A new method for the high-precision assessment of tumor changes in response to treatment.
| dc.contributor.author | Tar, P | |
| dc.contributor.author | Thacker, N | |
| dc.contributor.author | Babur, M | |
| dc.contributor.author | Watson, Y | |
| dc.contributor.author | Cheung, S | |
| dc.contributor.author | Little, R | |
| dc.contributor.author | Gieling, R | |
| dc.contributor.author | Williams, K | |
| dc.contributor.author | O'Connor, James P B | |
| dc.date.accessioned | 2018-04-24T19:47:44Z | |
| dc.date.available | 2018-04-24T19:47:44Z | |
| dc.date.issued | 2018-03-14 | |
| dc.identifier.citation | A new method for the high-precision assessment of tumor changes in response to treatment. 2018, Bioinformatics | en |
| dc.identifier.issn | 1367-4811 | |
| dc.identifier.pmid | 29547950 | |
| dc.identifier.doi | 10.1093/bioinformatics/bty115 | |
| dc.identifier.uri | http://hdl.handle.net/10541/620902 | |
| dc.description.abstract | Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both normal 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 modelling (LPM) evaluates changes in apparent diffusion co-efficient (ADC) before and 72 hours after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes are compared to those attainable using a conventional t-test analysis on basic ADC distribution parameters. | |
| dc.language.iso | en | en |
| dc.rights | Archived with thanks to Bioinformatics (Oxford, England) | en |
| dc.title | A new method for the high-precision assessment of tumor changes in response to treatment. | en |
| dc.type | Article | en |
| dc.contributor.department | Division of Informatics, Imaging and Data Science, University of Manchester, Manchester, UK | en |
| dc.identifier.journal | Bioinformatics | en |
| refterms.dateFOA | 2018-12-17T15:19:34Z | |
| html.description.abstract | Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both normal 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 modelling (LPM) evaluates changes in apparent diffusion co-efficient (ADC) before and 72 hours after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes are compared to those attainable using a conventional t-test analysis on basic ADC distribution parameters. |
