Full
Outline
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).
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.
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.
Our 3D ResNet18 model shows the best predictive performance in identifying DR-TB, and is expected to assist clinical diagnosis for DR-TB.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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