Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis
Tuberculosis (TB) drug resistance is a worldwide public health problem. It decreases the likelihood of a positive outcome for the individual patient and increases the likelihood of disease spread. Therefore, early detection of TB drug resistance is crucial for improving outcomes and controlling dise...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10194254/ |
| _version_ | 1827865915261190144 |
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| author | Vy C. B. Bui Ziv Yaniv Michael Harris Feng Yang Karthik Kantipudi Darrell Hurt Alex Rosenthal Stefan Jaeger |
| author_facet | Vy C. B. Bui Ziv Yaniv Michael Harris Feng Yang Karthik Kantipudi Darrell Hurt Alex Rosenthal Stefan Jaeger |
| author_sort | Vy C. B. Bui |
| collection | DOAJ |
| description | Tuberculosis (TB) drug resistance is a worldwide public health problem. It decreases the likelihood of a positive outcome for the individual patient and increases the likelihood of disease spread. Therefore, early detection of TB drug resistance is crucial for improving outcomes and controlling disease transmission. While drug-sensitive tuberculosis cases are declining worldwide because of effective treatment, the threat of drug-resistant tuberculosis is growing, and the success rate of drug-resistant tuberculosis treatment is only around 60%. The TB Portals program provides a publicly accessible repository of TB case data with an emphasis on collecting drug-resistant cases. The dataset includes multi-modal information such as socioeconomic/geographic data, clinical characteristics, pathogen genomics, and radiological features. The program is an international collaboration whose participants are typically under a substantial burden of drug-resistant tuberculosis, with data collected from standard clinical care provided to the patients. Consequentially, the TB Portals dataset is heterogenous in nature, with data representing multiple treatment centers in different countries and containing cross-domain information. This study presents the challenges and methods used to address them when working with this real-world dataset. Our goal was to evaluate whether combining radiological features derived from a chest X-ray of the host and genomic features from the pathogen can potentially improve the identification of the drug susceptibility type, drug-sensitive (DS-TB) or drug-resistant (DR-TB), and the length of the first successful drug regimen. To perform these studies, significantly imbalanced data needed to be processed, which included a much larger number of DR-TB cases than DS-TB, many more cases with radiological findings than genomic ones, and the sparse high dimensional nature of the genomic information. Three evaluation studies were carried out. First, the DR-TB/DS-TB classification model achieved an average accuracy of 92.4% when using genomic features alone or when combining radiological and genomic features. Second, the regression model for the length of the first successful treatment had a relative error of 53.5% using radiological features, 25.6% using genomic features, and 22.0% using both radiological and genomic features. Finally, the relative error of the third regression model predicting the length of the first treatment using the most common drug combination varied depending on the feature type used. When using radiological features alone, the relative error was 17.8%. For geno- mic features alone, the relative error increased to 19.9%. The model had a relative error of 19.0% when both radiological and genomic features were combined. Although combining radiological and genomic features did not improve upon the use of genomic features when classifying DR-TB/DS-TB, the combination of the two feature types improved the relative error of the predictive model for the length of the first successful treatment. Furthermore, the regression model trained on radiological features achieved the best performance when predicting the treatment length of the most common drug combination. |
| first_indexed | 2024-03-12T14:56:04Z |
| format | Article |
| id | doaj.art-e31eb842ef9345a0963dbae58ffe2a59 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:56:04Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e31eb842ef9345a0963dbae58ffe2a592023-08-14T23:00:43ZengIEEEIEEE Access2169-35362023-01-0111842288424010.1109/ACCESS.2023.329875010194254Combining Radiological and Genomic TB Portals Data for Drug Resistance AnalysisVy C. B. Bui0https://orcid.org/0000-0001-5749-3756Ziv Yaniv1https://orcid.org/0000-0003-0315-7727Michael Harris2https://orcid.org/0000-0001-8789-0912Feng Yang3https://orcid.org/0000-0002-8334-7450Karthik Kantipudi4https://orcid.org/0000-0002-6423-1647Darrell Hurt5https://orcid.org/0000-0002-9829-8567Alex Rosenthal6https://orcid.org/0000-0003-4190-9045Stefan Jaeger7https://orcid.org/0000-0001-6877-4318Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USAOffice of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USAOffice of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USALister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USAOffice of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USAOffice of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USAOffice of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USALister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USATuberculosis (TB) drug resistance is a worldwide public health problem. It decreases the likelihood of a positive outcome for the individual patient and increases the likelihood of disease spread. Therefore, early detection of TB drug resistance is crucial for improving outcomes and controlling disease transmission. While drug-sensitive tuberculosis cases are declining worldwide because of effective treatment, the threat of drug-resistant tuberculosis is growing, and the success rate of drug-resistant tuberculosis treatment is only around 60%. The TB Portals program provides a publicly accessible repository of TB case data with an emphasis on collecting drug-resistant cases. The dataset includes multi-modal information such as socioeconomic/geographic data, clinical characteristics, pathogen genomics, and radiological features. The program is an international collaboration whose participants are typically under a substantial burden of drug-resistant tuberculosis, with data collected from standard clinical care provided to the patients. Consequentially, the TB Portals dataset is heterogenous in nature, with data representing multiple treatment centers in different countries and containing cross-domain information. This study presents the challenges and methods used to address them when working with this real-world dataset. Our goal was to evaluate whether combining radiological features derived from a chest X-ray of the host and genomic features from the pathogen can potentially improve the identification of the drug susceptibility type, drug-sensitive (DS-TB) or drug-resistant (DR-TB), and the length of the first successful drug regimen. To perform these studies, significantly imbalanced data needed to be processed, which included a much larger number of DR-TB cases than DS-TB, many more cases with radiological findings than genomic ones, and the sparse high dimensional nature of the genomic information. Three evaluation studies were carried out. First, the DR-TB/DS-TB classification model achieved an average accuracy of 92.4% when using genomic features alone or when combining radiological and genomic features. Second, the regression model for the length of the first successful treatment had a relative error of 53.5% using radiological features, 25.6% using genomic features, and 22.0% using both radiological and genomic features. Finally, the relative error of the third regression model predicting the length of the first treatment using the most common drug combination varied depending on the feature type used. When using radiological features alone, the relative error was 17.8%. For geno- mic features alone, the relative error increased to 19.9%. The model had a relative error of 19.0% when both radiological and genomic features were combined. Although combining radiological and genomic features did not improve upon the use of genomic features when classifying DR-TB/DS-TB, the combination of the two feature types improved the relative error of the predictive model for the length of the first successful treatment. Furthermore, the regression model trained on radiological features achieved the best performance when predicting the treatment length of the most common drug combination.https://ieeexplore.ieee.org/document/10194254/TuberculosisradiomicsgenomicsTB Portalsmachine learningdrug resistance |
| spellingShingle | Vy C. B. Bui Ziv Yaniv Michael Harris Feng Yang Karthik Kantipudi Darrell Hurt Alex Rosenthal Stefan Jaeger Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis IEEE Access Tuberculosis radiomics genomics TB Portals machine learning drug resistance |
| title | Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis |
| title_full | Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis |
| title_fullStr | Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis |
| title_full_unstemmed | Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis |
| title_short | Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis |
| title_sort | combining radiological and genomic tb portals data for drug resistance analysis |
| topic | Tuberculosis radiomics genomics TB Portals machine learning drug resistance |
| url | https://ieeexplore.ieee.org/document/10194254/ |
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