Abstract
Objective
To develop a methodology which quantifies multiple changing lesion features resulting
in an optimized computed tomography (CT) response score (CRS) for prediction of overall
survival (OS) in response to treatment for metastatic colorectal carcinoma (MCRC).
Subjects and Methods
This Health Insurance Portability and Accountability Act-compliant, institutional
review board-approved retrospective study evaluated multiple changing imaging findings
and their correlation with OS with a new methodology comparing the baseline and first
post-treatment CT scans in 38 MCRC patients on last-line chemotherapy (cetuximab and
irinotecan). Tumor size/enhancement changes and interval development of new lesions
were quantified with either Likert-type scales (all parameters) or Response Evaluation
Criteria in Solid Tumors (RECIST) (size change only). The most predictive parameters
for OS were used to generate the CRS with an overall range of −3 (complete disappearance)
to +2 (definite tumor increase). The Cox Hazard Ratio was used to assess prediction
of survival. Reader agreement was evaluated by the kappa statistic.
Results
Tumor size was the best predictor of OS using the Likert-type scale or RECIST. The
CRS was not improved combining size change with other parameters. Use of the Likert-type
scale resulted in predicting OS with a Cox hazard ratio of 1.697 (P=.0004) and good agreement (kappa=0.73, 95% CI=0.41–1.10) between observers with no
significant difference using RECIST.
Conclusion
The methodology produces a CRS for MCRC predicting OS resulting from therapy which
expands standard RECIST guidelines to allow critical evaluation of multiple additional
imaging parameters. Size change alone was found to be the best parameter of those
considered in terms of maximizing agreement and prediction of OS.
Keywords
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References
- Imaging as a biomarker: standards for change measurements in therapy workshop summary.Acad Radiol. 2008; 15: 501-530
- End points and United States Food and Drug Administration approval of oncology drugs.J Clin Oncol. 2003; 21: 1404-1411
- Imaging response assessment in oncology.Cancer Imaging. 2006; 6: S126-S130
- New guidelines to evaluate the response to treatment in solid tumors.J Natl Cancer Inst. 2000; 92: 205-216
- New response evaluation criteria in solid tumours: revised RECIST guidelines (version 1.1).Eur J Cancer. 2009; 45: 228-247
- Evaluation of the response to treatment of solid tumours — a consensus statement of the international cancer imaging society.Br J Cancer. 2004; 90: 2256-2260
- Lung cancer: computerized quantification of tumor response — initial results.Radiology. 2006; 241: 892-898
- Primer on imaging technologies for cancer.J Clin Oncol. 2006; 24: 3225-3233
- Functional imaging in lung cancer.J Clin Oncol. 2005; 23: 3203-3211
- Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development.Clin Cancer Res. 2005; 11: 2785-2808
- Biomarkers in imaging: realizing radiology's future.Radiology. 2003; 227: 633-638
- Sometimes size doesn't matter: revaluating RECIST and tumor response rate end points.J Natl Cancer Inst. 2006; 98: 1272-1274
- Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies.Annu Rev Pharmacol Toxicol. 2001; 41: 347-366
- Using imaging biomarkers to accelerate drug development and clinical trials.Drug Discov Today. 2005; 10: 259-266
- BI-RADS decoded: detailed guidance on potentially confusing issues.Radiol Clin North Am. 2007; : 751-763
- BI-RADS Lexicon for US and mammography: interobserver variability and positive predictive value.Radiology. 2006; 239: 385-391
- Breast imaging reporting and data system, breast imaging atlas.4th ed. American College of Radiology, Reston, VA2003
Likert, R. (1932). A technique for the measurement of attitudes, archives of psychology, No.140.
- The use of a Simple Likert Scale to measure quality of life in brain tumor patients.J Neuro-Oncol. 2001; 55: 121-131
- Convergent evidence for construct validity of a 7-point Likert scale of lower limb muscle soreness.Clin J Sport Med. 2007; 17: 494-496
- Interpreting measures of treatment effect in cancer clinical trials.Oncologist. 2002; 7: 181-187
- The measurement of observer agreement for categorical data.Biometrics. 1977; 33: 159-174
- Automatic structuring of radiology reports: harbinger of a second information revolution in radiology.Radiology. 2002; 222: 5-7
- Chemotherapy for colorectal cancer liver metasteses.Oncologist. 2008; 13 (Epub 2008 Oct 6): 1063-1073
- A logistic regression model based on the national mammography database format to aid breast cancer diagnosis.AJR. 2009; 192: 1117-1127
- Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain.J Clin Epidemiol. 2001; 54: 979-985
- Primer on multiple regression models for diagnostic imaging research.Radiology. 2003; 229: 305-310
- K-ras Mutations and benefit from cetuximab in advanced colorectal cancer.N Engl J Med. 2008; 359 (No.17): 1757-1765
- Does training in the breast imaging reporting and data system (BI-RADS) improve biopsy recommendations or feature analysis agreement with experienced breast imagers at mammography?.Radiology. 2002; 224: 871-880
Article info
Publication history
Published online: March 17, 2010
Accepted:
January 10,
2010
Received in revised form:
January 2,
2010
Received:
November 5,
2009
Identification
Copyright
© 2010 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
