Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
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{"title"=>"Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma", "type"=>"journal", "authors"=>[{"first_name"=>"Olya", "last_name"=>"Grove", "scopus_author_id"=>"56205335900"}, {"first_name"=>"Anders E.", "last_name"=>"Berglund", "scopus_author_id"=>"7006543113"}, {"first_name"=>"Matthew B.", "last_name"=>"Schabath", "scopus_author_id"=>"6603452555"}, {"first_name"=>"Hugo J.W.L.", "last_name"=>"Aerts", "scopus_author_id"=>"16479697600"}, {"first_name"=>"Andre", "last_name"=>"Dekker", "scopus_author_id"=>"8876190500"}, {"first_name"=>"Hua", "last_name"=>"Wang", "scopus_author_id"=>"55937209500"}, {"first_name"=>"Emmanuel", "last_name"=>"Rios Velazquez", "scopus_author_id"=>"54893115000"}, {"first_name"=>"Philippe", "last_name"=>"Lambin", "scopus_author_id"=>"35242663400"}, {"first_name"=>"Yuhua", "last_name"=>"Gu", "scopus_author_id"=>"54784221400"}, {"first_name"=>"Yoganand", "last_name"=>"Balagurunathan", "scopus_author_id"=>"6601964766"}, {"first_name"=>"Edward", "last_name"=>"Eikman", "scopus_author_id"=>"6701479050"}, {"first_name"=>"Robert A.", "last_name"=>"Gatenby", "scopus_author_id"=>"7004904379"}, {"first_name"=>"Steven", "last_name"=>"Eschrich", "scopus_author_id"=>"6602433990"}, {"first_name"=>"Robert J.", "last_name"=>"Gillies", "scopus_author_id"=>"7102089341"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84928902792", "sgr"=>"84928902792", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0118261", "pmid"=>"25739030", "isbn"=>"10.1371/journal.pone.0118261", "pui"=>"604166277"}, "id"=>"49cda7cb-644b-32a3-9557-c4124c10d48e", "abstract"=>"Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.", "link"=>"http://www.mendeley.com/research/quantitative-computed-tomographic-descriptors-associate-tumor-shape-complexity-intratumor-heterogene", "reader_count"=>91, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>4, "Researcher"=>21, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>4, "Other"=>7, "Student > Master"=>7, "Student > Bachelor"=>7, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>4, "Researcher"=>21, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>22, "Student > Postgraduate"=>4, "Other"=>7, "Student > Master"=>7, "Student > Bachelor"=>7, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>13, "Engineering"=>11, "Nursing and Health Professions"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>2, "Medicine and Dentistry"=>29, "Agricultural and Biological Sciences"=>4, "Physics and Astronomy"=>12, "Computer Science"=>16, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>11}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>29}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>12}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>16}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>13}}, "reader_count_by_country"=>{"United States"=>3, "Italy"=>1, "France"=>1}, "group_count"=>7}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1935067"], "description"=>"<p><sup>1</sup> 96.7% (No. = 59) of this study population were ever smokers and 96.7% (No. = 59) were White race</p><p><sup>2</sup> Other includes B30s, B41s, B70s, CHST, FC01, FC13, LUNG, and STANDARD</p><p><sup>3</sup> Distribution based on the tertile values</p><p>Distribution of study population demographics and imaging parameters by imaging biomarkers in Cohort 1.</p>", "links"=>[], "tags"=>["entropy", "ccc", "ratio", "survival", "convexity", "Segmented tumor regions", "intratumor density variation", "lung adenocarcinoma", "Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity", "Preoperative chest CTs", "dr", "cohort", "tumor shape complexity"], "article_id"=>1325513, "categories"=>["Biological Sciences"], "users"=>["Olya Grove", "Anders E. Berglund", "Matthew B. Schabath", "Hugo J. W. L. Aerts", "Andre Dekker", "Hua Wang", "Emmanuel Rios Velazquez", "Philippe Lambin", "Yuhua Gu", "Yoganand Balagurunathan", "Edward Eikman", "Robert A. Gatenby", "Steven Eschrich", "Robert J. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118261.t001", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_study_population_demographics_and_imaging_parameters_by_imaging_biomarkers_in_Cohort_1_/1325513", "title"=>"Distribution of study population demographics and imaging parameters by imaging biomarkers in Cohort 1.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-04 03:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1935066"], "description"=>"<p>Convexity (a) shows similar range across cohorts (training-green, test-blue) However, training cohort is enriched with round tumors. The range of values for entropy ratio feature (b) is larger in training cohort. Both convexity (a) and entropy ratio (b) consistently capture targeted tumor characteristics in both cohorts.</p>", "links"=>[], "tags"=>["entropy", "ccc", "ratio", "survival", "convexity", "Segmented tumor regions", "intratumor density variation", "lung adenocarcinoma", "Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity", "Preoperative chest CTs", "dr", "cohort", "tumor shape complexity"], "article_id"=>1325512, "categories"=>["Biological Sciences"], "users"=>["Olya Grove", "Anders E. Berglund", "Matthew B. Schabath", "Hugo J. W. L. Aerts", "Andre Dekker", "Hua Wang", "Emmanuel Rios Velazquez", "Philippe Lambin", "Yuhua Gu", "Yoganand Balagurunathan", "Edward Eikman", "Robert A. Gatenby", "Steven Eschrich", "Robert J. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118261.g004", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histogram_of_the_two_imaging_features_across_cohorts_/1325512", "title"=>"Histogram of the two imaging features across cohorts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-04 03:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1935064"], "description"=>"<p>The tumors in the two prognostic groups (a) did not appear significantly different in the CT scans (b).</p>", "links"=>[], "tags"=>["entropy", "ccc", "ratio", "survival", "convexity", "Segmented tumor regions", "intratumor density variation", "lung adenocarcinoma", "Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity", "Preoperative chest CTs", "dr", "cohort", "tumor shape complexity"], "article_id"=>1325510, "categories"=>["Biological Sciences"], "users"=>["Olya Grove", "Anders E. Berglund", "Matthew B. Schabath", "Hugo J. W. L. Aerts", "Andre Dekker", "Hua Wang", "Emmanuel Rios Velazquez", "Philippe Lambin", "Yuhua Gu", "Yoganand Balagurunathan", "Edward Eikman", "Robert A. Gatenby", "Steven Eschrich", "Robert J. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118261.g003", "stats"=>{"downloads"=>4, "page_views"=>24, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Entropy_ratio_between_the_core_and_border_regions_of_the_tumor_is_predictive_of_patient_survival_/1325510", "title"=>"Entropy ratio between the core and border regions of the tumor is predictive of patient survival.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-04 03:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1935058"], "description"=>"<p>Convexity is computed as a ratio of tumor border (blue) to convex hull (red) (a). Convexity feature tracks the change in tumor morphology (b). Convexity is predictive of patient overall survival when dichotomized at the median value (c).</p>", "links"=>[], "tags"=>["entropy", "ccc", "ratio", "survival", "convexity", "Segmented tumor regions", "intratumor density variation", "lung adenocarcinoma", "Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity", "Preoperative chest CTs", "dr", "cohort", "tumor shape complexity"], "article_id"=>1325507, "categories"=>["Biological Sciences"], "users"=>["Olya Grove", "Anders E. Berglund", "Matthew B. Schabath", "Hugo J. W. L. Aerts", "Andre Dekker", "Hua Wang", "Emmanuel Rios Velazquez", "Philippe Lambin", "Yuhua Gu", "Yoganand Balagurunathan", "Edward Eikman", "Robert A. Gatenby", "Steven Eschrich", "Robert J. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118261.g001", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Convexity_feature_was_developed_to_quantify_tumor_shape_/1325507", "title"=>"Convexity feature was developed to quantify tumor shape.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-04 03:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1935085", "https://ndownloader.figshare.com/files/1935086", "https://ndownloader.figshare.com/files/1935087", "https://ndownloader.figshare.com/files/1935088", "https://ndownloader.figshare.com/files/1935089", "https://ndownloader.figshare.com/files/1935090", "https://ndownloader.figshare.com/files/1935091", "https://ndownloader.figshare.com/files/1935092", "https://ndownloader.figshare.com/files/1935093", "https://ndownloader.figshare.com/files/1935094"], "description"=>"<div><p>Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.</p></div>", "links"=>[], "tags"=>["entropy", "ccc", "ratio", "survival", "convexity", "Segmented tumor regions", "intratumor density variation", "lung adenocarcinoma", "Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity", "Preoperative chest CTs", "dr", "cohort", "tumor shape complexity"], "article_id"=>1325524, "categories"=>["Biological Sciences"], "users"=>["Olya Grove", "Anders E. Berglund", "Matthew B. Schabath", "Hugo J. W. L. Aerts", "Andre Dekker", "Hua Wang", "Emmanuel Rios Velazquez", "Philippe Lambin", "Yuhua Gu", "Yoganand Balagurunathan", "Edward Eikman", "Robert A. Gatenby", "Steven Eschrich", "Robert J. Gillies"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0118261.s001", "https://dx.doi.org/10.1371/journal.pone.0118261.s002", "https://dx.doi.org/10.1371/journal.pone.0118261.s003", "https://dx.doi.org/10.1371/journal.pone.0118261.s004", "https://dx.doi.org/10.1371/journal.pone.0118261.s005", "https://dx.doi.org/10.1371/journal.pone.0118261.s006", "https://dx.doi.org/10.1371/journal.pone.0118261.s007", "https://dx.doi.org/10.1371/journal.pone.0118261.s008", "https://dx.doi.org/10.1371/journal.pone.0118261.s009", "https://dx.doi.org/10.1371/journal.pone.0118261.s010"], "stats"=>{"downloads"=>9, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantitative_Computed_Tomographic_Descriptors_Associate_Tumor_Shape_Complexity_and_Intratumor_Heterogeneity_with_Prognosis_in_Lung_Adenocarcinoma_/1325524", "title"=>"Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-03-04 03:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1935068"], "description"=>"<p>Abbreviations: Hazard Ratio, HR; Confidence Intervals, CI.</p><p>Statistically significant hazard ratios (p < 0.05) are shown in bold.</p><p><sup>1</sup> The imaging features are dichotomized at their respective median values and age is dichotomized at 65 years</p><p><sup>2</sup> Each imaging biomarker is analyzed independently in separate univariate models. The unadjusted HRs represent the main effects of each covariate.</p><p><sup>3</sup> Based on forward selection, only two imaging biomarkers are included in the model but excluded age, gender, and stage.</p><p><sup>4</sup> Only two imaging biomarkers are included in the model in addition to age, gender, and stage</p><p>Cox Proportional Hazards Models for Overall Survival.</p>", "links"=>[], "tags"=>["entropy", "ccc", "ratio", "survival", "convexity", "Segmented tumor regions", "intratumor density variation", "lung adenocarcinoma", "Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity", "Preoperative chest CTs", "dr", "cohort", "tumor shape complexity"], "article_id"=>1325514, "categories"=>["Biological Sciences"], "users"=>["Olya Grove", "Anders E. Berglund", "Matthew B. Schabath", "Hugo J. W. L. Aerts", "Andre Dekker", "Hua Wang", "Emmanuel Rios Velazquez", "Philippe Lambin", "Yuhua Gu", "Yoganand Balagurunathan", "Edward Eikman", "Robert A. Gatenby", "Steven Eschrich", "Robert J. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118261.t002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cox_Proportional_Hazards_Models_for_Overall_Survival_/1325514", "title"=>"Cox Proportional Hazards Models for Overall Survival.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-03-04 03:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/1935063"], "description"=>"<p>While some tumors present with consistent mean entropy across the core and the boundary (a), others have a distinct difference in the values (b).</p>", "links"=>[], "tags"=>["entropy", "ccc", "ratio", "survival", "convexity", "Segmented tumor regions", "intratumor density variation", "lung adenocarcinoma", "Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity", "Preoperative chest CTs", "dr", "cohort", "tumor shape complexity"], "article_id"=>1325509, "categories"=>["Biological Sciences"], "users"=>["Olya Grove", "Anders E. Berglund", "Matthew B. Schabath", "Hugo J. W. L. Aerts", "Andre Dekker", "Hua Wang", "Emmanuel Rios Velazquez", "Philippe Lambin", "Yuhua Gu", "Yoganand Balagurunathan", "Edward Eikman", "Robert A. Gatenby", "Steven Eschrich", "Robert J. Gillies"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0118261.g002", "stats"=>{"downloads"=>2, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Entropy_ratio_was_developed_to_quantify_intensity_variations_across_the_tumor_/1325509", "title"=>"Entropy ratio was developed to quantify intensity variations across the tumor.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-03-04 03:04:53"}

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