% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Valous:130706,
      author       = {N. Valous$^*$ and B. Lahrmann and N. Halama and F. Bergmann
                      and D. Jäger$^*$ and N. Grabe},
      title        = {{S}patial intratumoral heterogeneity of proliferation in
                      immunohistochemical images of solid tumors.},
      journal      = {Medical physics},
      volume       = {43},
      number       = {6},
      issn         = {0094-2405},
      address      = {New York, NY},
      reportid     = {DKFZ-2017-05784},
      pages        = {2936 - 2947},
      year         = {2016},
      abstract     = {The interactions of neoplastic cells with each other and
                      the microenvironment are complex. To understand intratumoral
                      heterogeneity, subtle differences should be quantified. Main
                      factors contributing to heterogeneity include the gradient
                      ischemic level within neoplasms, action of microenvironment,
                      mechanisms of intercellular transfer of genetic information,
                      and differential mechanisms of modifications of genetic
                      material/proteins. This may reflect on the expression of
                      biomarkers in the context of prognosis/stratification.
                      Hence, a rigorous approach for assessing the spatial
                      intratumoral heterogeneity of histological biomarker
                      expression with accuracy and reproducibility is required,
                      since patterns in immunohistochemical images can be
                      challenging to identify and describe.A quantitative method
                      that is useful for characterizing complex irregular
                      structures is lacunarity; it is a multiscale technique that
                      exhaustively samples the image, while the decay of its index
                      as a function of window size follows characteristic patterns
                      for different spatial arrangements. In histological images,
                      lacunarity provides a useful measure for the spatial
                      organization of a biomarker when a sampling scheme is
                      employed and relevant features are computed. The proposed
                      approach quantifies the segmented proliferative cells and
                      not the textural content of the histological slide, thus
                      providing a more realistic measure of heterogeneity within
                      the sample space of the tumor region. The aim is to
                      investigate in whole sections of primary pancreatic
                      neuroendocrine neoplasms (pNENs), using whole-slide imaging
                      and image analysis, the spatial intratumoral heterogeneity
                      of Ki-67 immunostains. Unsupervised learning is employed to
                      verify that the approach can partition the tissue sections
                      according to distributional heterogeneity.The architectural
                      complexity of histological images has shown that single
                      measurements are often insufficient. Inhomogeneity of
                      distribution depends not only on percentage content of
                      proliferation phase but also on how the phase fills the
                      space. Lacunarity curves demonstrate variations in the
                      sampled image sections. Since the spatial distribution of
                      proliferation in each case is different, the width of the
                      curves changes too. Image sections that have smaller
                      numerical variations in the computed features correspond to
                      neoplasms with spatially homogeneous proliferation, while
                      larger variations correspond to cases where proliferation
                      shows various degrees of clumping. Grade 1
                      (uniform/nonuniform: $74\%/26\%)$ and grade 3 (uniform:
                      $100\%)$ pNENs demonstrate a more homogeneous proliferation
                      with grade 1 neoplasms being more variant, while grade 2
                      tumor regions render a more diverse landscape $(50\%/50\%).$
                      Hence, some cases show an increased degree of spatial
                      heterogeneity comparing to others with similar grade.
                      Whether this is a sign of different tumor biology and an
                      association with a more benign/malignant clinical course
                      needs to be investigated further. The extent and range of
                      spatial heterogeneity has the potential to be evaluated as a
                      prognostic marker.The association with tumor grade as well
                      as the rationale that the methodology reflects true tumor
                      architecture supports the technical soundness of the method.
                      This reflects a general approach which is relevant to other
                      solid tumors and biomarkers. Drawing upon the merits of
                      computational biomedicine, the approach uncovers salient
                      features for use in future studies of clinical relevance.},
      cin          = {G010 / D120},
      ddc          = {610},
      cid          = {I:(DE-He78)G010-20160331 / I:(DE-He78)D120-20160331},
      pnm          = {317 - Translational cancer research (POF3-317)},
      pid          = {G:(DE-HGF)POF3-317},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:27277043},
      doi          = {10.1118/1.4949003},
      url          = {https://inrepo02.dkfz.de/record/130706},
}