A new method for the high-precision assessment of tumor changes in response to treatment

Tar, Paul, Thacker, Neil, Babur, Muhammad, Watson, Yvonne, Cheung, Sue, Little, Ross, Gieling, Roben, Williams, Kaye and O’Connor, James (2018) A new method for the high-precision assessment of tumor changes in response to treatment. Bioinformatics. ISSN 1367-4803

Preview

Text (Full text) Tar et al - A new method for the high-precision assessment of tumor changes in response to treatment OA.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (773kB) | Preview

Abstract

Motivation
Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both development and also in response to treatment. The large variations observed in control group, tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, linear Poisson modeling (LPM) evaluates changes in apparent diffusion co-efficient between baseline and 72 h after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes is compared to those attainable using a conventional t-test analysis on basic apparent diffusion co-efficient distribution parameters.

Results
When LPMs were applied to treated tumors, the LPMs detected highly significant changes. The analyses were significant for all tumors, equating to a gain in power of 4-fold (i.e. equivalent to having a sample size 16 times larger), compared with the conventional approach. In contrast, highly significant changes are only detected at a cohort level using t-tests, restricting their potential use within personalized medicine and increasing the number of animals required during testing. Furthermore, LPM enabled the relative volumes of responding and non-responding tissue to be estimated for each xenograft model. Leave-one-out analysis of the treated xenografts provided quality control and identified potential outliers, raising confidence in LPM data at clinically relevant sample sizes.

Availability and implementation
TINA Vision open source software is available from www.tina-vision.net.

Actions (login required)

View Item

Downloads