Automated gleason grading on prostate biopsy slides by statistical representations of homology profile
•A new Statistical Representations of Homology Profile (SRHP) and its statistical representation was presented to capture the topological arrangement of nuclei with respect to the gland lumen.•SRHP approach could potentially serve as a decision support aid for discriminating G3 from G4 prostate canc...
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| Published in: | Computer methods and programs in biomedicine Vol. 194; p. 105528 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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| ISSN: | 0169-2607, 1872-7565, 1872-7565 |
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| Abstract | •A new Statistical Representations of Homology Profile (SRHP) and its statistical representation was presented to capture the topological arrangement of nuclei with respect to the gland lumen.•SRHP approach could potentially serve as a decision support aid for discriminating G3 from G4 prostate cancer grade.•SRHP are not just discriminating but also interpretable and intuitive.
Background and Objective:Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.
Methods:To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.
Results:On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.
Conclusions:We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading. |
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| AbstractList | Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.
To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.
On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.
We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading. •A new Statistical Representations of Homology Profile (SRHP) and its statistical representation was presented to capture the topological arrangement of nuclei with respect to the gland lumen.•SRHP approach could potentially serve as a decision support aid for discriminating G3 from G4 prostate cancer grade.•SRHP are not just discriminating but also interpretable and intuitive. Background and Objective:Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups. Methods:To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4. Results:On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images. Conclusions:We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading. Gleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.BACKGROUND AND OBJECTIVEGleason grading system is currently the clinical gold standard for determining prostate cancer aggressiveness. Prostate cancer is typically classified into one of 5 different categories with 1 representing the most indolent disease and 5 reflecting the most aggressive disease. Grades 3 and 4 are the most common and difficult patterns to be discriminated in clinical practice. Even though the degree of gland differentiation is the strongest determinant of Gleason grade, manual grading is subjective and is hampered by substantial inter-reader disagreement, especially with regard to intermediate grade groups.To capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.METHODSTo capture the topological characteristics and the degree of connectivity between nuclei around the gland, the concept of Homology Profile (HP) for prostate cancer grading is presented in this paper. HP is an algebraic tool, whereby, certain algebraic invariants are computed based on the structure of a topological space. We utilized the Statistical Representation of Homology Profile (SRHP) features to quantify the extent of glandular differentiation. The quantitative characteristics which represent the image patch are fed into a supervised classifier model for discrimination of grade patterns 3 and 4.On the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.RESULTSOn the basis of the novel homology profile, we evaluated 43 digitized images of prostate biopsy slides annotated for regions corresponding to Grades 3 and 4. The quantitative patch-level evaluation results showed that our approach achieved an Area Under Curve (AUC) of 0.96 and an accuracy of 0.89 in terms of discriminating Grade 3 and 4 patches. Our approach was found to be superior to comparative methods including handcrafted cellular features, Stacked Sparse Autoencoder (SSAE) algorithm and end-to-end supervised learning method (DLGg). Also, slide-level quantitative and qualitative evaluation results reflect the ability of our approach in discriminating Gleason Grade 3 from 4 patterns on H&E tissue images.We presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading.CONCLUSIONSWe presented a novel Statistical Representation of Homology Profile (SRHP) approach for automated Gleason grading on prostate biopsy slides. The most discriminating topological descriptions of cancerous regions for grade 3 and 4 in prostate cancer were identified. Moreover, these characteristics of homology profile are interpretable, visually meaningful and highly consistent with the rubric employed by pathologists for the task of Gleason grading. |
| ArticleNumber | 105528 |
| Author | Madabhushi, Anant Wang, Xiangxue Fu, Yao Fan, Xiangshan Xu, Jun Lu, Haoda Feldman, Michael D. Yan, Chaoyang Nakane, Kazuaki |
| AuthorAffiliation | a School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China d Dept. of Pathology, the affiliated Drum Tower Hospital, Nanjing University Medical School, 210008, China e Division of Surgical Pathology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA b Department of Molecular Pathology, Osaka University Graduate School of Medicine, Division of Health Science, Osaka 565-0871, Japan c Dept. of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA f Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH 44106 g Jiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China |
| AuthorAffiliation_xml | – name: a School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China – name: f Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH 44106 – name: c Dept. of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA – name: b Department of Molecular Pathology, Osaka University Graduate School of Medicine, Division of Health Science, Osaka 565-0871, Japan – name: d Dept. of Pathology, the affiliated Drum Tower Hospital, Nanjing University Medical School, 210008, China – name: e Division of Surgical Pathology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA – name: g Jiangsu Key Laboratory of Big Data Analysis Technique and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China |
| Author_xml | – sequence: 1 givenname: Chaoyang surname: Yan fullname: Yan, Chaoyang organization: School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China – sequence: 2 givenname: Kazuaki surname: Nakane fullname: Nakane, Kazuaki organization: Department of Molecular Pathology, Osaka University Graduate School of Medicine, Division of Health Science, Osaka 565–0871, Japan – sequence: 3 givenname: Xiangxue surname: Wang fullname: Wang, Xiangxue organization: Dept. of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA – sequence: 4 givenname: Yao surname: Fu fullname: Fu, Yao organization: Dept. of Pathology, the affiliated Drum Tower Hospital, Nanjing University Medical School, 210008, China – sequence: 5 givenname: Haoda surname: Lu fullname: Lu, Haoda organization: School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China – sequence: 6 givenname: Xiangshan surname: Fan fullname: Fan, Xiangshan organization: Dept. of Pathology, the affiliated Drum Tower Hospital, Nanjing University Medical School, 210008, China – sequence: 7 givenname: Michael D. surname: Feldman fullname: Feldman, Michael D. organization: Division of Surgical Pathology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA – sequence: 8 givenname: Anant surname: Madabhushi fullname: Madabhushi, Anant organization: Dept. of Biomedical Engineering, Case Western Reserve University, OH 44106-7207, USA – sequence: 9 givenname: Jun surname: Xu fullname: Xu, Jun email: jxx108@case.edu organization: School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32470903$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1002_ima_70092 crossref_primary_10_1016_j_labinv_2024_102060 crossref_primary_10_1007_s10916_024_02118_3 crossref_primary_10_1038_s41598_023_46213_w crossref_primary_10_1002_INMD_20240037 crossref_primary_10_1155_2022_7966553 crossref_primary_10_3390_cancers13061192 crossref_primary_10_1109_ACCESS_2020_3008868 crossref_primary_10_1038_s41374_021_00579_5 crossref_primary_10_1007_s42979_023_02546_x crossref_primary_10_1159_000546578 crossref_primary_10_1016_j_jpi_2023_100357 crossref_primary_10_3390_diagnostics12051149 |
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| Keywords | Homology Profile Gleason grading Prostate cancer Digitized needle biopsy samples Statistical representation |
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| Title | Automated gleason grading on prostate biopsy slides by statistical representations of homology profile |
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