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Ghandourh W, Dowling J, Chlap P, Oar A, Jacob S, Batumalai V, Holloway L. Assessing tumor centrality in lung stereotactic ablative body radiotherapy (SABR): the effects of variations in bronchial tree delineation and potential for automated methods. Med Dosim 2020; 46:94-101. [PMID: 33067108 DOI: 10.1016/j.meddos.2020.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/19/2020] [Accepted: 09/11/2020] [Indexed: 12/25/2022]
Abstract
Accurate delineation of the proximal bronchial tree (PBT) is crucial for appropriate assessment of lung tumor centrality and choice of Stereotactic Ablative Body Radiotherapy (SABR) dose prescription. Here, we investigate variabilities in manual PBT delineation and their potential to influence assessing lesion centrality. A fully automatic, intensity-based tool for PBT contouring and measuring distance to the target is also described. This retrospective analysis included a total of 61 patients treated with lung SABR. A subset of 41 patients was used as a training dataset, containing clinical PBT contour and additional subsequently generated manual contours. The tool was optimized and compared against manual contours in terms of volume, distance to the target and various overlap/similarity metrics. The remaining 20 patients were used as a validation dataset to investigate the dosimetric effects of variations between manual and automatic PBT contours. Considerable interobserver variability was observed, particularly in identifying the superior and inferior borders of the PBT. Automatic PBT contours were comparable to manual contours with average Dice of 0.63 to 0.79 and mean distance to agreement of 1.78 to 3.34 mm. No significant differences in dosimetric parameters were found between automatically and manually generated contours. A moderate negative correlation was found between PBT maximum dose and distance to the lesion (p < 0.05). Variability in manual PBT delineation may result in inconsistent assessment of tumor centrality. Automatic contouring can help standardize clinical practice, support investigations into the link between SABR outcomes and lesion proximity to central airways and the development of predictive toxicity models that incorporate precise measurements of tumor location in relation to high-risk organs.
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Affiliation(s)
- Wsam Ghandourh
- South Western Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia; Ingham Institute of Applied Medical Research, Sydney, New South Wales, Australia.
| | - Jason Dowling
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia; Australian E-Health Research Centre, Herston, QLD 4029, Australia
| | - Phillip Chlap
- South Western Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Ingham Institute of Applied Medical Research, Sydney, New South Wales, Australia
| | - Andrew Oar
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia; Collaboration for Cancer Outcomes Research and Evaluation (CCORE), Sydney, Australia; Western Sydney University, Campbelltown, New South Wales, Australia; Radiation Oncology Centres, Gold Coast University Hospital, Gold Coast, Australia
| | - Susannah Jacob
- South Western Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Ingham Institute of Applied Medical Research, Sydney, New South Wales, Australia; Collaboration for Cancer Outcomes Research and Evaluation (CCORE), Sydney, Australia
| | - Vikneswary Batumalai
- South Western Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia; Ingham Institute of Applied Medical Research, Sydney, New South Wales, Australia; Collaboration for Cancer Outcomes Research and Evaluation (CCORE), Sydney, Australia
| | - Lois Holloway
- South Western Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia; Ingham Institute of Applied Medical Research, Sydney, New South Wales, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia; Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia; Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, USA
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Doel T, Gavaghan DJ, Grau V. Review of automatic pulmonary lobe segmentation methods from CT. Comput Med Imaging Graph 2015; 40:13-29. [PMID: 25467805 DOI: 10.1016/j.compmedimag.2014.10.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Revised: 10/11/2014] [Accepted: 10/15/2014] [Indexed: 11/17/2022]
Abstract
The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms.
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Affiliation(s)
- Tom Doel
- Department of Computer Science, University of Oxford, Oxford, UK.
| | - David J Gavaghan
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science and Oxford e-Research Centre, University of Oxford, Oxford, UK
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Iwao Y, Wei Y, Kagei S, Gotoh T, Iwasawa T, Tsuzuki MDSG. Fusion of lung MR/CT images through lung vessel registration. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:3419-3422. [PMID: 24110463 DOI: 10.1109/embc.2013.6610276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Lung perfusion analysis with sequential contrasted MRI is an important clinical tool. This work is part of larger research in which the objective of fusing lung perfusion analysis and lung anatomical structures. In this work, it is proposed a multimodality MR/CT lung fusion algorithm based on lung vessel determination that analyzes lung perfusion. 3D contrasted MR and 3D CT images are first normalized (slice thickness, resolution and pixel intensity), then lung features are determined and elastically registered. The registration algorithm was checked by mapping in both directions, from MR to CT and vice-versa. A MR perfusion analysis result is fused with a 3D CT segmented lung vessel tree.
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