Publicação
Ontological representation of tumor-node-metastasis classification and an ontology-driven classifier: a study on colorectal cancer
| Resumo: | The most important staging system for cancer is the TNM Classification of Malignant Tumors (TNM) classification. The staging procedure compiles several clinical and pathological parameters based on the Extent of Disease (EOD). The objectives of this work are to present the Tumor-Nodes-Metastasis Ontology (TNM-O), a framework for the representation of the TNM classification of malignant tumors (TNM) system; to implement the TNM Colon and Rectum ontology, a modular ontology that represents the TNM classification for the colorectal tumors based on this framework; to develop an ontologically driven classifier application with the TNM-O as it’s knowledge base and to show the feasibility of this approach on real data. TNM Ontology (TNM-O) and TNM Colon and Rectum Ontology (TNMCRO) use the Foundational Model of Anatomy (FMA) for representing anatomical entities and BioTopLite2 (BTL2) as a domain top-level ontology. The classification rules of the TNM classification for colorectal tumors were represented as described in the literature. The automatic classifier for pathological data uses these ontologies as knowledge base. It was developed with JAVA using the Ontology Web Language (OWL)-application programming interface (API) to make the bridge between the application level and knowledge base. In this study, two datasets with real data where evaluated. The first dataset contained 382 entries that was classified by the regional lymph nodes. This study compared automatic classification with the expert one and obtained an accuracy of 55%. However, the classifier flagged inconsistencies and errors made during the manual tumor documentation that caused the misclassification. The second dataset contained 292 records carefully classified by a pathologist. In this dataset, automatic classification was optimal to all types of assessment. Therefore, this study proved that an ontology-driven automatic classifier enhances the consistency in tumor documentation and provides accurate instance classification during pathological assessment of tumors. |
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| Autores principais: | França, Fábio Humberto Pinto |
| Assunto: | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
| Ano: | 2015 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The most important staging system for cancer is the TNM Classification of Malignant Tumors (TNM) classification. The staging procedure compiles several clinical and pathological parameters based on the Extent of Disease (EOD). The objectives of this work are to present the Tumor-Nodes-Metastasis Ontology (TNM-O), a framework for the representation of the TNM classification of malignant tumors (TNM) system; to implement the TNM Colon and Rectum ontology, a modular ontology that represents the TNM classification for the colorectal tumors based on this framework; to develop an ontologically driven classifier application with the TNM-O as it’s knowledge base and to show the feasibility of this approach on real data. TNM Ontology (TNM-O) and TNM Colon and Rectum Ontology (TNMCRO) use the Foundational Model of Anatomy (FMA) for representing anatomical entities and BioTopLite2 (BTL2) as a domain top-level ontology. The classification rules of the TNM classification for colorectal tumors were represented as described in the literature. The automatic classifier for pathological data uses these ontologies as knowledge base. It was developed with JAVA using the Ontology Web Language (OWL)-application programming interface (API) to make the bridge between the application level and knowledge base. In this study, two datasets with real data where evaluated. The first dataset contained 382 entries that was classified by the regional lymph nodes. This study compared automatic classification with the expert one and obtained an accuracy of 55%. However, the classifier flagged inconsistencies and errors made during the manual tumor documentation that caused the misclassification. The second dataset contained 292 records carefully classified by a pathologist. In this dataset, automatic classification was optimal to all types of assessment. Therefore, this study proved that an ontology-driven automatic classifier enhances the consistency in tumor documentation and provides accurate instance classification during pathological assessment of tumors. |
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