Predicting N2 lymph node metastasis in presurgical stage I‐II non‐small cell lung cancer using multiview radiomics and deep learning method. Issue 4 (6th January 2023)

• Record Type: Journal Article
• Authors: Zhang, Hanfei; Liao, Meiyan; Guo, Qun; Chen, Jun; Wang, Shan; Liu, Songmei; Xiao, Feng
• Is Part Of: Medical physics. Volume 50:Issue 4(2023)
• Page Start: 2049
• Page End: 2060
• Publication Date: 2023-01-06
• Subjects: CT -- deep learning -- nomogram -- lymph node metastasis -- non‐small cell lung cancer -- radiomic; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16177
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Accurate diagnosis of N2 lymph node status of the resectable stage I‐II non‐small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically. Purpose: To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I‐II NSCLC using multiview radiomics and deep learning method. Methods: In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum‐redundancy maximum‐relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end‐to‐end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer–Lemeshow test, and decision curves, respectively. Results: Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant ( p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use. Conclusion: This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I‐II NSCLC.