CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance
Objective To develop and validate a deep learning model based on chest CT images to accurately distinguish between drug-resistant (DR-TB) and -sensitive tuberculosis (DS-TB). Methods A retrospective study was conducted on 722 cases of confirmed secondary tuberculosis admitted in our center from...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Army Medical University
2025-07-01
|
| Series: | 陆军军医大学学报 |
| Subjects: | |
| Online Access: | https://aammt.tmmu.edu.cn/html/202504117.html |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850078030455635968 |
|---|---|
| author | LI Chunhua LIU Xueyan ZHENG Jiaofeng |
| author_facet | LI Chunhua LIU Xueyan ZHENG Jiaofeng |
| author_sort | LI Chunhua |
| collection | DOAJ |
| description | Objective To develop and validate a deep learning model based on chest CT images to accurately distinguish between drug-resistant (DR-TB) and -sensitive tuberculosis (DS-TB). Methods A retrospective study was conducted on 722 cases of confirmed secondary tuberculosis admitted in our center from January 2019 to December 2022. According to the results of antimicrobial susceptibility test, they were divided into 357 DS-TB cases and 365 DR-TB cases. Pre-existing U-Net segmentation model was employed to segment the lung parenchyma regions in CT images. The dataset was randomly partitioned into a training set and a testing set in an 8:2 ratio. Six 3D deep learning architectures (3D Swin Transformer, 3D ShuffleNet v2, 3D ViT, 3D MobileNet v2, 3D DenseNet, and 3D ResNet18) were employed to evaluate the discriminative efficiency between DS-TB and DR-TB. Hyperparameters were optimized by five-fold cross-validation on the training set to construct the optimal model. The performance of the constructed model was assessed using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Six radiologists independently evaluated DR-TB identification on the test set, and their performance was compared with the best-performing deep learning model. Results The AUC value in DR-TB prediction was 0.583, 0.704, 0.698, 0.758, 0.736, and 0.841, respectively, for 3D Swin Transformer, 3D ShuffleNet v2, 3D ViT, 3D MobileNet v2, 3D DenseNet, and 3D ResNet18. The 3D ResNet18 model demonstrated optimal performance, achieving a sensitivity of 0.935 (95%CI: 0.880~0.987), a specificity of 0.642 (95%CI: 0.492~0.757), a PPV of 0.750 (95%CI: 0.663~0.835), an NPV of 0.896 (95%CI: 0.809~0.976), an AUC value of 0.841, and a F1-score of 0.832. The radiologists got a F1-score of 0.571, 0.450, 0.675, 0.623, 0.617 and 0.635, respectively, and the F1-score of the 3D ResNet18 model is all higher than that of the radiologists. The highest-performing radiologist achieved sensitivity, specificity, PPV and NPV of 0.701 (95%CI: 0.605~0.802), 0.567 (95%CI: 0.447~0.684), 0.651 (95%CI: 0.549~0.757), and 0.623 (95%CI: 0.500~0.754), with all these values lower than those of the 3D ResNet18 model (P<0.05). Class activation mapping showed that the 3D ResNet18 model could focus on key lesion areas. The class activation mapping demonstrated that the 3D ResNet18 model could effectively focus on critical lesion regions. Conclusion Our 3D ResNet18 model shows the best predictive performance in identifying DR-TB, and is expected to assist clinical diagnosis for DR-TB.
|
| format | Article |
| id | doaj-art-4f0a759ac2aa4f0c9f38c1dddb855cab |
| institution | DOAJ |
| issn | 2097-0927 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Journal of Army Medical University |
| record_format | Article |
| series | 陆军军医大学学报 |
| spelling | doaj-art-4f0a759ac2aa4f0c9f38c1dddb855cab2025-08-20T02:45:41ZzhoEditorial Office of Journal of Army Medical University陆军军医大学学报2097-09272025-07-0147141676168410.16016/j.2097-0927.202504117CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistanceLI Chunhua0LIU Xueyan1ZHENG Jiaofeng2Department of Medical Imaging， Chongqing Public Health Medical Center， Chongqing， ChinaDepartment of Medical Imaging， Chongqing Public Health Medical Center， Chongqing， ChinaDepartment of Medical Imaging， Chongqing Public Health Medical Center， Chongqing， ChinaObjective To develop and validate a deep learning model based on chest CT images to accurately distinguish between drug-resistant (DR-TB) and -sensitive tuberculosis (DS-TB). Methods A retrospective study was conducted on 722 cases of confirmed secondary tuberculosis admitted in our center from January 2019 to December 2022. According to the results of antimicrobial susceptibility test, they were divided into 357 DS-TB cases and 365 DR-TB cases. Pre-existing U-Net segmentation model was employed to segment the lung parenchyma regions in CT images. The dataset was randomly partitioned into a training set and a testing set in an 8:2 ratio. Six 3D deep learning architectures (3D Swin Transformer, 3D ShuffleNet v2, 3D ViT, 3D MobileNet v2, 3D DenseNet, and 3D ResNet18) were employed to evaluate the discriminative efficiency between DS-TB and DR-TB. Hyperparameters were optimized by five-fold cross-validation on the training set to construct the optimal model. The performance of the constructed model was assessed using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Six radiologists independently evaluated DR-TB identification on the test set, and their performance was compared with the best-performing deep learning model. Results The AUC value in DR-TB prediction was 0.583, 0.704, 0.698, 0.758, 0.736, and 0.841, respectively, for 3D Swin Transformer, 3D ShuffleNet v2, 3D ViT, 3D MobileNet v2, 3D DenseNet, and 3D ResNet18. The 3D ResNet18 model demonstrated optimal performance, achieving a sensitivity of 0.935 (95%CI: 0.880~0.987), a specificity of 0.642 (95%CI: 0.492~0.757), a PPV of 0.750 (95%CI: 0.663~0.835), an NPV of 0.896 (95%CI: 0.809~0.976), an AUC value of 0.841, and a F1-score of 0.832. The radiologists got a F1-score of 0.571, 0.450, 0.675, 0.623, 0.617 and 0.635, respectively, and the F1-score of the 3D ResNet18 model is all higher than that of the radiologists. The highest-performing radiologist achieved sensitivity, specificity, PPV and NPV of 0.701 (95%CI: 0.605~0.802), 0.567 (95%CI: 0.447~0.684), 0.651 (95%CI: 0.549~0.757), and 0.623 (95%CI: 0.500~0.754), with all these values lower than those of the 3D ResNet18 model (P<0.05). Class activation mapping showed that the 3D ResNet18 model could focus on key lesion areas. The class activation mapping demonstrated that the 3D ResNet18 model could effectively focus on critical lesion regions. Conclusion Our 3D ResNet18 model shows the best predictive performance in identifying DR-TB, and is expected to assist clinical diagnosis for DR-TB. https://aammt.tmmu.edu.cn/html/202504117.htmldeep learningtuberculosisdrug resistancedrug susceptibility |
| spellingShingle | LI Chunhua LIU Xueyan ZHENG Jiaofeng CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance 陆军军医大学学报 deep learning tuberculosis drug resistance drug susceptibility |
| title | CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance |
| title_full | CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance |
| title_fullStr | CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance |
| title_full_unstemmed | CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance |
| title_short | CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance |
| title_sort | cpredictive performance of ct images based 3d resnet18 model for identifying lung tuberculosis drug resistance |
| topic | deep learning tuberculosis drug resistance drug susceptibility |
| url | https://aammt.tmmu.edu.cn/html/202504117.html |
| work_keys_str_mv | AT lichunhua cpredictiveperformanceofctimagesbased3dresnet18modelforidentifyinglungtuberculosisdrugresistance AT liuxueyan cpredictiveperformanceofctimagesbased3dresnet18modelforidentifyinglungtuberculosisdrugresistance AT zhengjiaofeng cpredictiveperformanceofctimagesbased3dresnet18modelforidentifyinglungtuberculosisdrugresistance |