Academic Radiology
Volume 19, Issue 3 , Pages 326-330, March 2012

Statistical Power in Quantitative Diffusion MRI of Tumor Response:

Strategies for Future Studies

  • Ted K. Yanagihara, PhD

      Affiliations

    • Department of Neuroscience, Columbia University, College of Physicians and Surgeons, 710 W. 168th Street, B41, New York, NY 10032
    • Medical Scientist Training Program, Columbia University, College of Physicians and Surgeons, 710 W. 168th Street, B41, New York, NY 10032
    • Corresponding Author InformationAddress correspondence to: T.K.Y.
  • ,
  • Benjamin Kennedy, MD

      Affiliations

    • Department of Neurological Surgery, Columbia University, College of Physicians and Surgeons, 710 W. 168th Street, B41, New York, NY 10032
  • ,
  • Krishna Surapaneni, MD

      Affiliations

    • Department of Radiology, Columbia University, College of Physicians and Surgeons, 710 W. 168th Street, B41, New York, NY 10032
  • ,
  • Jeffrey N. Bruce, MD

      Affiliations

    • Department of Neurological Surgery, Columbia University, College of Physicians and Surgeons, 710 W. 168th Street, B41, New York, NY 10032

Received 7 July 2011; accepted 12 October 2011. published online 19 December 2011.

Rationale and Objectives

Diffusion magnetic resonance imaging may be useful in tracking tumor growth and response to treatment. However, studies using these measures may lack statistical power to draw definitive conclusions regarding changes in tumor cellularity. Using apparent diffusion coefficient values taken from the literature, the investigators estimated sample sizes for a range of changes to the mean.

Materials and Methods

A literature search was performed of studies measuring the average apparent diffusion coefficients for various bodily tissues, and the mean and standard deviation from each study were recorded. Analyses of statistical power were then performed using these values and comparing them to a population of healthy controls.

Results

Tumor cellularity as measured by apparent diffusion coefficients may have high sensitivity, but the analyses indicate that investigations in this field may potentially suffer from low statistical power. For example, the findings indicate that samples of <20 patients may require a mean change of approximately 25% between study conditions.

Conclusions

Suggestions are offered for improvements in methodologic approaches and in data reporting to assist in overcoming the limitations of small sample sizes. On the basis of this literature review, reference values are provided to help investigators estimate study sample size to achieve adequate statistical power.

Key Words: Diffusion tensor imaging, average diffusion coefficient, statistical power, tumor progression

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 This study was supported by training grant 5T32GM0007367-34 from the National Institutes of Health (Bethesda, MD).

PII: S1076-6332(11)00511-3

doi:10.1016/j.acra.2011.10.024

Academic Radiology
Volume 19, Issue 3 , Pages 326-330, March 2012