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Computerized Segmentation And Measurement Of Malignant Pleural Mesothelioma.

W. Sensakovic, S. Armato, C. Straus, R. Roberts, P. Caligiuri, Adam Starkey, H. Kindler
Published 2011 · Medicine

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PURPOSE The current linear method to track tumor progression and evaluate treatment efficacy is insufficient for malignant pleural mesothelioma (MPM). A volumetric method for tumor measurement could improve the evaluation of novel treatments, but a fully manual implementation of volume measurement is too tedious and time-consuming. This manuscript presents a computerized method for the three-dimensional segmentation and volumetric analysis of MPM. METHODS The computerized MPM segmentation method segments the lung parenchyma and hemithoracic cavities to define the pleural space. Nonlinear diffusion and a k-means classifier are then implemented to identify MPM in the pleural space. A database of 31 computed tomography scans from 31 patients with pathologically confirmed MPM was retrospectively collected. Three observers independently outlined five randomly selected sections in each scan. The Jaccard similarity coefficient (J) between each of the observers and between the observer-defined and computer-defined segmentations was calculated. The computer-defined and the observer-defined segmentation areas (averaged over all observers) were both calculated for each axial section and compared using Bland-Altman plots. RESULTS The median J value among observers averaged over all sections was 0.517. The median J between the computer-defined and manual segmentations was 0.484. The difference between these values was not statistically significant. The area delineated by the computerized method demonstrated variability and bias comparable to the tumor area calculated from manual delineations. CONCLUSIONS A computerized method for segmentation and measurement of MPM was developed. This method requires minimal initialization by the user and demonstrated good agreement with manually drawn outlines and area measurements. This method will allow volumetric tracking of tumor progression and may improve the evaluation of novel MPM treatments.
This paper references
10.1109/38.135915
Shape-based interpolation
G. Herman (1992)
10.2486/INDHEALTH.45.379
Malignant mesothelioma: global incidence and relationship with asbestos.
C. Bianchi (2007)
10.1109/ICIP.1997.632133
Parallel implementations of AOS schemes: a fast way of nonlinear diffusion filtering
J. Weickert (1997)
10.1118/1.1688211
Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques.
S. Armato (2004)
10.1200/JCO.2003.11.136
Phase III study of pemetrexed in combination with cisplatin versus cisplatin alone in patients with malignant pleural mesothelioma.
N. Vogelzang (2003)
10.1016/J.LUNGCAN.2004.04.013
Staging and response to therapy of malignant pleural mesothelioma.
R. Heelan (2004)
Computerized segmentation and measurement of pleural disease
W. Sensakovic (2010)
10.1007/s10552-009-9357-4
Ionizing radiation: a risk factor for mesothelioma
J. Goodman (2009)
10.1137/0729052
Image selective smoothing and edge detection by nonlinear diffusion. II
L. Alvarez (1992)
10.1038/sj.bjc.6690105
The European mesothelioma epidemic
J. Peto (1999)
10.1118/1.1812611
Automated matching of temporally sequential CT sections.
W. Sensakovic (2004)
10.1109/42.52980
Shape-based interpolation of multidimensional objects.
S. P. Raya (1990)
10.2214/AJR.05.0076
Variability in mesothelioma tumor response classification.
S. Armato (2006)
10.1109/NSSMIC.2008.4774434
A general method for the identification and repair of concavities in segmented medical images
W.F. Sensakovic (2008)
10.1148/RADIOLOGY.211.1.R99AP15283
Abdominal fat: standardized technique for measurement at CT.
T. Yoshizumi (1999)
10.1007/s11864-008-0067-z
Mesothelioma Epidemiology, Carcinogenesis, and Pathogenesis
H. Yang (2008)
10.1093/ANNONC/MDH059
Modified RECIST criteria for assessment of response in malignant pleural mesothelioma.
M. Byrne (2004)
10.1118/1.2761369
TU‐D‐L100J‐05: Assessment of Mesothelioma Tumor Response: Correlation of Tumor Thickness and Tumor Area
S. Armato (2007)
10.1016/j.lungcan.2010.05.016
Imaging in pleural mesothelioma: a review of imaging research presented at the 9th International Meeting of the International Mesothelioma Interest Group.
A. Nowak (2010)
10.1136/oem.2003.010165
Changing trends in US mesothelioma incidence
H. Weill (2004)
10.1118/1.3056461
A modified gradient correlation filter for image segmentation: application to airway and bowel.
W. Sensakovic (2009)
10.1007/978-3-7091-6586-7_13
Theoretical Foundations of Anisotropic Diffusion in Image Processing
J. Weickert (1994)
10.1016/S0022-5223(98)70274-0
Preoperative tumor volume is associated with outcome in malignant pleural mesothelioma.
H. Pass (1998)



This paper is referenced by
10.1634/theoncologist.2019-0574
A Phase II Study of Pazopanib in Patients with Malignant Pleural Mesothelioma: NCCTG N0623 (Alliance).
K. Parikh (2019)
10.1118/1.4810940
Variability of tumor area measurements for response assessment in malignant pleural mesothelioma.
Z. Labby (2013)
10.1016/j.jtho.2018.02.021
Progress in the Management of Malignant Pleural Mesothelioma in 2017
Amanda J McCambridge (2018)
10.1093/annonc/mds537
Prognostic significance of metabolic response by positron emission tomography after neoadjuvant chemotherapy for resectable malignant pleural mesothelioma.
Y. Tsutani (2013)
10.9738/CC66.1
Three-dimensional stereoscopic volume rendering of malignant pleural mesothelioma.
N. Mollberg (2012)
10.1016/j.athoracsur.2016.06.069
A Multicenter Study of Volumetric Computed Tomography for Staging Malignant Pleural Mesothelioma.
V. Rusch (2016)
10.1016/j.lungcan.2015.07.011
Imaging in pleural mesothelioma: A review of the 12th International Conference of the International Mesothelioma Interest Group.
S. Armato (2015)
10.1093/annonc/mds535
Disease volumes as a marker for patient response in malignant pleural mesothelioma.
Z. Labby (2013)
10.1097/JTO.0b013e31826915f1
Initial Analysis of the International Association For the Study of Lung Cancer Mesothelioma Database
V. Rusch (2012)
10.1016/j.jtho.2018.02.021
State of the Art : Advances in Malignant Pleural Mesothelioma in 2017
Amanda J McCambridge (2018)
10.1117/1.JMI.7.1.012705
Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion
E. Gudmundsson (2020)
10.1200/JCO.2008.19.8523
Malignant pleural mesothelioma.
A. Tsao (2009)
10.21037/atm.2017.05.23
Volumetric assessment in malignant pleural mesothelioma.
D. Murphy (2017)
10.1016/j.compmedimag.2017.05.006
Malignant pleural mesothelioma segmentation for photodynamic therapy planning
W. Brahim (2018)
10.1016/j.jtho.2016.04.027
North American Multicenter Volumetric CT Study for Clinical Staging of Malignant Pleural Mesothelioma: Feasibility and Logistics of Setting Up a Quantitative Imaging Study
R. Gill (2016)
10.4018/978-1-4666-0059-1.CH007
Techniques for the Automated Segmentation of Lung in Thoracic Computed Tomography Scans
W. Sensakovic (2012)
10.1016/j.cmpb.2013.04.011
Validation study of a fast, accurate, and precise brain tumor volume measurement
M. Dang (2013)
10.1007/s11042-018-6400-z
The pleural thickening approximation from thoracic CT scans
W. Brahim (2018)
10.1016/j.ijrobp.2014.11.030
Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development.
A. Cunliffe (2015)
10.1016/j.lungcan.2016.09.003
Imaging in pleural mesothelioma: A review of the 13th International Conference of the International Mesothelioma Interest Group.
S. Armato (2016)
10.1097/JTO.0000000000000211
Observer Variability in Mesothelioma Tumor Thickness Measurements: Defining Minimally Measurable Lesions
S. Armato (2014)
10.1016/j.lungcan.2014.11.019
Radiologic-pathologic correlation of mesothelioma tumor volume.
S. Armato (2015)
10.1117/12.2043229
Detection, modeling and matching of pleural thickenings from CT data towards an early diagnosis of malignant pleural mesothelioma
K. Chaisaowong (2014)
10.1109/ATSIP.2017.8075605
Malignant pleural mesothelioma segmentation from thoracic CT scans
W. Brahim (2017)
10.1007/978-3-642-54111-7_7
Automated Assessment of Pleural Thickening
K. Chaisaowong (2014)
10.1117/1.JMI.5.3.034503
Deep convolutional neural networks for the automated segmentation of malignant pleural mesothelioma on computed tomography scans
E. Gudmundsson (2018)
10.1016/j.lungcan.2018.11.033
Imaging in pleural mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group.
S. Armato (2019)
10.1117/12.2512974
Pre-trained deep convolutional neural networks for the segmentation of malignant pleural mesothelioma tumor on CT scans
E. Gudmundsson (2019)
10.1093/ejcts/ezv422
Specimen weight and volume: important predictors of survival in malignant pleural mesothelioma.
Diana Y. Kircheva (2016)
Bland-Altman Plots for Evaluating Agreement Between Solid Tumor Measurements
Chaya S. Moskowitz (2011)
10.1118/1.4812679
Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography.
Yanhui Guo (2013)
Automated Assessment System for Pleural Thickenings Towards an Early Diagnosis of Pleuramesothelioma
K. Chaisaowong (2013)
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