Combining COPD with Clinical, Pathological and Demographic Information Refines Prognosis and Treatment Response Prediction of Non-Small Cell Lung Cancer
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{"title"=>"Combining COPD with clinical, pathological and demographic information refines prognosis and treatment response prediction of non-small cell lung cancer", "type"=>"journal", "authors"=>[{"first_name"=>"Joseph", "last_name"=>"Putila", "scopus_author_id"=>"41461772800"}, {"first_name"=>"Nancy Lan", "last_name"=>"Guo", "scopus_author_id"=>"23094473400"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pmid"=>"24967586", "scopus"=>"2-s2.0-84903376992", "pui"=>"373399450", "doi"=>"10.1371/journal.pone.0100994", "sgr"=>"84903376992"}, "id"=>"e844f8f6-d6a1-3fa7-b640-34cf6a902535", "abstract"=>"BACKGROUND: Accurate assessment of a patient's risk of recurrence and treatment response is an important prerequisite of personalized therapy in lung cancer. This study extends a previously described non-small cell lung cancer prognostic model by the addition of chemotherapy and co-morbidities through the use of linked SEER-Medicare data.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: Data on 34,203 lung adenocarcinoma and 26,967 squamous cell lung carcinoma patients were used to determine the contribution of Chronic Obstructive Pulmonary Disease (COPD) to prognostication in 30 treatment combinations. A Cox model including COPD was estimated on 1,000 bootstrap samples, with the resulting model assessed on ROC, Brier Score, Harrell's C, and Nagelkerke's R2 metrics in order to evaluate improvements in prognostication over a model without COPD. The addition of COPD to the model incorporating cancer stage, age, gender, race, and tumor grade was shown to improve prognostication in multiple patient groups. For lung adenocarcinoma patients, there was an improvement on the prognostication in the overall patient population and in patients without receiving chemotherapy, including those receiving surgery only. For squamous cell carcinoma, an improvement on prognostication was seen in both the overall patient population and in patients receiving multiple types of chemotherapy. COPD condition was able to stratify patients receiving the same treatments into significantly (log-rank p<0.05) different prognostic groups, independent of cancer stage.\\n\\nCONCLUSION/SIGNIFICANCE: Combining patient information on COPD, cancer stage, age, gender, race, and tumor grade could improve prognostication and prediction of treatment response in individual non-small cell lung cancer patients. This model enables refined prognosis and estimation of clinical outcome of comprehensive treatment regimens, providing a useful tool for personalized clinical decision-making.", "link"=>"http://www.mendeley.com/research/combining-copd-clinical-pathological-demographic-information-refines-prognosis-treatment-response-pr", "reader_count"=>16, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>4, "Lecturer"=>1, "Librarian"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>3, "Student > Ph. D. Student"=>2, "Student > Postgraduate"=>1, "Other"=>3, "Student > Master"=>4, "Lecturer"=>1, "Librarian"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Medicine and Dentistry"=>10, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Psychology"=>1, "Social Sciences"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>10}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United Kingdom"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1569019"], "description"=>"<p>Log-rank tests were used to assess the difference in survival probabilities of two groups.</p>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "patients", "copd", "treated"], "article_id"=>1085881, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Kaplan_Meier_analysis_of_patients_with_and_without_COPD_among_those_treated_with_surgery_alone_/1085881", "title"=>"Kaplan-Meier analysis of patients with and without COPD among those treated with surgery alone.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-26 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1569021"], "description"=>"<p>Patient survival at 60 months for the total population sample is shown for the range of Hazard Scores (left), with the risk-groups delimited by vertical bars. Model coefficients used to determine the Hazard Score for each patient are shown on the forest plot (right). A: AJCC 3<sup>rd</sup> Staging Edition; B: AJCC 6<sup>th</sup> Staging Edition; C: AJCC 7<sup>th</sup> Staging Edition.</p>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "prognostic"], "article_id"=>1085883, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comprehensive_prognostic_model_for_lung_adenocarcinoma_/1085883", "title"=>"Comprehensive prognostic model for lung adenocarcinoma.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-26 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1569022"], "description"=>"<p>Model coefficients used to determine the Hazard Score for each patient are shown on the forest plot (right). Patient survival at 24 months for the total population sample is shown for the range of Hazard Scores (left), with the risk-groups delimited by vertical bars. A: AJCC 3<sup>rd</sup> Staging Edition; B: AJCC 6<sup>th</sup> Staging Edition; C: AJCC 7<sup>th</sup> Staging Edition.</p>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "squamous"], "article_id"=>1085884, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comprehensive_model_for_squamous_cell_lung_carcinoma_/1085884", "title"=>"Comprehensive model for squamous cell lung carcinoma.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-26 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1569025"], "description"=>"<p>Given the patient information submitted by the user (left), the web-based tool will estimate survival for each treatment category using the survival observed for patients of a particular treatment modality and similar Hazard Score (right).</p>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "web-based", "prognostic"], "article_id"=>1085887, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_example_of_output_from_the_web_based_version_of_the_comprehensive_prognostic_model_/1085887", "title"=>"An example of output from the web-based version of the comprehensive prognostic model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-06-26 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1569026"], "description"=>"<p>These patients were diagnosed with AJCC 3<sup>rd</sup> staging edition. * indicates statistical significance at a <i>P</i><0.05 level.</p>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "survived", "groups", "restricted", "patients"], "article_id"=>1085888, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_patient_information_in_good_prognosis_those_survived_more_than_5_years_and_poor_prognosis_those_survived_less_than_2_years_groups_when_the_patient_sample_was_restricted_to_patients_receiving_surgery_without_radiation_or_chemotherapy_/1085888", "title"=>"Comparison of patient information in good prognosis (those survived more than 5 years) and poor prognosis (those survived less than 2 years) groups when the patient sample was restricted to patients receiving surgery without radiation or chemotherapy.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-26 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1569027"], "description"=>"<p>* indicates statistical significance at a <i>P</i><0.05 level.</p>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "comorbid", "nsclc", "restricted", "patients"], "article_id"=>1085889, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Association_of_each_comorbid_condition_and_NSCLC_survival_when_the_patient_sample_was_restricted_to_patients_receiving_surgery_without_radiation_or_chemotherapy_/1085889", "title"=>"Association of each comorbid condition and NSCLC survival when the patient sample was restricted to patients receiving surgery without radiation or chemotherapy.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-26 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1569028"], "description"=>"<p>Distribution of demographic and clinical characteristics of patients diagnosed with adenocarcinoma or squamous cell carcinoma in the original AJCC 3<sup>rd</sup> and 6<sup>th</sup> staging editions and recoded 7<sup>th</sup> edition.</p>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "patients", "diagnosed", "adenocarcinoma", "squamous", "carcinoma", "ajcc", "staging", "editions", "recoded"], "article_id"=>1085890, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_demographic_and_clinical_characteristics_of_patients_diagnosed_with_adenocarcinoma_or_squamous_cell_carcinoma_in_the_original_AJCC_3_rd_and_6_th_staging_editions_and_recoded_7_th_edition_/1085890", "title"=>"Distribution of demographic and clinical characteristics of patients diagnosed with adenocarcinoma or squamous cell carcinoma in the original AJCC 3<sup>rd</sup> and 6<sup>th</sup> staging editions and recoded 7<sup>th</sup> edition.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-06-26 03:28:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/1569033", "https://ndownloader.figshare.com/files/1569034", "https://ndownloader.figshare.com/files/1569035", "https://ndownloader.figshare.com/files/1569036", "https://ndownloader.figshare.com/files/1569037", "https://ndownloader.figshare.com/files/1569038", "https://ndownloader.figshare.com/files/1569039"], "description"=>"<div><p>Background</p><p>Accurate assessment of a patient’s risk of recurrence and treatment response is an important prerequisite of personalized therapy in lung cancer. This study extends a previously described non-small cell lung cancer prognostic model by the addition of chemotherapy and co-morbidities through the use of linked SEER-Medicare data.</p><p>Methodology/Principal Findings</p><p>Data on 34,203 lung adenocarcinoma and 26,967 squamous cell lung carcinoma patients were used to determine the contribution of Chronic Obstructive Pulmonary Disease (COPD) to prognostication in 30 treatment combinations. A Cox model including COPD was estimated on 1,000 bootstrap samples, with the resulting model assessed on ROC, Brier Score, Harrell’s C, and Nagelkerke’s R<sup>2</sup> metrics in order to evaluate improvements in prognostication over a model without COPD. The addition of COPD to the model incorporating cancer stage, age, gender, race, and tumor grade was shown to improve prognostication in multiple patient groups. For lung adenocarcinoma patients, there was an improvement on the prognostication in the overall patient population and in patients without receiving chemotherapy, including those receiving surgery only. For squamous cell carcinoma, an improvement on prognostication was seen in both the overall patient population and in patients receiving multiple types of chemotherapy. COPD condition was able to stratify patients receiving the same treatments into significantly (log-rank <i>p</i><0.05) different prognostic groups, independent of cancer stage.</p><p>Conclusion/Significance</p><p>Combining patient information on COPD, cancer stage, age, gender, race, and tumor grade could improve prognostication and prediction of treatment response in individual non-small cell lung cancer patients. This model enables refined prognosis and estimation of clinical outcome of comprehensive treatment regimens, providing a useful tool for personalized clinical decision-making.</p></div>", "links"=>[], "tags"=>["Diagnostic medicine", "prognosis", "epidemiology", "health care", "copd", "pathological", "refines", "non-small"], "article_id"=>1085895, "categories"=>["Biological Sciences"], "users"=>["Joseph Putila", "Nancy Lan Guo"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0100994.s001", "https://dx.doi.org/10.1371/journal.pone.0100994.s002", "https://dx.doi.org/10.1371/journal.pone.0100994.s003", "https://dx.doi.org/10.1371/journal.pone.0100994.s004", "https://dx.doi.org/10.1371/journal.pone.0100994.s005", "https://dx.doi.org/10.1371/journal.pone.0100994.s006", "https://dx.doi.org/10.1371/journal.pone.0100994.s007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Combining_COPD_with_Clinical_Pathological_and_Demographic_Information_Refines_Prognosis_and_Treatment_Response_Prediction_of_Non_Small_Cell_Lung_Cancer_/1085895", "title"=>"Combining COPD with Clinical, Pathological and Demographic Information Refines Prognosis and Treatment Response Prediction of Non-Small Cell Lung Cancer", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-06-26 03:28:50"}

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Relative Metric

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