Artificial intelligence-based radiomics for the prediction of nodal metastasis in early-stage lung cancer

Abstract

Abstract We aimed to investigate the value of computed tomography (CT)-based radiomics with artificial intelligence (AI) in predicting pathological lymph node metastasis (pN) in patients with clinical stage 0–IA non-small cell lung cancer (c-stage 0–IA NSCLC). This study enrolled 720 patients who underwent complete surgical resection for clinical stage 0–IA NSCLC, and were assigned to the derivation and validation cohorts. Using the AI software Beta Version (Fujifilm Corporation, Japan), 39 AI imaging factors, including 17 factors from the AI ground-glass nodule analysis and 22 radiomics features from nodule characterization analysis, were extracted to identify factors associated with pN. Multivariate analysis showed that clinical stage IA3 (p = .028), solid-part size (p < .001), and average solid CT value (p = .033) were independently associated with pN. The receiver operating characteristic analysis showed that the area under the curve and optimal cut-off values of the average solid CT value relevant to pN were 0.761 and − 103 Hounsfield units, and the threshold value provided sensitivity, specificity, and negative predictive values of 67%, 59%, and 93%, respectively. Measuring the average solid-CT value of tumors for pN may have broad clinical applications such as guiding surgical approaches and individualized postoperative treatment.