The Experts below are selected from a list of 46035 Experts worldwide ranked by ideXlab platform
Henk C Hoogsteden - One of the best experts on this subject based on the ideXlab platform.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Joachim Aerts, Bianca Den Hamer, Michael Den A Bakker, Peter Riegman, John A Foekens, Henk C Hoogsteden, Wilfred F J Van Ijcken, Cor Van Der Leest, Peter J Van Der Spek, Frank GrosveldAbstract:textabstractBackground: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
Joseph H Schwab - One of the best experts on this subject based on the ideXlab platform.
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development of machine learning algorithms for Prediction of 5 year spinal chordoma Survival
World Neurosurgery, 2018Co-Authors: Aditya V Karhade, Quirina Thio, Paul T Ogink, Jason Kim, Santiago A Lozanocalderon, Kevin A Raskin, Joseph H SchwabAbstract:Background Chordomas are locally invasive slow-growing tumors that are difficult to study because of the rarity of the tumors and the lack of significant volumes of patients with longitudinal follow-up. As such, there are currently no machine learning studies in the chordoma literature. The purpose of this study was to develop machine learning models for Survival Prediction and deploy them as open access web applications as a proof of concept for machine learning in rare nervous system lesions. Methods The National Cancer Institute's Surveillance, Epidemiology, and End Results program database was used to identify adult patients diagnosed with spinal chordoma between 1995 and 2010. Four machine learning models were used to predict 5-year Survival for spinal chordoma and assessed by discrimination, calibration, and overall performance. Results The 5-year overall Survival for 265 patients with spinal chordoma was 67.5%. Variables used for Prediction were age at diagnosis, tumor size, tumor location, extent of tumor invasion, and extent of surgery. For 5-year Survival Prediction, the Bayes Point Machine achieved the best performance with a c statistic of 0.80, calibration slope of 1.01, calibration intercept of 0.03, and Brier score of 0.16. This model for 5-year mortality Prediction was incorporated into an open access application and can be found online ( https://sorg-apps.shinyapps.io/chordoma/ ). Conclusions This analysis of patients with spinal chordoma demonstrated that machine learning models can be developed for Survival Prediction in rare pathologies and have the potential to serve as the basis for creation of decision support tools in the future.
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can machine learning techniques be used for 5 year Survival Prediction of patients with chondrosarcoma
Clinical Orthopaedics and Related Research, 2018Co-Authors: Quirina C B S Thio, Aditya V Karhade, Paul T Ogink, Kevin A Raskin, Karen De Amorim Bernstein, Santiago Lozano A Calderon, Joseph H SchwabAbstract:AbstractBackgroundSeveral studies have identified prognostic factors for patients with chondrosarcoma, but there are few studies investigating the accuracy of computationally intensive methods such as machine learning. Machine learning is a type of artificial intelligence that enables computers to l
Michael Den A Bakker - One of the best experts on this subject based on the ideXlab platform.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Joachim Aerts, Bianca Den Hamer, Michael Den A Bakker, Peter Riegman, John A Foekens, Henk C Hoogsteden, Wilfred F J Van Ijcken, Cor Van Der Leest, Peter J Van Der Spek, Frank GrosveldAbstract:textabstractBackground: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
Bianca Den Hamer - One of the best experts on this subject based on the ideXlab platform.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Joachim Aerts, Bianca Den Hamer, Michael Den A Bakker, Peter Riegman, John A Foekens, Henk C Hoogsteden, Wilfred F J Van Ijcken, Cor Van Der Leest, Peter J Van Der Spek, Frank GrosveldAbstract:textabstractBackground: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
Peter Riegman - One of the best experts on this subject based on the ideXlab platform.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Joachim Aerts, Bianca Den Hamer, Michael Den A Bakker, Peter Riegman, John A Foekens, Henk C Hoogsteden, Wilfred F J Van Ijcken, Cor Van Der Leest, Peter J Van Der Spek, Frank GrosveldAbstract:textabstractBackground: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
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gene expression based classification of non small cell lung carcinomas and Survival Prediction
PLOS ONE, 2010Co-Authors: Jun Hou, Joachim G J V Aerts, Bianca Den Hamer, Wilfred F J Van Ijcken, Michael Den A Bakker, Peter Riegman, Cor Van Der Leest, Peter J Van Der Spek, John A Foekens, Henk C HoogstedenAbstract:Background: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery Survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier Survival curves show that the two groups display a significant difference in post-surgery Survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the Prediction of clinical outcome.
