In BMC cancer
BACKGROUND : Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.
METHODS : Retrospectively enroll patients with pathologically-confirmed solid pulmonary nodules and collect clinical data. Obtain pre-treatment high-resolution thoracic CT and manually delineate the nodule in 3D. Then, all patients were randomly divided into training and testing sets at a ratio of 7:3, and convolutional neural networks (CNN) models and random forest (RF) models were established. Survival analyses were performed for patients with solid adenocarcinomas.
RESULTS : Totally 720 solid pulmonary nodules were enrolled, 348 benign and 372 malignant. The CNN model with clinical features achieved the highest AUC [0.819, 95% confidence interval (CI): 0.760-0.877] with a sensitivity of 0.778, specificity of 0.788 and accuracy of 0.783. No significant differences were observed between the CNN and radiomics models. There were 295 solid adenocarcinomas in survival analysis. Different disease-free survival was observed between the low-risk and high-risk groups divided according to the radiomics Rad-score. However, the groups based on deep learning signatures showed similar survival. Cox regression analysis indicated that the radiomics Rad-score (hazard ratio: 5.08, 95% CI: 2.61-9.90) was an independent predictor of recurrence.
CONCLUSIONS : The radiomics and deep learning models can well predict the malignancy of solid pulmonary nodules. Radiomics signatures also demonstrate prognostic value in solid adenocarcinomas.
Zhang Rui, Wei Ying, Shi Feng, Ren Jing, Zhou Qing, Li Weimin, Chen Bojiang
2022-Nov-01
Adenocarcinoma, Deep learning, Disease-free survival, Radiomics, Solid pulmonary nodules
