Discrimination of mediastinal metastatic lymph nodes in NSCLC based on radiomic features in different phases of CT imaging

Abstract

Background: We aimed to develop radiomic models based on different phases of computed tomography (CT) imaging and to investigate the efficacy of models for diagnosing mediastinal metastatic lymph nodes (LNs) in nonsmall cell lung cancer (NSCLC). Methods: Eighty-six NSCLC patients were enrolled in this study, and we selected 231 mediastinal LNs confirmed by pathology results as the subjects which were divided into training (n = 163) and validation cohorts (n = 68). The regions of interest (ROIs) were delineated on CT scans in the plain phase, arterial phase and venous phase, respectively. Radiomic features were extracted from the CT images in each phase. A least absolute shrinkage and selection operator (LASSO) algorithm was used to select features, and multivariate logistic regression analysis was used to build models. We constructed six models (orders 1-6) based on the radiomic features of the single-and dual-phase CT images. The performance of the radiomic model was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV). Results: A total of 846 features were extracted from each ROI, and 10, 9, 5, 2, 2, and 9 features were chosen to develop models 1-6, respectively. All of the models showed excellent discrimination, with AUCs greater than 0.8. The plain CT radiomic model, model 1, yielded the highest AUC, specificity, accuracy and PPV, which were 0.926 and 0.925; 0.860 and 0.769; 0.871 and 0.882; and 0.906 and 0.870 in the training and validation sets, respectively. When the plain and venous phase CT radiomic features were combined with the arterial phase CT images, the sensitivity increased from 0.879 and 0.919 to 0.949 and 0979 and the NPV increased from 0.821 and 0.789 to 0.878 and 0.900 in the training group, respectively. Conclusions: All of the CT radiomic models based on different phases all showed high accuracy and precision for the diagnosis of LN metastasis (LNM) in NSCLC patients. When combined with arterial phase CT, the sensitivity and NPV of the model was be further improved.

Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

“…A major hurdle that remains in the analysis of lymph nodes is represented by the radiological assessment, often in contrast with the pathological one. Most radiomics studies so far focused on the detection of positive nodal metastases rather than their prognostic values (39)(40)(41)(42)(43)(44).…”

Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer

“…Sha et al. demonstrated that radiomic features of mediastinal LNs on CT images exhibited acceptable ability in predicting the involvement of LNs in non-small-cell lung cancer patients [32] . Their study indicated that radiomic features might be different between positive and negative LNs, and these distinguishing features might be useful for directly predicting the involvement in specific LNs.…”

Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models

“…Some studies extended radiomic analysis outside tumor volume, including also peritumoral regions for prediction of patients’ survival [ 21 ] or adjuvant chemotherapy [ 22 ]. Similarly, lymph nodes were sometimes considered to be ROI in studies that aimed at identifying lymph nodes metastases [ 23 , 24 ]. Some radiomic NTCP models based on computed tomography (CT) were developed, for instance for prediction of xerostomia after head and neck cancer radiotherapy [ 25 , 26 ], radiation-induced pneumonitis [ 27 ].…”