Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients
Summary: Promptly identification of multidrug-resistant tuberculosis (MDR-TB) patients at high risk of treatment failure is essential for improving cure rates. This study aimed to develop a habitat radiomics based transformer fusion model to assess treatment effectiveness of MDR-TB. Independent pati...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225010041 |
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| author | Xinna Lv Yichuan Wang Chenyu Ding Lixin Qin Xiaoyue Xu Ye Li Dailun Hou |
| author_facet | Xinna Lv Yichuan Wang Chenyu Ding Lixin Qin Xiaoyue Xu Ye Li Dailun Hou |
| author_sort | Xinna Lv |
| collection | DOAJ |
| description | Summary: Promptly identification of multidrug-resistant tuberculosis (MDR-TB) patients at high risk of treatment failure is essential for improving cure rates. This study aimed to develop a habitat radiomics based transformer fusion model to assess treatment effectiveness of MDR-TB. Independent patient cohorts from two hospitals were included. Radiomics features were extracted from the habitat and peripheral regions of cavities to construct predictive models. Then, a transformer-based fusion model integrating features from all regions was established. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance. The transformer fusion model combining two subregions and peripheral area achieved remarkable performance, with AUC values of 1.000, 0.959, and 0.879 in the training, validation, and test cohort, respectively. The finding highlights the efficacy of our model in predicting treatment effectiveness of MDR-TB patients and its potential to guide individualized therapy. |
| format | Article |
| id | doaj-art-756fd857c55041eab3c5ee2d6f9309ae |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-756fd857c55041eab3c5ee2d6f9309ae2025-08-20T03:11:26ZengElsevieriScience2589-00422025-06-0128611274310.1016/j.isci.2025.112743Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patientsXinna Lv0Yichuan Wang1Chenyu Ding2Lixin Qin3Xiaoyue Xu4Ye Li5Dailun Hou6Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, ChinaDepartment of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Radiology, Wuhan Pulmonary Hospital, Wuhan, ChinaDepartment of Radiology, Wuhan Pulmonary Hospital, Wuhan, ChinaDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Jinan, China; Corresponding authorDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China; Corresponding authorSummary: Promptly identification of multidrug-resistant tuberculosis (MDR-TB) patients at high risk of treatment failure is essential for improving cure rates. This study aimed to develop a habitat radiomics based transformer fusion model to assess treatment effectiveness of MDR-TB. Independent patient cohorts from two hospitals were included. Radiomics features were extracted from the habitat and peripheral regions of cavities to construct predictive models. Then, a transformer-based fusion model integrating features from all regions was established. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance. The transformer fusion model combining two subregions and peripheral area achieved remarkable performance, with AUC values of 1.000, 0.959, and 0.879 in the training, validation, and test cohort, respectively. The finding highlights the efficacy of our model in predicting treatment effectiveness of MDR-TB patients and its potential to guide individualized therapy.http://www.sciencedirect.com/science/article/pii/S2589004225010041DiseaseArtificial intelligence applications |
| spellingShingle | Xinna Lv Yichuan Wang Chenyu Ding Lixin Qin Xiaoyue Xu Ye Li Dailun Hou Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients iScience Disease Artificial intelligence applications |
| title | Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients |
| title_full | Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients |
| title_fullStr | Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients |
| title_full_unstemmed | Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients |
| title_short | Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients |
| title_sort | habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary mdr tb patients |
| topic | Disease Artificial intelligence applications |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225010041 |
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