CENTRAL3D: A CLINICAL TOOL FOR IMPROVED CHARACTERIZATION OF CENTRALLY LOCATED NON-SMALL CELL LUNG CANCER
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1 CENTRAL3D: A CLINICAL TOOL FOR IMPROVED CHARACTERIZATION OF CENTRALLY LOCATED NON-SMALL CELL LUNG CANCER Dominique Mathieu MD MSc, Vincent Cousineau Daoust MSc, Alexis Lenglet MD MSc, Édith Filion MD, Stéphane Bedwani PhD, Houda Bahig MD, Laurent Bilodeau MD, Toni Vu MD, David Roberge MD, Marie-Pierre Campeau MD Centre hospitalier de l Université de Montréal September 15, 2017
2 None Conflicts of interest
3 Introduction The irradiation of central lung tumors by SBRT is associated with an increased risk of high-grade toxicity. The classification of central vs peripheral tumors is essential to adapting the fractionation.
4 Purpose To measure inter-observer variability in the classification of central and ultra-central pulmonary tumors. To evaluate clinical applications of Central3D, an anatomical visualization tool.
5 Software Central3D House tool developed in collaboration with our Department of Physics. Uses planning contours to visualize volumes in an interactive 3D environment. Displays the location and geometry of the macroscopic tumor volume (GTV) in relation to the organs at risk (OARs).
6 Software Central3D Multiples functions: Minimum distance between two structures. Overlap in planning target volume (PTV). Display of isodoses of interests. Surface projection maps. S
7 Method 4 radiation oncologists blindly classified 20 lung tumors treated by CyberKnife as: Peripheral vs central. Central vs ultra-central. D min from the GTV to the proximal bronchial tree (PBT).
8 Method - Definitions Central tumor : GTV inside or touching the 2 cm area from the PBT. Ultra-central tumor : PTV overlaping the heart, PBT, esophagus or major vessels.
9 Method - Definitions Peripheral, central, ultra-central Radiation Therapy Oncology Group. RTOG 0813: Seamless phase I/II study of SBRT for early stage, centrally located, NSCLC in medically inoperable patients. Philadelphia (PA): 2009.
10 Method - Measures of D min Evaluated in axial / sagittal / coronal. Borders of GTV to PBT. D min = 2,1 cm
11 Method - Statistics Agreement between clinicians reported with a Fleiss Kappa Interprétation < 0 Poor agreement Slight agreement Fair agreement Moderate agreement Substantial agreement Almost perfect agreement Concordance actually reached above chance Concordance achievable beyond chance
12 Method - Delineation Reference OARs drawn by a radiologist according to the RTOG Thoracic Atlas Kong FM, Machatay M, Bradley J et al. RTOG 1106: Randomized phase II trial of individualized adaptive radiotherapy using during treatment FDG-PET. CT and modern technology in locally advanced NSCLC
13 Results - Classification Central/peripheral classification 3/20 discordant cases = 0,81 Central/ultra-central classification 5/20 discordant cases = 0,58
14 Results D min measures
15 Discussion Evaluation bias induced by The representation of planning scans in axial, sagittal and coronal sections. The discordant identification of complex anatomical structures. Cases of disagreement were revised with Central3D to obtain consensus among clinicians.
16 Case 1: T2a (3,1 cm) RUL Peripheral (2 MDs) vs central (2 MDs). Central (2 MDs) vs ultra-central (0 MD). D min of GTV to the PBT MD 1 = 1,4 cm MD 2 = 1,8 cm MD 3 = 2,4 cm MD 4 = 2,5 cm Central3D = 1,7 cm
17 Case 1: T2a (3,1 cm) RUL GTV, trachea, esophagus, D axial = 2,2 cm
18 Case 1: T2a (3,1 cm) RUL GTV, PBT, heart, D sagittal = 1,7 cm
19 Case 1: T2a (3,1 cm) RUL Visualization in Central3D
20 Strengths / Limitations Discordant classifications of tumors not described in the literature. Central3D is a tool that can be easily implemented into treatment planning. Several modules under development to meet clinical needs. X Requires precise OAR contours. X Internal validation with a small sample.
21 Conclusion The classification of pulmonary tumors is subject to variability between observers. Central3D is useful software for classifying central lesions.
22 Acknowledgment Department of Physics - Vincent Cousineau-Daoust - Stéphane Bedwani Department of Radiology - Laurent Bilodeau Department of Radiation Oncology - Marie-Pierre Campeau - Édith Filion - Alexis Langlet - Houda Bahig - Toni Vu - David Roberge
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