An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue
Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopatho...
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| Published in | Cancers Vol. 13; no. 8; p. 1784 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
08.04.2021
MDPI |
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| Online Access | Get full text |
| ISSN | 2072-6694 2072-6694 |
| DOI | 10.3390/cancers13081784 |
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| Abstract | Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC. |
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| AbstractList | Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC. Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC.Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC. Simple SummaryAn established dataset of histopathology images obtained by biopsy and reviewed by two pathologists is used to create a two-stage oral squamous cell carcinoma diagnostic AI-based system. In the first stage, automated multiclass grading of OSCC is performed to improve the objectivity and reproducibility of histopathological examination. Furthermore, in the second stage, semantic segmentation of OSCC on epithelial and stromal tissue is performed in order to assist the clinician in discovering new informative features. Proposed AI-system based on deep convolutional neural networks and preprocessing methods achieved satisfactory results in terms of multiclass grading and segmenting. This research is the first step in analysing the tumor microenvironment, i.e., tumor-stroma ratio and segmentation of the microenvironment cells.AbstractOral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC. |
| Author | Dekanić, Andrea Ćabov, Tomislav Musulin, Jelena Štifanić, Daniel Car, Zlatan Zulijani, Ana |
| AuthorAffiliation | 3 Faculty of Dental Medicine, University of Rijeka, Krešimirova Ul. 40, 51000 Rijeka, Croatia 2 Department of Oral Surgery, Clinical Hospital Center Rijeka, Krešimirova Ul. 40, 51000 Rijeka, Croatia; ana.zulijani@sz.uniri.hr 4 Department of Pathology and Cytology, Clinical Hospital Center Rijeka, Krešimirova Ul. 42, 51000 Rijeka, Croatia; andrea.dekanic@medri.uniri.hr 5 Faculty of Medicine, University of Rijeka, Ul. Braće Branchetta 20/1, 51000 Rijeka, Croatia 1 Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; jmusulin@riteh.hr (J.M.); car@riteh.hr (Z.C.) |
| AuthorAffiliation_xml | – name: 4 Department of Pathology and Cytology, Clinical Hospital Center Rijeka, Krešimirova Ul. 42, 51000 Rijeka, Croatia; andrea.dekanic@medri.uniri.hr – name: 3 Faculty of Dental Medicine, University of Rijeka, Krešimirova Ul. 40, 51000 Rijeka, Croatia – name: 5 Faculty of Medicine, University of Rijeka, Ul. Braće Branchetta 20/1, 51000 Rijeka, Croatia – name: 1 Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; jmusulin@riteh.hr (J.M.); car@riteh.hr (Z.C.) – name: 2 Department of Oral Surgery, Clinical Hospital Center Rijeka, Krešimirova Ul. 40, 51000 Rijeka, Croatia; ana.zulijani@sz.uniri.hr |
| Author_xml | – sequence: 1 givenname: Jelena orcidid: 0000-0002-5213-1550 surname: Musulin fullname: Musulin, Jelena – sequence: 2 givenname: Daniel orcidid: 0000-0001-9396-2441 surname: Štifanić fullname: Štifanić, Daniel – sequence: 3 givenname: Ana orcidid: 0000-0003-2618-2263 surname: Zulijani fullname: Zulijani, Ana – sequence: 4 givenname: Tomislav orcidid: 0000-0002-8872-2811 surname: Ćabov fullname: Ćabov, Tomislav – sequence: 5 givenname: Andrea orcidid: 0000-0003-1723-6354 surname: Dekanić fullname: Dekanić, Andrea – sequence: 6 givenname: Zlatan orcidid: 0000-0003-2817-9252 surname: Car fullname: Car, Zlatan |
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| Snippet | Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to... Simple SummaryAn established dataset of histopathology images obtained by biopsy and reviewed by two pathologists is used to create a two-stage oral squamous... |
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| SubjectTerms | Accuracy Algorithms Artificial intelligence Automation Biopsy Classification Computer applications Datasets Deep learning Diagnosis Esophageal cancer Head & neck cancer Hospitals Lung cancer Medical prognosis Metastasis Neoplasia Neural networks Oral cancer Oral squamous cell carcinoma Patients Reproducibility Segmentation Semantics Squamous cell carcinoma Stroma Throat cancer Tumor microenvironment Tumors Wavelet transforms |
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| Title | An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue |
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