title: A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation.
abstract: OBJECTIVE
  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.
  
  
  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 3weeks, 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.
authors.affiliation: Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy; Physics Department, Alma Mater Studiorum University of Bologna, Italy. Electronic address: guidi.gabriele@policlinico.mo.it.
authors.forename: G
authors.initials: G
authors.lastname: Guidi
authors.affiliation: Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: N
authors.initials: N
authors.lastname: Maffei
authors.affiliation: Radiation Oncology Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: B
authors.initials: B
authors.lastname: Meduri
authors.affiliation: Radiation Oncology Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: E
authors.initials: E
authors.lastname: D'Angelo
authors.affiliation: Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: G M
authors.initials: GM
authors.lastname: Mistretta
authors.affiliation: Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: P
authors.initials: P
authors.lastname: Ceroni
authors.affiliation: Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy; Radiotherapy Unit, Altnagelvin Hospital, Londonderry, United Kingdom.
authors.forename: A
authors.initials: A
authors.lastname: Ciarmatori
authors.affiliation: Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: A
authors.initials: A
authors.lastname: Bernabei
authors.affiliation: Medical Physics Department, Az.Ospedaliero-Universitaria Ospedale Riuniti di Ancona, Italy.
authors.forename: S
authors.initials: S
authors.lastname: Maggi
authors.affiliation: Radiation Oncology Department, Az.Ospedaliero-Universitaria Ospedale Riuniti di Ancona, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Cardinali
authors.affiliation: Medical Physics Department, Az.Ospedaliero-Universitaria Ospedale Riuniti di Ancona, Italy.
authors.forename: V E
authors.initials: VE
authors.lastname: Morabito
authors.affiliation: Medical Physics Department, AUSL4 Teramo, Italy.
authors.forename: F
authors.initials: F
authors.lastname: Rosica
authors.affiliation: Radiation Oncology Department, AUSL4 Teramo, Italy.
authors.forename: S
authors.initials: S
authors.lastname: Malara
authors.affiliation: Medical Physics Department, AUSL4 Teramo, Italy.
authors.forename: A
authors.initials: A
authors.lastname: Savini
authors.affiliation: Medical Physics Department, AUSL4 Teramo, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Orlandi
authors.affiliation: Radiation Oncology Department, AUSL4 Teramo, Italy.
authors.forename: C
authors.initials: C
authors.lastname: D'Ugo
authors.affiliation: Radiation Oncology Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy.
authors.forename: F
authors.initials: F
authors.lastname: Bunkheila
authors.affiliation: Medical Physics Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Bono
authors.affiliation: Medical Physics Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy.
authors.forename: S
authors.initials: S
authors.lastname: Lappi
authors.affiliation: Radiation Oncology Department, Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Italy.
authors.forename: C
authors.initials: C
authors.lastname: Blasi
authors.affiliation: Radiation Oncology Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: F
authors.initials: F
authors.lastname: Lohr
authors.affiliation: Medical Physics Department, Az. Ospedaliero Universitaria di Modena, Italy.
authors.forename: T
authors.initials: T
authors.lastname: Costi
mesh_terms: D015331:Cohort Studies; D006801:Humans; D000069550:Machine Learning; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies
keywords: Adaptive RT; Deformable registration; Machine learning; Re-planning