To Compare the Application Value of Different Deep Learning Models Based on CT in Predicting Visceral Pleural Invasion of Non-small Cell Lung Cancer: A Retrospective, Multicenter Study.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Tongde Hospital of Zhejiang Province Affiliated to Zhejiang Chinese Medical University (Tongde Hospital of Zhejiang Province), Hangzhou, Zhejiang Province, China (X.Z., C.Y., J.W.).
- Department of Radiology, Guangyuan Hospital of Traditional Chinese Medicine, Guangyuan, Sichuan Province, China (Y.Y.).
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, China (Z.X.).
- Department of Radiology, Anqing Municipal Hospital, Anqing, Anhui Province, China (H.S.).
- Jianpei Technology, Hangzhou, Zhejiang Province, China (H.J., L.H.).
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang Province, China (T.Y.).
- Department of Radiology, Tongde Hospital of Zhejiang Province Affiliated to Zhejiang Chinese Medical University (Tongde Hospital of Zhejiang Province), Hangzhou, Zhejiang Province, China (X.Z., C.Y., J.W.). Electronic address: [email protected].
Abstract
Visceral pleural invasion (VPI) indicates poor prognosis in non-small cell lung cancer (NSCLC), and upgrades T classification of NSCLC from T1 to T2 when accompanied by VPI. This study aimed to develop and validate deep learning models for the accurate prediction of VPI in patients with NSCLC, and to compare the performance of two-dimensional (2D), three-dimensional (3D), and hybrid 3D models. This retrospective study included consecutive patients with pathologically confirmed lung tumor between June 2017 and September 2022. The clinical data and preoperative imaging features of these patients were investigated and their relationships with VPI were statistically compared. Elastic fiber staining analysis results were the gold standard for diagnosis of VPI. The data of non-VPI and VPI patients were randomly divided into training cohort and validation cohort based on 8:2 and 6:4, respectively. The EfficientNet-B0_2D model and Double-head Res2Net/_F6/_F24 models were constructed, optimized and verified using two convolutional neural network model architectures-EfficientNet-B0 and Res2Net, respectively, by extracting the features of original CT images and combining specific clinical-CT features. The receiver operating characteristic curve, the area under the curve (AUC), and confusion matrix were utilized to assess the diagnostic efficiency of models. Delong test was used to compare performance between models. A total of 1931 patients with NSCLC were finally evaluated. By univariate analysis, 20 clinical-CT features were identified as risk predictors of VPI. Comparison of the diagnostic efficacy among the EfficientNet-b0_2D, Double-head Res2Net, Res2Net_F6, and Res2Net_F24 combined models revealed that Double-head Res2Net_F6 model owned the largest AUC of 0.941 among all models, followed by Double-head Res2Net (AUC=0.879), Double-head Res2Net_F24 (AUC=0.876), and EfficientNet-b0_2D (AUC=0.785). The three 3D-based models showed comparable predictive performance in the validation cohort and all outperformed the 2D model (EfficientNet-B0_2D, all P＜0.05). It is feasible to predict VPI in NSCLC with the predictive models based on deep learning, and the Double-head Res2Net_F6 model fused with six clinical-CT features showed greatest diagnostic efficacy.