Jiang Hu1, Boji Liu1, Weihao Xie1, Jinhan Zhu1, Xiaoli Yu2, Huikuan Gu1, Mingli Wang1, Yixuan Wang1, ZhenYu Qi1. 1. Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China. 2. Sun Yat-sen Memory Hospital, Guangzhou, China.
Abstract
BACKGROUND AND PURPOSE: To validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved. METHODS AND MATERIALS: A knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated. RESULTS: With comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable. CONCLUSION: The automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.
BACKGROUND AND PURPOSE: To validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved. METHODS AND MATERIALS: A knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated. RESULTS: With comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable. CONCLUSION: The automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.
Authors: Allen M Chen; Nancy Y Lee; Claus C Yang; Tianxiao Liu; Samir Narayan; Srinivasan Vijayakumar; James A Purdy Journal: Technol Cancer Res Treat Date: 2010-06
Authors: Jim P Tol; Alexander R Delaney; Max Dahele; Ben J Slotman; Wilko F A R Verbakel Journal: Int J Radiat Oncol Biol Phys Date: 2015-01-30 Impact factor: 7.038
Authors: Binbin Wu; Francesco Ricchetti; Giuseppe Sanguineti; Michael Kazhdan; Patricio Simari; Robert Jacques; Russell Taylor; Todd McNutt Journal: Int J Radiat Oncol Biol Phys Date: 2010-08-26 Impact factor: 7.038
Authors: Nancy Y Lee; Qiang Zhang; David G Pfister; John Kim; Adam S Garden; James Mechalakos; Kenneth Hu; Quynh T Le; A Dimitrios Colevas; Bonnie S Glisson; Anthony Tc Chan; K Kian Ang Journal: Lancet Oncol Date: 2011-12-15 Impact factor: 41.316
Authors: Marco Fusella; Alessandro Scaggion; Nicola Pivato; Marco Andrea Rossato; Alessandra Zorz; Marta Paiusco Journal: Med Phys Date: 2018-04-19 Impact factor: 4.071
Authors: Lan Lu; Yang Sheng; Jeremy Donaghue; Zhilei Liu Shen; Matt Kolar; Q Jackie Wu; Ping Xia Journal: J Appl Clin Med Phys Date: 2019-07-31 Impact factor: 2.102