Gabriele Guidi1, Nicola Maffei2, Claudio Vecchi3, Alberto Ciarmatori4, Grazia Maria Mistretta5, Giovanni Gottardi5, Bruno Meduri6, Giuseppe Baldazzi3, Filippo Bertoni6, Tiziana Costi5. 1. Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Physics Department, University of Bologna, Italy. Electronic address: guidi.gabriele@policlinico.mo.it. 2. Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Physics Department, University of Bologna, Italy. 3. Physics Department, University of Bologna, Italy. 4. Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy; Post-graduate School in Medical Physics, University of Bologna, Italy. 5. Medical Physics Department, Az. Ospedaliero-Universitaria di Modena, Italy. 6. Radiation Oncology Department, Az. Ospedaliero-Universitaria di Modena, Italy.
Abstract
PURPOSE: 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.
PURPOSE: 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: Elizabeth Huynh; Ahmed Hosny; Christian Guthier; Danielle S Bitterman; Steven F Petit; Daphne A Haas-Kogan; Benjamin Kann; Hugo J W L Aerts; Raymond H Mak Journal: Nat Rev Clin Oncol Date: 2020-08-25 Impact factor: 66.675
Authors: Issam El Naqa; Dan Ruan; Gilmer Valdes; Andre Dekker; Todd McNutt; Yaorong Ge; Q Jackie Wu; Jung Hun Oh; Maria Thor; Wade Smith; Arvind Rao; Clifton Fuller; Ying Xiao; Frank Manion; Matthew Schipper; Charles Mayo; Jean M Moran; Randall Ten Haken Journal: Med Phys Date: 2018-08-24 Impact factor: 4.071
Authors: Sarah Weppler; Harvey Quon; Colleen Schinkel; James Ddamba; Nabhya Harjai; Clarisse Vigal; Craig A Beers; Lukas Van Dyke; Wendy Smith Journal: Front Oncol Date: 2021-06-07 Impact factor: 6.244