@MASTERSTHESIS\{IMM2014-06782,
    author       = "A. W. Lindberg",
    title        = "Quantitative tumor heterogeneity assessment on a nuclear population basis",
    year         = "2014",
    school       = "Technical University of Denmark, Department of Applied Mathematics and Computer Science",
    address      = "Richard Petersens Plads, Building 324, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk",
    type         = "",
    note         = "{DTU} supervisors: Professor Rasmus Larsen, rlar@dtu.dk, {DTU} Compute, and Professor Knut Conradsen, knco@dtu.dk, {DTU} Compute",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "20 \% of all treatments of breast cancer aren’t successful due to the heterogeneity in the cell population. The heterogeneity can be measured as the response from various biomarkers which also is used in the determination of the type of treatment.
226 {TMA} cores stained with either {ER} or Ki67 were aligned with their neighbor slice stained with {PCK} and the tumor cells with positive and negative response to the biomarker were segmented using the software {VIS}. An automatic method for a better visualization of the heterogeneity in the cell population response from the two biomarkers Ki67 and {ER} was developed in form of a heatmap that illustrated the percentage of positive cells in small areas.
The visualization was further used as a guidance to find the largest area with highest response (hottest hotspots) and an automatic calculation of the heterogeneity score in this area was performed. 110 of the 226 {TMA} cores were scored by a pathologist. The automatic calculated scores for the Ki67 {TMA} cores were compared with the pathologist scores. The scores calculated in the hottest hotspot were not significantly different from the pathologist scores but were in general 7 \% higher than the pathologist scores. The automatic calculated scores for the {ER} {TMA} cores were also compared with the pathologist scores. The scores were not found significantly different from the the pathologist scores but were in general 1.3 \% lower.
The impact on calculating the scores in hotspots was investigated and it was found that scores from the first, second, third and fourth hottest hotspot weren’t significantly different from the pathologist scores but scores calculated randomly outside the hottest hotspot were different from the pathologist scores."
}