Tim Lustberg1, Johan van Soest2, Mark Gooding3, Devis Peressutti3, Paul Aljabar3, Judith van der Stoep2, Wouter van Elmpt2, Andre Dekker2. 1. Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands. Electronic address: tim.lustberg@maastro.nl. 2. Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands. 3. Mirada Medical Ltd., Oxford, United Kingdom.
Abstract
BACKGROUND AND PURPOSE: Contouring of organs at risk (OARs) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancer patients. MATERIAL AND METHODS: Twenty CT scans of stage I-III NSCLC patients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation. The lungs, esophagus, spinal cord, heart and mediastinum were contoured for this study. The time to perform the manual tasks was recorded. RESULTS: With a median time of 20 min for manual contouring, the total median time saved was 7.8 min when using atlas-based contouring and 10 min for deep learning contouring. Both atlas based and deep learning adjustment times were significantly lower than manual contouring time for all OARs except for the left lung and esophagus of the atlas based contouring. CONCLUSIONS: User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards. In addition, deep learning contouring shows promising results compared to existing solutions.
BACKGROUND AND PURPOSE: Contouring of organs at risk (OARs) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancerpatients. MATERIAL AND METHODS: Twenty CT scans of stage I-III NSCLCpatients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation. The lungs, esophagus, spinal cord, heart and mediastinum were contoured for this study. The time to perform the manual tasks was recorded. RESULTS: With a median time of 20 min for manual contouring, the total median time saved was 7.8 min when using atlas-based contouring and 10 min for deep learning contouring. Both atlas based and deep learning adjustment times were significantly lower than manual contouring time for all OARs except for the left lung and esophagus of the atlas based contouring. CONCLUSIONS: User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards. In addition, deep learning contouring shows promising results compared to existing solutions.
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: Kuo Men; Huaizhi Geng; Chingyun Cheng; Haoyu Zhong; Mi Huang; Yong Fan; John P Plastaras; Alexander Lin; Ying Xiao Journal: Med Phys Date: 2018-12-07 Impact factor: 4.071
Authors: Xingyu Wu; Jayaram K Udupa; Yubing Tong; Dewey Odhner; Gargi V Pednekar; Charles B Simone; David McLaughlin; Chavanon Apinorasethkul; Ontida Apinorasethkul; John Lukens; Dimitris Mihailidis; Geraldine Shammo; Paul James; Akhil Tiwari; Lisa Wojtowicz; Joseph Camaratta; Drew A Torigian Journal: Med Image Anal Date: 2019-01-29 Impact factor: 8.545
Authors: Eric D Morris; Ahmed I Ghanem; Ming Dong; Milan V Pantelic; Eleanor M Walker; Carri K Glide-Hurst Journal: Med Phys Date: 2019-12-29 Impact factor: 4.071
Authors: Kuo Men; Pamela Boimel; James Janopaul-Naylor; Haoyu Zhong; Mi Huang; Huaizhi Geng; Chingyun Cheng; Yong Fan; John P Plastaras; Edgar Ben-Josef; Ying Xiao Journal: Phys Med Biol Date: 2018-09-17 Impact factor: 3.609