Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology
Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were i...
Ausführliche Beschreibung
Autor*in: |
Zhdanovich, Yauheniya [verfasserIn] |
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E-Artikel |
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Englisch |
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2023 |
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Anmerkung: |
© The Author(s) 2023 |
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Übergeordnetes Werk: |
Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 24(2023), 1 vom: 03. Jan. |
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Übergeordnetes Werk: |
volume:24 ; year:2023 ; number:1 ; day:03 ; month:01 |
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DOI / URN: |
10.1186/s12859-022-05124-9 |
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Katalog-ID: |
SPR05130760X |
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100 | 1 | |a Zhdanovich, Yauheniya |e verfasserin |4 aut | |
245 | 1 | 0 | |a Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology |
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520 | |a Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. | ||
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700 | 1 | |a Ackermann, Jörg |4 aut | |
700 | 1 | |a Wild, Peter J. |4 aut | |
700 | 1 | |a Köllermann, Jens |4 aut | |
700 | 1 | |a Bankov, Katrin |4 aut | |
700 | 1 | |a Döring, Claudia |4 aut | |
700 | 1 | |a Flinner, Nadine |4 aut | |
700 | 1 | |a Reis, Henning |4 aut | |
700 | 1 | |a Wenzel, Mike |4 aut | |
700 | 1 | |a Höh, Benedikt |4 aut | |
700 | 1 | |a Mandel, Philipp |4 aut | |
700 | 1 | |a Vogl, Thomas J. |4 aut | |
700 | 1 | |a Harter, Patrick |4 aut | |
700 | 1 | |a Filipski, Katharina |4 aut | |
700 | 1 | |a Koch, Ina |4 aut | |
700 | 1 | |a Bernatz, Simon |4 aut | |
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10.1186/s12859-022-05124-9 doi (DE-627)SPR05130760X (SPR)s12859-022-05124-9-e DE-627 ger DE-627 rakwb eng Zhdanovich, Yauheniya verfasserin aut Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. Prostate cancer (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Quantitative features (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ackermann, Jörg aut Wild, Peter J. aut Köllermann, Jens aut Bankov, Katrin aut Döring, Claudia aut Flinner, Nadine aut Reis, Henning aut Wenzel, Mike aut Höh, Benedikt aut Mandel, Philipp aut Vogl, Thomas J. aut Harter, Patrick aut Filipski, Katharina aut Koch, Ina aut Bernatz, Simon aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 24(2023), 1 vom: 03. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:24 year:2023 number:1 day:03 month:01 https://dx.doi.org/10.1186/s12859-022-05124-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 03 01 |
spelling |
10.1186/s12859-022-05124-9 doi (DE-627)SPR05130760X (SPR)s12859-022-05124-9-e DE-627 ger DE-627 rakwb eng Zhdanovich, Yauheniya verfasserin aut Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. Prostate cancer (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Quantitative features (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ackermann, Jörg aut Wild, Peter J. aut Köllermann, Jens aut Bankov, Katrin aut Döring, Claudia aut Flinner, Nadine aut Reis, Henning aut Wenzel, Mike aut Höh, Benedikt aut Mandel, Philipp aut Vogl, Thomas J. aut Harter, Patrick aut Filipski, Katharina aut Koch, Ina aut Bernatz, Simon aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 24(2023), 1 vom: 03. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:24 year:2023 number:1 day:03 month:01 https://dx.doi.org/10.1186/s12859-022-05124-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 03 01 |
allfields_unstemmed |
10.1186/s12859-022-05124-9 doi (DE-627)SPR05130760X (SPR)s12859-022-05124-9-e DE-627 ger DE-627 rakwb eng Zhdanovich, Yauheniya verfasserin aut Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. Prostate cancer (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Quantitative features (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ackermann, Jörg aut Wild, Peter J. aut Köllermann, Jens aut Bankov, Katrin aut Döring, Claudia aut Flinner, Nadine aut Reis, Henning aut Wenzel, Mike aut Höh, Benedikt aut Mandel, Philipp aut Vogl, Thomas J. aut Harter, Patrick aut Filipski, Katharina aut Koch, Ina aut Bernatz, Simon aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 24(2023), 1 vom: 03. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:24 year:2023 number:1 day:03 month:01 https://dx.doi.org/10.1186/s12859-022-05124-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 03 01 |
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10.1186/s12859-022-05124-9 doi (DE-627)SPR05130760X (SPR)s12859-022-05124-9-e DE-627 ger DE-627 rakwb eng Zhdanovich, Yauheniya verfasserin aut Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. Prostate cancer (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Quantitative features (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ackermann, Jörg aut Wild, Peter J. aut Köllermann, Jens aut Bankov, Katrin aut Döring, Claudia aut Flinner, Nadine aut Reis, Henning aut Wenzel, Mike aut Höh, Benedikt aut Mandel, Philipp aut Vogl, Thomas J. aut Harter, Patrick aut Filipski, Katharina aut Koch, Ina aut Bernatz, Simon aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 24(2023), 1 vom: 03. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:24 year:2023 number:1 day:03 month:01 https://dx.doi.org/10.1186/s12859-022-05124-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 03 01 |
allfieldsSound |
10.1186/s12859-022-05124-9 doi (DE-627)SPR05130760X (SPR)s12859-022-05124-9-e DE-627 ger DE-627 rakwb eng Zhdanovich, Yauheniya verfasserin aut Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. Prostate cancer (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Quantitative features (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ackermann, Jörg aut Wild, Peter J. aut Köllermann, Jens aut Bankov, Katrin aut Döring, Claudia aut Flinner, Nadine aut Reis, Henning aut Wenzel, Mike aut Höh, Benedikt aut Mandel, Philipp aut Vogl, Thomas J. aut Harter, Patrick aut Filipski, Katharina aut Koch, Ina aut Bernatz, Simon aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 24(2023), 1 vom: 03. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:24 year:2023 number:1 day:03 month:01 https://dx.doi.org/10.1186/s12859-022-05124-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 24 2023 1 03 01 |
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Zhdanovich, Yauheniya @@aut@@ Ackermann, Jörg @@aut@@ Wild, Peter J. @@aut@@ Köllermann, Jens @@aut@@ Bankov, Katrin @@aut@@ Döring, Claudia @@aut@@ Flinner, Nadine @@aut@@ Reis, Henning @@aut@@ Wenzel, Mike @@aut@@ Höh, Benedikt @@aut@@ Mandel, Philipp @@aut@@ Vogl, Thomas J. @@aut@@ Harter, Patrick @@aut@@ Filipski, Katharina @@aut@@ Koch, Ina @@aut@@ Bernatz, Simon @@aut@@ |
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Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. 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Zhdanovich, Yauheniya |
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Zhdanovich, Yauheniya misc Prostate cancer misc Prediction misc Quantitative features misc Statistical analysis misc Machine learning Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology |
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Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology Prostate cancer (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Quantitative features (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 |
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Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology |
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Zhdanovich, Yauheniya Ackermann, Jörg Wild, Peter J. Köllermann, Jens Bankov, Katrin Döring, Claudia Flinner, Nadine Reis, Henning Wenzel, Mike Höh, Benedikt Mandel, Philipp Vogl, Thomas J. Harter, Patrick Filipski, Katharina Koch, Ina Bernatz, Simon |
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evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology |
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Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology |
abstract |
Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. © The Author(s) 2023 |
abstractGer |
Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. © The Author(s) 2023 |
abstract_unstemmed |
Background Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine. © The Author(s) 2023 |
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Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology |
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Ackermann, Jörg Wild, Peter J. Köllermann, Jens Bankov, Katrin Döring, Claudia Flinner, Nadine Reis, Henning Wenzel, Mike Höh, Benedikt Mandel, Philipp Vogl, Thomas J. Harter, Patrick Filipski, Katharina Koch, Ina Bernatz, Simon |
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Ackermann, Jörg Wild, Peter J. Köllermann, Jens Bankov, Katrin Döring, Claudia Flinner, Nadine Reis, Henning Wenzel, Mike Höh, Benedikt Mandel, Philipp Vogl, Thomas J. Harter, Patrick Filipski, Katharina Koch, Ina Bernatz, Simon |
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Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. Methods Retrospectively, 106 prostate tissue samples from 48 patients (mean age, %$66\pm 6.6%$ years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. Results Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of %$0.93\pm 0.04%$, %$0.91\pm 0.06%$, and %$0.92\pm 0.05%$, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4. Conclusions Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. 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