Full
Outline
To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC).
This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals (January 2013 to October 2023). Patients were divided into: a training set (n=502) and internal validation set (n=217) via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University; external validation set 1 (n=173) from General Hospital of Eastern Theater Command; external validation set 2 (n=178) from Daping Hospital of Army Medical University. Deep learning features were extracted using a 2.5D convolutional neural network (CNN) with ResNet101 backbone, radiomics features were derived from CT images, and clinical risk factors were identified to construct models. An integrated model combined deep learning, clinical, and radiomics features. All four models were developed using random forest (RF) classifiers. Calibration curves assessed goodness-of-fit, and decision curve analysis (DCA) evaluated clinical utility.
The deep learning model achieved AUCs of 0.833 7 (95%CI: 0.770 6~0.884 7), 0.815 1 (0.741 6~0.882 8), and 0.810 1 (0.745 2~0.873 6) in the internal and two external validation sets, respectively. Clinical models yielded AUCs of 0.731 0 (0.660 2~0.802 1), 0.746 0 (0.666 4~0.824 9), and 0.813 4 (0.743 1~0.883 6); radiomics models showed AUCs of 0.762 4 (0.692 4~0.825 1), 0.745 4 (0.671 1~0.814 3), and 0.724 7 (0.639 7~0.801 6). The integrated model demonstrated optimal performance with AUCs of 0.905 5 (0.857 0~0.945 4), 0.832 7 (0.763 3~0.896 4), and 0.889 0 (0.834 4~0.934 3). DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15~0.85 using the integrated model.
Deep learning features from CT images effectively predict EGFR mutation status in NSCLC. The integrated model combining deep learning, clinical, and radiomics features further enhances predictive performance.
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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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