Literature DB >> 20534736

A pilot study of volume measurement as a method of tumor response evaluation to aid biomarker development.

Binsheng Zhao1, Geoffrey R Oxnard, Chaya S Moskowitz, Mark G Kris, William Pao, Pingzhen Guo, Valerie M Rusch, Marc Ladanyi, Naiyer A Rizvi, Lawrence H Schwartz.   

Abstract

PURPOSE: Tissue biomarker discovery is potentially limited by conventional tumor measurement techniques, which have an uncertain ability to accurately distinguish sensitive and resistant tumors. Semiautomated volumetric measurement of computed tomography imaging has the potential to more accurately capture tumor growth dynamics, allowing for more exact separation of sensitive and resistant tumors and a more accurate comparison of tissue characteristics. EXPERIMENTAL
DESIGN: Forty-eight patients with early stage non-small cell lung cancer and clinical characteristics of sensitivity to gefitinib were studied. High-resolution computed tomography was done at baseline and after 3 weeks of gefitinib. Tumors were then resected and molecularly profiled. Unidimensional and volumetric measurements were done using a semiautomated algorithm. Measurement changes were evaluated for their ability to differentiate tumors with and without sensitizing mutations.
RESULTS: Forty-four percent of tumors had epidermal growth factor receptor-sensitizing mutations. Receiver operating characteristic curve analysis showed that volumetric measurement had a higher area under the curve than unidimensional measurement for identifying tumors harboring sensitizing mutations (P = 0.009). Tumor volume decrease of >24.9% was the imaging criteria best able to classify tumors with and without sensitizing mutations (sensitivity, 90%; specificity, 89%).
CONCLUSIONS: Volumetric tumor measurement was better than unidimensional tumor measurement at distinguishing tumors based on presence or absence of a sensitizing mutation. Use of volume-based response assessment for the development of tissue biomarkers could reduce contamination between sensitive and resistant tumor populations, improving our ability to identify meaningful predictors of sensitivity. ©2010 AACR.

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Year:  2010        PMID: 20534736      PMCID: PMC2940965          DOI: 10.1158/1078-0432.CCR-10-0125

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  30 in total

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10.  KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib.

Authors:  William Pao; Theresa Y Wang; Gregory J Riely; Vincent A Miller; Qiulu Pan; Marc Ladanyi; Maureen F Zakowski; Robert T Heelan; Mark G Kris; Harold E Varmus
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  53 in total

1.  Volumes to learn: advancing therapeutics with innovative computed tomography image data analysis.

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5.  A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response.

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Journal:  Nature       Date:  2012-03-18       Impact factor: 49.962

6.  Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes.

Authors:  Geoffrey R Oxnard; Binsheng Zhao; Camelia S Sima; Michelle S Ginsberg; Leonard P James; Robert A Lefkowitz; Pingzhen Guo; Mark G Kris; Lawrence H Schwartz; Gregory J Riely
Journal:  J Clin Oncol       Date:  2011-07-05       Impact factor: 44.544

Review 7.  New targetable oncogenes in non-small-cell lung cancer.

Authors:  Geoffrey R Oxnard; Adam Binder; Pasi A Jänne
Journal:  J Clin Oncol       Date:  2013-02-11       Impact factor: 44.544

8.  Evaluating the agreement between tumour volumetry and the estimated volumes of tumour lesions using an algorithm.

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9.  Exploring intra- and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals.

Authors:  Binsheng Zhao; Yongqiang Tan; Daniel J Bell; Sarah E Marley; Pingzhen Guo; Helen Mann; Marietta L J Scott; Lawrence H Schwartz; Dana C Ghiorghiu
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10.  Test-retest reproducibility analysis of lung CT image features.

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