Introduction: A multicenter research was carried out to validate predictive strategies: to determinate patients eligible for re-planning due to predictable anatomical variations. Advantage and challenges of IGRT, deformable image registration and different set-up protocols were evaluated in a multicenter retrospective study using planning data. Materials and Methods: 76 head and neck (H&N) patients were considered with more than 2200 daily studies analyzed: 1200 MVCT from Center-A (A), 600 CBCT from Center-B (B), 240 CBCT from Center-C (C) and 240 CBCT from Center-D (D). To obtain a predictive time of warping during the 6 weeks of therapy, the study focused on volume and dose variations of parotid glands (PG). RayStation hybrid algorithm, supported by IronPython scripts and GPU computing, was used to perform a daily deformable image registration, structures automatic re-contouring and dose accumulation. A home-made machine-learning classifier tool was developed in MATLAB. Support Vector Machines (SVM) and cluster analysis were used for a weekly time-series evaluation and patients' selection. Results: At the end of the treatment (~30 fractions), the whole cohort is affected by a PG mean reduction of 23.7 ± 8.8%: 25.1 ± 9.2% (A), 23.8 ± 6.6% (B), 21.2 ± 10.3% (C) and 24.4 ± 9.8% (D). Using machine learning approach, the shrinkage of 86.3% of cases can be predicted during the first 3 weeks of therapy: 89.6% (A), 92.7% (B), 76.0% (C) and 87.0% (D). The number of patients that would benefit from a review of the initial plan reached 53.5% from the 4th week, with an inter-centers variability of 19.7%. Conclusions: A SVM and cluster decision making tool was developed and trained in order to overcome Adaptive RT logistic challenges in a busy clinical routine. The time for re-planning and the specific patients that would benefit from a new plan were quantified in this multicenter study to increase the personalization of the patients' treatment during all sessions.

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

GUIDI, GABRIELE
Investigation
;
CIARMATORI, ALBERTO
Membro del Collaboration Group
;
BUNKHEILA, FEISAL;BLASI, CLAUDIO;BALDAZZI, GIUSEPPE
Supervision
;
COSTI, TIZIANA
Membro del Collaboration Group
2016

Abstract

Introduction: A multicenter research was carried out to validate predictive strategies: to determinate patients eligible for re-planning due to predictable anatomical variations. Advantage and challenges of IGRT, deformable image registration and different set-up protocols were evaluated in a multicenter retrospective study using planning data. Materials and Methods: 76 head and neck (H&N) patients were considered with more than 2200 daily studies analyzed: 1200 MVCT from Center-A (A), 600 CBCT from Center-B (B), 240 CBCT from Center-C (C) and 240 CBCT from Center-D (D). To obtain a predictive time of warping during the 6 weeks of therapy, the study focused on volume and dose variations of parotid glands (PG). RayStation hybrid algorithm, supported by IronPython scripts and GPU computing, was used to perform a daily deformable image registration, structures automatic re-contouring and dose accumulation. A home-made machine-learning classifier tool was developed in MATLAB. Support Vector Machines (SVM) and cluster analysis were used for a weekly time-series evaluation and patients' selection. Results: At the end of the treatment (~30 fractions), the whole cohort is affected by a PG mean reduction of 23.7 ± 8.8%: 25.1 ± 9.2% (A), 23.8 ± 6.6% (B), 21.2 ± 10.3% (C) and 24.4 ± 9.8% (D). Using machine learning approach, the shrinkage of 86.3% of cases can be predicted during the first 3 weeks of therapy: 89.6% (A), 92.7% (B), 76.0% (C) and 87.0% (D). The number of patients that would benefit from a review of the initial plan reached 53.5% from the 4th week, with an inter-centers variability of 19.7%. Conclusions: A SVM and cluster decision making tool was developed and trained in order to overcome Adaptive RT logistic challenges in a busy clinical routine. The time for re-planning and the specific patients that would benefit from a new plan were quantified in this multicenter study to increase the personalization of the patients' treatment during all sessions.
Abstracts of the 9th National Congress of the Associazione Italiana di Fisica Medica
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Guidi, G.; Maffei, N.; Meduri, B.; Ciarmatori, A.; Mistretta, G.M.; Maggi, S.; Cardinali, M.; Morabito, V.E.; Rosica, F.; Malara, S.; Savini, A.; Orlandi, G.; D'Ugo, C.; Bunkheila, F.; Bono, M.; Lappi, S.; Blasi, C.; Giacobazzi, P.; Baldazzi, G.; Costi, T.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/671707
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