Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer

Abstract

To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients. Methods: This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models. Results: The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both < 0.001). Conclusion: A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.

Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

“…In general, studies sought to evaluate whether or not radiomic signatures could outperform existing methods for patient risk stratification. 20 studies related radiomics to overall survival [ [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] ], 18 to the likelihood of local or metastatic recurrence [ [18] , [19] , [20] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ], 6 to response, disease-free or progression-free survival [ 32 , [47] , [48] , [49] , [50] , [51] ], and 2 to staging [ 54 , 55 ]. Two further studies focused on the association of radiomics signatures to lung toxicity [ 52 , 53 ], Four studies investigated multiple endpoints.…”

Machine Learning Models for Risk Prediction of Lymph Nodes Metastasis in Non-Small Cell Lung Cancer: Development and Validation Study

“…First, one of the study limitations is genomics and radiomics have not been considered. Some studies have recently proved that genomics and radiomics can predict LNM in NSCLC [33][34][35], but we are unsure whether adding these factors will improve our model. Second, small sample size will lead to residual confounding or statistical uctuation [36].…”

Preoperative staging of the mediastinum is an essential and multidisciplinary task

“…Different studies have shown that radiomics are more accurate than conventional morphological measurements in diagnosing lymph node metastasis, 11 even in patients with normal‐sized nodes 12,13 . Moreover, some studies have shown that radiomics can predict mediastinal involvement after extracting the data of the ROI from the primary tumour instead of the lymph nodes 13,14 …”

“…12,13 Moreover, some studies have shown that radiomics can predict mediastinal involvement after extracting the data of the ROI from the primary tumour instead of the lymph nodes. 13,14 Magnetic resonance imaging…”