Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images
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{"title"=>"Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images", "type"=>"journal", "authors"=>[{"first_name"=>"Ulas", "last_name"=>"Bagci", "scopus_author_id"=>"24176491700"}, {"first_name"=>"Jianhua", "last_name"=>"Yao", "scopus_author_id"=>"22837413500"}, {"first_name"=>"Kirsten", "last_name"=>"Miller-Jaster", "scopus_author_id"=>"52364578100"}, {"first_name"=>"Xinjian", "last_name"=>"Chen", "scopus_author_id"=>"36609812400"}, {"first_name"=>"Daniel J.", "last_name"=>"Mollura", "scopus_author_id"=>"8886027300"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84874205032", "pmid"=>"23431398", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84874205032", "issn"=>"19326203", "pui"=>"368387159", "doi"=>"10.1371/journal.pone.0057105"}, "id"=>"e4383f22-f6f8-32aa-acfa-dfa0569b3e96", "abstract"=>"We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on (18)F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 (18)F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75 ± 1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV(max) (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV(max). We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).", "link"=>"http://www.mendeley.com/research/predicting-future-morphological-changes-lesions-radiotracer-uptake-18ffdgpet-images", "reader_count"=>28, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>5, "Researcher"=>3, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>5, "Researcher"=>3, "Student > Ph. D. Student"=>5, "Student > Postgraduate"=>2, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>1, "Medicine and Dentistry"=>14, "Physics and Astronomy"=>4, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>14}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Computer Science"=>{"Computer Science"=>6}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Greece"=>1, "Korea (South)"=>1, "United States"=>1, "France"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1004696"], "description"=>"<p>Circularity, volume, and combined circularity and volume are used as surrogate truth for morphological change. Prediction abilities of features are summarized with Spearman rank coefficients. ns indicates non-significant correlation, minus sign indicates reverse relationship.</p>", "links"=>[], "tags"=>["morphological", "uptake", "regions", "corresponding", "spearman", "coefficients"], "article_id"=>665318, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.t004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Longitudinal_assessment_of_morphological_changes_of_uptake_regions_through_SUV_max_texture_and_combined_SUV_max_and_texture_features_are_given_with_corresponding_spearman_coefficients_and_p_values_/665318", "title"=>"Longitudinal assessment of morphological changes of uptake regions through SUV<sub>max</sub>, texture, and combined SUV<sub>max</sub> and texture features are given with corresponding spearman coefficients and p-values.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 01:28:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/700683"], "description"=>"<p>Average intensity (AVGint), standard deviation of intensities (SDint), median value of intensities (MEDIANint), maximum absolute deviation of intensities (MADint), interquartile of intensity histogram (IRQint), Autocorrelation (ACorr), contrast shade (Cshade), homogeneity (Homog), difference of entropy (DiffEntropy), maximum probability (MAX-PR), summation of square of intensity values (SUM-SQR), summation of average of intensity values (SUM-AVG), summation of variation of intensity values (SUM-VAR), short run emphasis (SRE), long run emphasis (LRE), gray level non-uniformity (GLN), run length non-uniformity (RLN), run percentage (RP), low gray level run emphasis (LGRE), high gray level run emphasis (HGRE), short run low gray level emphasis (SRLE), short run high gray level emphasis (SRHGE), long run low gray level emphasis (LRLGE), long run high gray level emphasis (LRHGE).</p>", "links"=>[], "tags"=>["deviation", "intensities", "median", "interquartile", "histogram", "autocorrelation", "homogeneity", "entropy", "probability", "summation", "values", "non-uniformity"], "article_id"=>371078, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_intensity_AVGint_standard_deviation_of_intensities_SDint_median_value_of_intensities_MEDIANint_maximum_absolute_deviation_of_intensities_MADint_interquartile_of_intensity_histogram_IRQint_Autocorrelation_ACorr_contrast_shade_Cshade_homogeneity_Ho/371078", "title"=>"Average intensity (AVGint), standard deviation of intensities (SDint), median value of intensities (MEDIANint), maximum absolute deviation of intensities (MADint), interquartile of intensity histogram (IRQint), Autocorrelation (ACorr), contrast shade (Cshade), homogeneity (Homog), difference of entropy (DiffEntropy), maximum probability (MAX-PR), summation of square of intensity values (SUM-SQR), summation of average of intensity values (SUM-AVG), summation of variation of intensity values (SUM-VAR), short run emphasis (SRE), long run emphasis (LRE), gray level non-uniformity (GLN), run length non-uniformity (RLN), run percentage (RP), low gray level run emphasis (LGRE), high gray level run emphasis (HGRE), short run low gray level emphasis (SRLE), short run high gray level emphasis (SRHGE), long run low gray level emphasis (LRLGE), long run high gray level emphasis (LRHGE).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 00:17:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/701832"], "description"=>"<div><p>We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on <sup>18</sup>F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 <sup>18</sup>F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV<sub>max</sub> (<i>p</i><0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV<sub>max</sub>. We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, <i>p</i><2e-16).</p> </div>", "links"=>[], "tags"=>["predicting", "morphological", "changes", "lesions", "radiotracer", "uptake", "18f-fdg-pet", "images"], "article_id"=>372231, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105", "stats"=>{"downloads"=>0, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Predicting_Future_Morphological_Changes_of_Lesions_from_Radiotracer_Uptake_in_18F_FDG_PET_Images__/372231", "title"=>"Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 00:37:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/701069"], "description"=>"<p>Second row: an example intra-observer variation was demonstrated (user 1 time 1: blue, user 1 time 2: red drawings). Third row: Users drawing (blue) and automatically found (white) boundaries of uptake regions were demonstrated.</p>", "links"=>[], "tags"=>["inter-observer", "was", "demonstrated", "fused", "pet-ct"], "article_id"=>371466, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_First_row_an_example_inter_observer_variation_was_demonstrated_in_fused_PET_CT_image_user_1_yellow_user_2_blue_drawings_/371466", "title"=>"First row: an example inter-observer variation was demonstrated in fused PET-CT image (user 1: yellow, user 2: blue drawings).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 00:24:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/701651"], "description"=>"<p>The selected five best informative features, and their histograms, box-plots with mean and median (diagonal), one-to-one regression curves in matrix row-column format (in lower panel), and spearman correlation values (in upper panel) are given.</p>", "links"=>[], "tags"=>["informative", "box-plots", "median", "one-to-one", "regression", "curves", "matrix", "row-column", "spearman", "values"], "article_id"=>372043, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.g007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_selected_five_best_informative_features_and_their_histograms_box_plots_with_mean_and_median_diagonal_one_to_one_regression_curves_in_matrix_row_column_format_in_lower_panel_and_spearman_correlation_values_in_upper_panel_are_given_/372043", "title"=>"The selected five best informative features, and their histograms, box-plots with mean and median (diagonal), one-to-one regression curves in matrix row-column format (in lower panel), and spearman correlation values (in upper panel) are given.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 00:34:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004734"], "description"=>"<p>Number of lesions is subject to change from patient to patient.</p>", "links"=>[], "tags"=>["demographics", "corresponding", "lesion", "numbers", "baseline", "follow-up", "scans"], "article_id"=>665356, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Patient_demographics_with_gender_information_SUV_max_values_corresponding_to_lesion_numbers_denoted_by_L_both_for_baseline_and_follow_up_scans_are_enlisted_/665356", "title"=>"Patient demographics with gender information; SUV<sub>max</sub> values corresponding to lesion numbers (denoted by L#) both for baseline and follow-up scans are enlisted.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 01:29:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/701493"], "description"=>"<p>The resultant correlation values are used in hierarchical clustering algorithm to show the detail relationships of the feature sets. Again correlation values are running from R = −1 negative correlation (white) to R = 1 positive correlation (blue).</p>", "links"=>[], "tags"=>["matrix", "obtained"], "article_id"=>371886, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.g006", "stats"=>{"downloads"=>3, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_matrix_is_obtained_using_only_the_features_which_have_strong_correlation_with_SUV_max_features_/371886", "title"=>"Correlation matrix is obtained using only the features, which have strong correlation with SUV<sub>max</sub> features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 00:31:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/701190"], "description"=>"<p>We parameterize the surface (a) of lesion using Euler angles of boundary points, and we colorized the surface points with respect to those angles in radians (b). This shape information (i.e., circularity) was used in longitudinal assessment of uptake changes.</p>", "links"=>[], "tags"=>["obtained", "segmented", "uptake", "regions", "non-specific"], "article_id"=>371590, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_example_surface_pair_obtained_from_segmented_uptake_regions_i_e_non_specific_mass_from_lung_regions_of_a_particular_patient_is_shown_/371590", "title"=>"An example surface pair obtained from segmented uptake regions (i.e., non-specific mass from lung regions of a particular patient) is shown.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 00:26:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004775"], "description"=>"***<p>denotes the highest correlation ratio. Correlation ratios with conventional global approach are also shown in the last column as a comparison to local approach.</p>", "links"=>[], "tags"=>["p-values", "correlating", "textural"], "article_id"=>665401, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.t003", "stats"=>{"downloads"=>7, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_ratios_95_confidence_intervals_and_p_values_of_significantly_correlating_textural_features_with_SUV_max_are_summarized_/665401", "title"=>"Correlation ratios, 95% confidence intervals, and p-values of significantly correlating textural features with SUV<sub>max</sub> are summarized.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 01:30:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/700860"], "description"=>"<p>(a) Automatically detected uptake regions (first), its segmented version (second), and fusion of segmented region into original image (third) are shown. After detection, the details of object (blue) and background (red) seeding and conducted segmentation are shown in (c) and (d), respectively. Segmented region (e) is divided into local windows and for each local window pre-defined textural and shape features are extracted (f). Tools to control extraction of textural features, segmentation, SUV analysis, and the immediate results are shown in (b).</p>", "links"=>[], "tags"=>["biotechnology", "oncology", "mathematics", "radiology and medical imaging"], "article_id"=>371258, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_example_view_from_our_proposed_framework_software_is_shown_/371258", "title"=>"An example view from our proposed framework/software is shown.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 00:20:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/1004715"], "description"=>"<p>Entropy and Max.PR follows normality assumption but with different variances (see F-test results in the text).</p>*<p>indicates accepted null-hypothesis.</p>", "links"=>[], "tags"=>["normality"], "article_id"=>665339, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.t005", "stats"=>{"downloads"=>8, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Shapiro_Wilk_normality_test_for_the_selected_feature_is_given_/665339", "title"=>"Shapiro-Wilk normality test for the selected feature is given.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-19 01:28:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/701280"], "description"=>"<p>A correlation matrix together with clustering (i.e., Pearson uncentered) of the feature points is presented from R = −1 negative correlation (white) to R = 1 positive correlation (blue).</p>", "links"=>[], "tags"=>["clustering", "based"], "article_id"=>371676, "categories"=>["Cancer", "Mathematics", "Biotechnology", "Cell Biology"], "users"=>["Ulas Bagci", "Jianhua Yao", "Kirsten Miller-Jaster", "Xinjian Chen", "Daniel J. Mollura"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0057105.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hierarchical_clustering_is_used_based_on_the_correlation_of_all_data_at_hand_/371676", "title"=>"Hierarchical clustering is used based on the correlation of all data at hand.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-19 00:27:56"}

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Relative Metric

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