Low-Order Non-Spatial Effects Dominate Second-Order Spatial Effects in the Texture Quantifier Analysis of 18F-FDG-PET Images
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{"title"=>"Low-order non-spatial effects dominate second-order spatial effects in the texture quantifier analysis of 18F-FDG-PET images", "type"=>"journal", "authors"=>[{"first_name"=>"Frank J.", "last_name"=>"Brook", "scopus_author_id"=>"56533610800"}, {"first_name"=>"Perry W.", "last_name"=>"Grigsby", "scopus_author_id"=>"35407226300"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84923886320", "doi"=>"10.1371/journal.pone.0116574", "pui"=>"602543247", "pmid"=>"25714472", "scopus"=>"2-s2.0-84923886320", "issn"=>"19326203"}, "id"=>"c43d765a-f420-38fa-8a21-6399698a6ac3", "abstract"=>"BACKGROUND:There is increasing interest in applying image texture quantifiers to assess the intra-tumor heterogeneity observed in FDG-PET images of various cancers. Use of these quantifiers as prognostic indicators of disease outcome and/or treatment response has yielded inconsistent results. We study the general applicability of some well-established texture quantifiers to the image data unique to FDG-PET.\\n\\nMETHODS:We first created computer-simulated test images with statistical properties consistent with clinical image data for cancers of the uterine cervix. We specifically isolated second-order statistical effects from low-order effects and analyzed the resulting variation in common texture quantifiers in response to contrived image variations. We then analyzed the quantifiers computed for FIGOIIb cervical cancers via receiver operating characteristic (ROC) curves and via contingency table analysis of detrended quantifier values.\\n\\nRESULTS:We found that image texture quantifiers depend strongly on low-effects such as tumor volume and SUV distribution. When low-order effects are controlled, the image texture quantifiers tested were not able to discern only the second-order effects. Furthermore, the results of clinical tumor heterogeneity studies might be tunable via choice of patient population analyzed.\\n\\nCONCLUSION:Some image texture quantifiers are strongly affected by factors distinct from the second-order effects researchers ostensibly seek to assess via those quantifiers.", "link"=>"http://www.mendeley.com/research/loworder-nonspatial-effects-dominate-secondorder-spatial-effects-texture-quantifier-analysis-18ffdgp", "reader_count"=>17, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>2, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Other"=>3, "Student > Master"=>3, "Student > Bachelor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>2, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Other"=>3, "Student > Master"=>3, "Student > Bachelor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>1, "Medicine and Dentistry"=>7, "Agricultural and Biological Sciences"=>1, "Physics and Astronomy"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1924940"], "description"=>"<p>Both three-dimensional objects have identical gray level distributions yet stark differences in spatial arrangement are clear. The vertical edge of the images corresponds to a length of 64 mm.</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318350, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.g001", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cross_sections_of_simulated_objects_/1318350", "title"=>"Cross-sections of simulated objects.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924941"], "description"=>"<p>There is effectively no difference between these quantifiers for the homogeneous (gray, circles) and heterogeneous (black, triangles) objects.</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318351, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Entropy_and_energy_plotted_versus_object_volume_/1318351", "title"=>"Entropy and energy plotted versus object volume.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924942"], "description"=>"<p>The dissimilarity scales <i>non-linearly</i> with volume. The trend curve for the homogeneous objects (gray, circles) crosses that of the heterogeneous objects (black, triangles).</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318352, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dissimilarity_plotted_versus_object_volume_/1318352", "title"=>"Dissimilarity plotted versus object volume.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924943"], "description"=>"<p>The volume where homogeneity correctly discriminates homogeneous objects (gray, circles) from heterogeneous ones (black, triangles) is slightly less than for dissimilarity.</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318353, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Homogeneity_plotted_versus_object_volume_/1318353", "title"=>"Homogeneity plotted versus object volume.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924944"], "description"=>"<p>The volume where the mean-field energy correctly discriminates homogeneous objects (gray, circles) from heterogeneous ones (black, triangles) is less than for homogeneity.</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318354, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_field_energy_plotted_versus_object_volume_/1318354", "title"=>"Mean field energy plotted versus object volume.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924945"], "description"=>"<p>Here, <i>p(m, n)</i> is the probability that distinct gray levels <i>m</i> and <i>n</i> appear in adjacency within the three-dimensional object analyzed.</p><p>Texture Quantifier Definitions.</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318355, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.t001", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Texture_Quantifier_Definitions_/1318355", "title"=>"Texture Quantifier Definitions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924946"], "description"=>"<p>Here, τ is Kendall’s measure of rank correlation with tumor volume and AUC is the area under the receiver-operating curve for binary treatment response.</p><p>Texture Quantifiers Applied to Clinical Data.</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318356, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.t002", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Texture_Quantifiers_Applied_to_Clinical_Data_/1318356", "title"=>"Texture Quantifiers Applied to Clinical Data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924947"], "description"=>"<p>Note that here Cramer’s <i>ϕ</i> is derived from raw quantifier values whereas the AUC was derived from volume-detrended quantifier values.</p><p>Texture Quantifiers Applied to Similar Clinical Pairs.</p>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318357, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0116574.t003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Texture_Quantifiers_Applied_to_Similar_Clinical_Pairs_/1318357", "title"=>"Texture Quantifiers Applied to Similar Clinical Pairs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-02-25 04:00:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1924950", "https://ndownloader.figshare.com/files/1924951"], "description"=>"<div><p>Background</p><p>There is increasing interest in applying image texture quantifiers to assess the intra-tumor heterogeneity observed in FDG-PET images of various cancers. Use of these quantifiers as prognostic indicators of disease outcome and/or treatment response has yielded inconsistent results. We study the general applicability of some well-established texture quantifiers to the image data unique to FDG-PET.</p><p>Methods</p><p>We first created computer-simulated test images with statistical properties consistent with clinical image data for cancers of the uterine cervix. We specifically isolated second-order statistical effects from low-order effects and analyzed the resulting variation in common texture quantifiers in response to contrived image variations. We then analyzed the quantifiers computed for FIGOIIb cervical cancers via receiver operating characteristic (ROC) curves and via contingency table analysis of detrended quantifier values.</p><p>Results</p><p>We found that image texture quantifiers depend strongly on low-effects such as tumor volume and SUV distribution. When low-order effects are controlled, the image texture quantifiers tested were not able to discern only the second-order effects. Furthermore, the results of clinical tumor heterogeneity studies might be tunable via choice of patient population analyzed.</p><p>Conclusion</p><p>Some image texture quantifiers are strongly affected by factors distinct from the second-order effects researchers ostensibly seek to assess via those quantifiers.</p></div>", "links"=>[], "tags"=>["roc", "detrended quantifier values.ResultsWe", "tumor heterogeneity studies", "contingency table analysis", "image texture quantifiers", "patient population analyzed.ConclusionSome image texture quantifiers", "image data", "suv", "Texture Quantifier Analysis", "texture quantifiers"], "article_id"=>1318360, "categories"=>["Biological Sciences"], "users"=>["Frank J. Brooks", "Perry W. Grigsby"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0116574.s001", "https://dx.doi.org/10.1371/journal.pone.0116574.s002"], "stats"=>{"downloads"=>1, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Low_Order_Non_Spatial_Effects_Dominate_Second_Order_Spatial_Effects_in_the_Texture_Quantifier_Analysis_of_18F_FDG_PET_Images_/1318360", "title"=>"Low-Order Non-Spatial Effects Dominate Second-Order Spatial Effects in the Texture Quantifier Analysis of 18F-FDG-PET Images", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-02-25 04:00:44"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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