappi, S; Blasi, C; Lohr, F; Costi, T. - In: PHYSICA MEDICA. - ISSN 1120-1797. - 32:12(2016), pp. 1659-1666. [10.1016/j.ejmp.2016.10.005]
Guidi, G; Maffei, N; Meduri, B; D'Angelo, E; Mistretta, Gm; Ceroni, P; Ciarmatori, A; Bernabei, A; Maggi, S; Cardinali, M; Morabito, Ve; Rosica, F; Malara, S; Savini, A; Orlandi, G; D'Ugo, C; Bunkheila, F; Bono, M; Lappi, S; Blasi, C; Lohr, F; Costi, T
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1172382
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