Soleil Hernandez1, Carlos Sjogreen2, Skylar S Gay3, Callistus Nguyen3, Tucker Netherton2, Adenike Olanrewaju3, Lifei Joy Zhang3, Dong Joo Rhee2, José David Méndez3, Laurence E Court2, Carlos E Cardenas2. 1. The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA. Electronic address: shernandez6@mdanderson.org. 2. The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA. 3. The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA.
Abstract
PURPOSE: We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process. METHODS: We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified. RESULTS: Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans. CONCLUSION: The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.
PURPOSE: We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process. METHODS: We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified. RESULTS: Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans. CONCLUSION: The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.
Authors: Mischa de Ridder; Cornelis P J Raaijmakers; Frank A Pameijer; Remco de Bree; Floris C J Reinders; Patricia A H Doornaert; Chris H J Terhaard; Marielle E P Philippens Journal: Cancers (Basel) Date: 2022-06-20 Impact factor: 6.575
Authors: Carlos E Cardenas; Sanne E Blinde; Abdallah S R Mohamed; Sweet Ping Ng; Cornelis Raaijmakers; Marielle Philippens; Alexis Kotte; Abrahim A Al-Mamgani; Irene Karam; David J Thomson; Jared Robbins; Kate Newbold; Clifton D Fuller; Chris Terhaard Journal: Int J Radiat Oncol Biol Phys Date: 2022-02-04 Impact factor: 8.013