title: A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities.
abstract: OBJECTIVE
  Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning.
  
  
  METHODS
  1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB(®) homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments.
  
  
  RESULTS
  Using retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area.
  
  
  CONCLUSIONS
  Warping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints.
authors.affiliation: Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Physics Department, University of Bologna, Italy. Electronic address: guidi.gabriele@policlinico.mo.it.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Guidi
authors.affiliation: Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Physics Department, University of Bologna, Italy.
authors.forename: Nicola
authors.initials: N
authors.lastname: Maffei
authors.affiliation: Physics Department, University of Bologna, Italy.
authors.forename: Claudio
authors.initials: C
authors.lastname: Vecchi
authors.affiliation: Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Post-graduate School in Medical Physics, University of Bologna, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Ciarmatori
authors.affiliation: Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy.
authors.forename: Grazia Maria
authors.initials: GM
authors.lastname: Mistretta
authors.affiliation: Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Gottardi
authors.affiliation: Radiation Oncology Department, Az. Ospedaliero-Universitaria di Modena, Italy.
authors.forename: Bruno
authors.initials: B
authors.lastname: Meduri
authors.affiliation: Physics Department, University of Bologna, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Baldazzi
authors.affiliation: Radiation Oncology Department, Az. Ospedaliero-Universitaria di Modena, Italy.
authors.forename: Filippo
authors.initials: F
authors.lastname: Bertoni
authors.affiliation: Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy.
authors.forename: Tiziana
authors.initials: T
authors.lastname: Costi
mesh_terms: D000465:Algorithms; D005260:Female; D006258:Head and Neck Neoplasms / Q000532:radiotherapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011880:Radiotherapy Planning, Computer-Assisted; D061089:Radiotherapy, Image-Guided / Q000379:methods*; D012189:Retrospective Studies; D060388:Support Vector Machine; D000069558:Unsupervised Machine Learning
keywords: Adaptive radiation therapy; Cluster analysis; ROC curves; Support vector machines