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Variability In Mesothelioma Tumor Response Classification.

S. Armato, J. L. Ogarek, Adam Starkey, N. Vogelzang, H. Kindler, M. Kocherginsky, H. MacMahon
Published 2006 · Medicine

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OBJECTIVE The objective of our study was to evaluate observer variability in the measurement of temporal change in mesothelioma tumor thickness and in the resulting tumor response classification from CT scans. In addition, the performance of a semiautomated measurement method was evaluated. MATERIALS AND METHODS Four observers individually used an interface that displayed two serial CT scans from the same patient to measure mesothelioma tumor thickness on the follow-up CT scans of 22 patients based on baseline scan measurements. During one session, observers acquired measurements on the follow-up scans based on written reports of baseline scan measurements; in another session, baseline scan measurements were superimposed on the baseline scan for direct visual comparison. Follow-up scan measurements also were obtained from a semiautomated method. Measurement variability and tumor response classification concordance were evaluated for manual measurements acquired in both modes and for semiautomated measurements. RESULTS Although only a small increase in tumor response classification concordance rate was obtained with the visual approach (84.8%) relative to the more standard written-report approach (82.6%), the actual measurements acquired by observers were statistically significantly different between the two approaches (p = 0.03). Both the semiautomated measurements and the resulting tumor response classifications were consistent with manual measurements. CONCLUSION The presentation of baseline scan tumor measurements affects measurements acquired on follow-up scans and could influence tumor response classification. The potential utility of semiautomated tumor thickness measurements was shown in the context of measuring tumor response.
This paper references
10.2214/AJR.167.4.8819370
Analysis of interobserver and intraobserver variability in CT tumor measurements.
K. Hopper (1996)
10.1200/JCO.1999.17.1.25
Cisplatin and gemcitabine treatment for malignant mesothelioma: a phase II study.
M. Byrne (1999)
10.1093/JNCI/91.6.523
Measuring response in solid tumors: unidimensional versus bidimensional measurement.
K. James (1999)
10.2214/AJR.155.5.2120965
Malignant pleural mesothelioma: CT manifestations in 50 cases.
A. Kawashima (1990)
10.1002/1097-0142(19810101)47:1<207::AID-CNCR2820470134>3.0.CO;2-6
Reporting results of cancer treatment
A. Miller (1981)
10.2214/AJR.172.4.10587144
Staging of malignant pleural mesothelioma: comparison of CT and MR imaging.
R. Heelan (1999)
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)
10.1046/j.1440-1843.1998.00069.x
Computed tomographic findings of environmental asbestos‐related malignant pleural mesothelioma
U. Yilmaz (1998)
10.2214/AJR.176.2.1760333
Radiologic measurement of tumor size in clinical trials: past, present, and future.
S. Saini (2001)
10.1200/JCO.1997.15.12.3507
Response rate accuracy in oncology trials: reasons for interobserver variability. Groupe Français d'Immunothérapie of the Fédération Nationale des Centres de Lutte Contre le Cancer.
P. Thiesse (1997)
10.2307/2529310
The measurement of observer agreement for categorical data.
J. Landis (1977)
10.1093/ANNONC/MDH059
Modified RECIST criteria for assessment of response in malignant pleural mesothelioma.
M. Byrne (2004)
10.1016/J.CTRV.2007.07.012
Assessment of tumor response in malignant pleural mesothelioma.
G. Ceresoli (2007)
10.1200/JCO.2000.18.23.3912
Phase II study of vinorelbine in patients with malignant pleural mesothelioma.
J. Steele (2000)
10.1016/J.LUNGCAN.2004.04.022
Moving beyond chemotherapy: novel cytostatic agents for malignant mesothelioma.
H. Kindler (2004)
10.1118/1.1812611
Automated matching of temporally sequential CT sections.
W. Sensakovic (2004)
10.1016/S0009-9260(99)90824-3
Malignant pleural mesothelioma: the spectrum of manifestations on CT in 70 cases.
C. Ng (1999)
10.1148/RADIOGRAPHICS.16.3.8897631
Integrated multimedia timeline of medical images and data for thoracic oncology patients.
D. Aberle (1996)
10.1118/1.1688211
Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques.
S. Armato (2004)
10.1016/J.LUNGCAN.2004.02.001
Is "no treatment" better than radiotherapy and chemotherapy?
L. Debevec (2004)
10.1053/SONC.2002.30228
The role of imaging in malignant pleural mesothelioma.
E. Marom (2002)
10.1016/J.ACRA.2005.05.021
Evaluation of semiautomated measurements of mesothelioma tumor thickness on CT scans.
S. Armato (2005)
10.1200/JCO.2003.01.144
Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response.
J. Erasmus (2003)
10.1093/jnci/92.3.205
New Guidelines to Evaluate the Response to Treatment in Solid Tumors.
P. Therasse (2000)



This paper is referenced by
10.1186/s12885-018-4113-3
A prospective study to investigate the role of serial serum mesothelin in monitoring mesothelioma
D. de Fonseka (2018)
10.1007/978-3-642-10862-4_3
Imaging of mesothelioma.
R. Gill (2011)
10.1200/JCO.2009.27.3649
Phase II study of asparagine-glycine-arginine-human tumor necrosis factor alpha, a selective vascular targeting agent, in previously treated patients with malignant pleural mesothelioma.
V. Gregorc (2010)
10.1093/ejcts/ezu393
Evaluation of imaging techniques for the assessment of tumour progression in an orthotopic rat model of malignant pleural mesothelioma†.
Mayura Meerang (2015)
10.1245/s10434-010-1107-z
Volume-Based Parameter of 18F-FDG PET/CT in Malignant Pleural Mesothelioma: Prediction of Therapeutic Response and Prognostic Implications
H. Lee (2010)
10.1158/1078-0432.CCR-10-1929
Serum Soluble Mesothelin Concentrations in Malignant Pleural Mesothelioma: Relationship to Tumor Volume, Clinical Stage and Changes in Tumor Burden
J. Creaney (2010)
10.1016/B978-0-323-05375-4.50028-8
Pleura and Chest Wall
N. Rahman (2009)
10.1093/jnci/dju331
Use and misuse of waterfall plots.
Tiffany Shao (2014)
10.3348/kjr.2012.13.4.371
Imaging-Based Tumor Treatment Response Evaluation: Review of Conventional, New, and Emerging Concepts
Hee Kang (2012)
10.1371/journal.pone.0026722
An In Vivo Platform for Tumor Biomarker Assessment
E. Servais (2011)
Automated Assessment System for Pleural Thickenings Towards an Early Diagnosis of Pleuramesothelioma
K. Chaisaowong (2013)
10.1109/ISBI.2011.5872798
Random walk-based automated segmentation for the prognosis of malignant pleural mesothelioma
Mitchell Chen (2011)
10.21037/atm.2017.05.23
Volumetric assessment in malignant pleural mesothelioma.
D. Murphy (2017)
10.1016/j.lungcan.2013.08.005
Imaging in pleural mesothelioma: a review of the 11th International Conference of the International Mesothelioma Interest Group.
S. Armato (2013)
Role of PET / CT in Diagnosis and Staging of Malignant Mesothelioma
D. I. Mohamed (2017)
10.1097/JTO.0b013e31828354c8
Lung Volume Measurements as a Surrogate Marker for Patient Response in Malignant Pleural Mesothelioma
Z. Labby (2013)
10.1053/j.semtcvs.2009.06.011
Current trends in radiologic management of malignant pleural mesothelioma.
R. Gill (2009)
10.1118/1.4810940
Variability of tumor area measurements for response assessment in malignant pleural mesothelioma.
Z. Labby (2013)
10.1109/CIMI.2013.6583852
A fully automatic probabilistic 3D approach for the detection and assessment of pleural thickenings from CT data
K. Chaisaowong (2013)
10.1002/cam4.1182
Prognostic and predictive role of [18F]fluorodeoxyglucose positron emission tomography (FDG‐PET) in patients with unresectable malignant pleural mesothelioma (MPM) treated with up‐front pemetrexed‐based chemotherapy
P. Zucali (2017)
10.1007/s00259-014-2960-y
Quantitative analyses at baseline and interim PET evaluation for response assessment and outcome definition in patients with malignant pleural mesothelioma
E. Lopci (2014)
10.1136/bmjopen-2012-001620
Utilisation of a thoracic oncology database to capture radiological and pathological images for evaluation of response to chemotherapy in patients with malignant pleural mesothelioma
George B. Carey (2012)
10.1016/j.jtho.2018.04.034
Revised Modified Response Evaluation Criteria in Solid Tumors for Assessment of Response in Malignant Pleural Mesothelioma (Version 1.1)
S. Armato (2018)
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)
10.1967/s002449910904
18F-FDG PET/CT in therapy response and in predicting responders or non-responders in malignant pleural mesothelioma patients, by using semi-quantitative mRECIST and EORTC criteria.
A. Niccoli Asabella (2018)
A Multimodality Imaging Review of Malignant Pleural Mesothelioma Assessment
Anna K. Nowak (2011)
10.4155/CLI.11.72
Treatment of malignant pleural mesothelioma: current status and future directions
P. Zucali (2011)
10.1111/j.1743-7563.2010.01316.x
Audit of patients with mesothelioma treated with pemetrexed in a single institution in Western Australia
A. Hasani (2010)
10.1038/bjc.2017.22
Prognostication and monitoring of mesothelioma using biomarkers: a systematic review
D. Arnold (2017)
10.1016/J.LUNGCAN.2005.03.011
Computerized analysis of mesothelioma on CT scans.
S. Armato (2005)
Computer-assisted volumetric tumour assessment for the evaluation of patient response in malignant pleural mesothelioma
J. A. Schnabel (2011)
10.1016/J.CTRV.2007.07.012
Assessment of tumor response in malignant pleural mesothelioma.
G. Ceresoli (2007)
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