A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks
The increase in diagnostic and therapeutic procedures in the treatment of oncological diseases, as well as the limited capacity of experts to provide information, necessitates the development of therapy decision support systems (TDSS). We have developed a treatment decision model that integrates ava...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/11/1/110 |
| _version_ | 1827628253181902848 |
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| author | Aisha Hikal Jan Gaebel Thomas Neumuth Andreas Dietz Matthaeus Stoehr |
| author_facet | Aisha Hikal Jan Gaebel Thomas Neumuth Andreas Dietz Matthaeus Stoehr |
| author_sort | Aisha Hikal |
| collection | DOAJ |
| description | The increase in diagnostic and therapeutic procedures in the treatment of oncological diseases, as well as the limited capacity of experts to provide information, necessitates the development of therapy decision support systems (TDSS). We have developed a treatment decision model that integrates available patient information as well as tumor characteristics. They are assessed according to their relevance in evaluating the optimal therapy option. Our treatment model is based on Bayesian networks (BN) which integrate patient-specific data with expert-based implemented causalities to suggest the optimal therapy option and therefore potentially support the decision-making process for treatment of laryngeal carcinoma. To test the reliability of our model, we compared the calculations of our model with the documented therapy from our data set, which contained information on 97 patients with laryngeal carcinoma. Information on 92 patients was used in our analyses and the model suggested the correct treatment in 419 out of 460 treatment modalities (accuracy of 91%). However, unequally distributed clinical data in the test sets revealed weak spots in the model that require revision for future utilization. |
| first_indexed | 2024-03-09T13:29:06Z |
| format | Article |
| id | doaj.art-48b41a5eef8c448bbc06a9689deb6172 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T13:29:06Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-48b41a5eef8c448bbc06a9689deb61722023-11-30T21:19:34ZengMDPI AGBiomedicines2227-90592023-01-0111111010.3390/biomedicines11010110A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian NetworksAisha Hikal0Jan Gaebel1Thomas Neumuth2Andreas Dietz3Matthaeus Stoehr4Head and Neck Surgery, Department of Otorhinolaryngology, University Hospital Leipzig, 04103 Leipzig, GermanyInnovation Center Computer Assisted Surgery (ICCAS), Faculty of Medicine, University Leipzig, 04103 Leipzig, GermanyInnovation Center Computer Assisted Surgery (ICCAS), Faculty of Medicine, University Leipzig, 04103 Leipzig, GermanyHead and Neck Surgery, Department of Otorhinolaryngology, University Hospital Leipzig, 04103 Leipzig, GermanyHead and Neck Surgery, Department of Otorhinolaryngology, University Hospital Leipzig, 04103 Leipzig, GermanyThe increase in diagnostic and therapeutic procedures in the treatment of oncological diseases, as well as the limited capacity of experts to provide information, necessitates the development of therapy decision support systems (TDSS). We have developed a treatment decision model that integrates available patient information as well as tumor characteristics. They are assessed according to their relevance in evaluating the optimal therapy option. Our treatment model is based on Bayesian networks (BN) which integrate patient-specific data with expert-based implemented causalities to suggest the optimal therapy option and therefore potentially support the decision-making process for treatment of laryngeal carcinoma. To test the reliability of our model, we compared the calculations of our model with the documented therapy from our data set, which contained information on 97 patients with laryngeal carcinoma. Information on 92 patients was used in our analyses and the model suggested the correct treatment in 419 out of 460 treatment modalities (accuracy of 91%). However, unequally distributed clinical data in the test sets revealed weak spots in the model that require revision for future utilization.https://www.mdpi.com/2227-9059/11/1/110therapy decision support system (TDSS)Bayesian networks (BN)tumor boardlaryngeal carcinoma |
| spellingShingle | Aisha Hikal Jan Gaebel Thomas Neumuth Andreas Dietz Matthaeus Stoehr A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks Biomedicines therapy decision support system (TDSS) Bayesian networks (BN) tumor board laryngeal carcinoma |
| title | A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks |
| title_full | A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks |
| title_fullStr | A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks |
| title_full_unstemmed | A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks |
| title_short | A Treatment Decision Support Model for Laryngeal Cancer Based on Bayesian Networks |
| title_sort | treatment decision support model for laryngeal cancer based on bayesian networks |
| topic | therapy decision support system (TDSS) Bayesian networks (BN) tumor board laryngeal carcinoma |
| url | https://www.mdpi.com/2227-9059/11/1/110 |
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