Quantitative Risk Stratification of Oral Leukoplakia with Exfoliative Cytology
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{"title"=>"Quantitative risk stratification of oral leukoplakia with exfoliative cytology", "type"=>"journal", "authors"=>[{"first_name"=>"Yao", "last_name"=>"Liu", "scopus_author_id"=>"56556521000"}, {"first_name"=>"Jianying", "last_name"=>"Li", "scopus_author_id"=>"56645040300"}, {"first_name"=>"Xiaoyong", "last_name"=>"Liu", "scopus_author_id"=>"36065320600"}, {"first_name"=>"Xudong", "last_name"=>"Liu", "scopus_author_id"=>"57192260284"}, {"first_name"=>"Waqaar", "last_name"=>"Khawar", "scopus_author_id"=>"56644703700"}, {"first_name"=>"Xinyan", "last_name"=>"Zhang", "scopus_author_id"=>"15077613000"}, {"first_name"=>"Fan", "last_name"=>"Wang", "scopus_author_id"=>"56643347000"}, {"first_name"=>"Xiaoxin", "last_name"=>"Chen", "scopus_author_id"=>"35274508100"}, {"first_name"=>"Zheng", "last_name"=>"Sun", "scopus_author_id"=>"56555490400"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"604392669", "issn"=>"19326203", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0126760", "scopus"=>"2-s2.0-84929346773", "pmid"=>"25978541", "sgr"=>"84929346773"}, "id"=>"14d73d43-496d-3f46-b7bd-983f12778288", "abstract"=>"Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma (OSCC). Test outcome is reported as \"negative\", \"atypical\" (defined as abnormal epithelial changes of uncertain diagnostic significance), and \"positive\" (defined as definitive cellular evidence of epithelial dysplasia or carcinoma). The major challenge is how to properly manage the \"atypical\" patients in order to diagnose OSCC early and prevent OSCC. In this study, we collected exfoliative cytology data, histopathology data, and clinical data of normal subjects (n=102), oral leukoplakia (OLK) patients (n=82), and OSCC patients (n=93), and developed a data analysis procedure for quantitative risk stratification of OLK patients. This procedure involving a step called expert-guided data transformation and reconstruction (EdTAR) which allows automatic data processing and reconstruction and reveals informative signals for subsequent risk stratification. Modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Among the several models tested using resampling methods for parameter pruning and performance evaluation, Support Vector Machine (SVM) was found to be optimal with a high sensitivity (median>0.98) and specificity (median>0.99). With the SVM model, we constructed an oral cancer risk index (OCRI) which may potentially guide clinical follow-up of OLK patients. One OLK patient with an initial OCRI of 0.88 developed OSCC after 40 months of follow-up. In conclusion, we have developed a statistical method for qualitative risk stratification of OLK patients. This method may potentially improve cost-effectiveness of clinical follow-up of OLK patients, and help design clinical chemoprevention trial for high-risk populations.", "link"=>"http://www.mendeley.com/research/quantitative-risk-stratification-oral-leukoplakia-exfoliative-cytology-3", "reader_count"=>15, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Student > Doctoral Student"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>4, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Student > Doctoral Student"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>4, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>1, "Medicine and Dentistry"=>9, "Agricultural and Biological Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>3}}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2070412"], "description"=>"<p>(A) Selected cells with abnormally high DI values (>2.3). (B) A scatter plot with y-axis as the area of nucleus and x-axis as DI value. (C) Distribution histogram of DI values of all nuclei. (D) Distribution histogram of DI values of the three cell populations after simulation from normal distribution, diploid cell population (red; μ = 1.001, σ = 0.19), tetraploid cell population (green; μ = 2.002, σ = 0.25) and aneuploidy cell population (blue; μ = 2.300, σ = 0.5). When these three cell populations are merged at the ratio of 0.893:0.092:0.005, a composite distribution histogram (black) can be generated.</p>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416928, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126760.g001", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_DNA_contents_in_exfoliative_cytology_/1416928", "title"=>"Distribution of DNA contents in exfoliative cytology.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-15 03:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2070425"], "description"=>"<p>General characteristics of normal subjects, OLK patients and OSCC patients.</p>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416941, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126760.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_General_characteristics_of_normal_subjects_OLK_patients_and_OSCC_patients_/1416941", "title"=>"General characteristics of normal subjects, OLK patients and OSCC patients.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-15 03:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2070424"], "description"=>"<p>Exfoliative cytology was performed in April 2008 and a density plot of DI data was generated (A).With EdTAR, positive signals were relatively amplified and an OCRI was calculated as 0.88 (B). Histopathology of biopsy showed mild dysplasia on H&E stained section, scale bar = 100 μm (C). A tumor was observed in August 2011 with a histopathological diagnosis of OSCC, scale bar = 100 μm (D).</p>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416940, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126760.g006", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Application_of_EdTAR_in_clinical_follow_up_of_one_patient_Case_128141_/1416940", "title"=>"Application of EdTAR in clinical follow-up of one patient (Case 128141).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-15 03:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2070418"], "description"=>"<p>OCRI was calculated for each case with known pathology, and ranged between 0 and 1, where 0 indicates the lowest risk of OSCC and 1 indicates the highest risk of OSCC.</p>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416934, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126760.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Oral_Cancer_Risk_Index_OCRI_of_normal_subjects_OLK_patients_and_OSCC_patients_/1416934", "title"=>"Oral Cancer Risk Index (OCRI) of normal subjects, OLK patients and OSCC patients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-15 03:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2070414"], "description"=>"<p>All density plots have x-axis as DI value and y-axis as density. Panel A, D and G showed density plots before data processing by EdTAR. In Panel A, a major peek with a DI of 0.995 represents the diploid cell population, where another small peaks (DI = 0.594) was a minor population possibly due to image processing. In Panel D, a major peek with a DI of 0.798 represents the diploid cell population (3,590 cells). Other than this peak, four peaks with DI values of 1.25, 1.75, 2.22, and 2.74, were present. In Panel G, a major peek with a DI of 1.02 represents the diploid cell population, and a second peak with a DI of 1.79 represents the tetraploid cell population. Other than these two peaks, three peaks with DI values of 3.25, 3.57, and 3.99 were present, and were believed to represent the aneuploidy cell population. Panel B, E and H corresponding with Panel A, D and G respectively were three plots showing the net results of data processing by EdTAR. Signals of the aneuploidy cell populations were amplified in Panel E and H. Panel C, F and I showed boxplots of newly constructed variables after data processing with EdTAR. The x-axis indicated the new variables along a range of DI [0–8] and y-axis the boxplot of available values for each variable.</p>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416930, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126760.g003", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Application_of_EdTAR_in_processing_data_of_three_samples_with_pathological_diagnosis_of_normal_A_C_OLK_D_F_and_OSCC_G_I_/1416930", "title"=>"Application of EdTAR in processing data of three samples with pathological diagnosis of normal (A-C), OLK (D-F), and OSCC (G-I).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-15 03:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2070427", "https://ndownloader.figshare.com/files/2070428", "https://ndownloader.figshare.com/files/2070429"], "description"=>"<div><p>Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma (OSCC). Test outcome is reported as “negative”, “atypical” (defined as abnormal epithelial changes of uncertain diagnostic significance), and “positive” (defined as definitive cellular evidence of epithelial dysplasia or carcinoma). The major challenge is how to properly manage the “atypical” patients in order to diagnose OSCC early and prevent OSCC. In this study, we collected exfoliative cytology data, histopathology data, and clinical data of normal subjects (n=102), oral leukoplakia (OLK) patients (n=82), and OSCC patients (n=93), and developed a data analysis procedure for quantitative risk stratification of OLK patients. This procedure involving a step called expert-guided data transformation and reconstruction (EdTAR) which allows automatic data processing and reconstruction and reveals informative signals for subsequent risk stratification. Modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Among the several models tested using resampling methods for parameter pruning and performance evaluation, Support Vector Machine (SVM) was found to be optimal with a high sensitivity (median>0.98) and specificity (median>0.99). With the SVM model, we constructed an oral cancer risk index (OCRI) which may potentially guide clinical follow-up of OLK patients. One OLK patient with an initial OCRI of 0.88 developed OSCC after 40 months of follow-up. In conclusion, we have developed a statistical method for qualitative risk stratification of OLK patients. This method may potentially improve cost-effectiveness of clinical follow-up of OLK patients, and help design clinical chemoprevention trial for high-risk populations.</p></div>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416943, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0126760.s001", "https://dx.doi.org/10.1371/journal.pone.0126760.s002", "https://dx.doi.org/10.1371/journal.pone.0126760.s003"], "stats"=>{"downloads"=>4, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Quantitative_Risk_Stratification_of_Oral_Leukoplakia_with_Exfoliative_Cytology_/1416943", "title"=>"Quantitative Risk Stratification of Oral Leukoplakia with Exfoliative Cytology", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-05-15 03:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2070416"], "description"=>"<p>Six models (SVM, RRF, PLR, NNET, KNN, and CART) were tested for their performance using three parameters, ROC, sensitivity and specificity. Each model was trained on the training data and tested on the testing data. Each boxplot showed the distribution of these three parameters (R caret package <a href=\"http://cran.r-project.org/web/packages/caret/index.html\" target=\"_blank\">http://cran.r-project.org/web/packages/caret/index.html</a>).</p>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416932, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126760.g004", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Assessment_of_statistical_models_/1416932", "title"=>"Assessment of statistical models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-15 03:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/2070413"], "description"=>"<p>Starting with DI values as the raw data, EdTAR first identified candidate peaks of cell populations. Diploid cell population was extracted and further filtered if more than one population is detected. The same procedure was applied to extract the tetraploid cell population and thus the aneuploid cell population was isolated. Data of these three cell populations were reconstructed across a wide rage [0–8] using the discrete density at each interval. The newly constructed data was used for training the statistical model and calculation of the Oral Cancer Risk Index (OCRI).</p>", "links"=>[], "tags"=>["risk stratification", "oscc", "data analysis procedure", "svm", "support vector machine", "cancer risk index", "method", "Quantitative Risk Stratification", "Exfoliative Cytology Exfoliative cytology", "exfoliative cytology data", "OCRI", "OLK patients", "model"], "article_id"=>1416929, "categories"=>["Biological Sciences"], "users"=>["Yao Liu", "Jianying Li", "Xiaoyong Liu", "Xudong Liu", "Waqaar Khawar", "Xinyan Zhang", "Fan Wang", "Xiaoxin Chen", "Zheng Sun"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126760.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Work_flow_of_e_xpert_guided_d_ata_t_ransformation_a_nd_r_econstruction_EdTAR_/1416929", "title"=>"Work flow of expert-guided data transformation and reconstruction (EdTAR).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-15 03:31:58"}

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