Programmed Death-Ligand 1 Expression and EGFR Mutations in Multifocal Lung Cancer

BACKGROUND:Little is known about the programmed death-ligand 1 (PD-L1) expression in multifocal lung cancer, such as the expression in multiple primary lung cancer and pulmonary metastasis. In this translational study, we investigated PD-L1 expression and its relationship with the epidermal growth factor receptor (EGFR) mutation status in resected multifocal lung cancer. / METHODS:The PD-L1 expression in 152 samples of multifocal lung cancer from 59 patients was evaluated by an immunohistochemical analysis. / RESULTS: Among the 152 lung cancer lesions of 59 patients, PD-L1 expression was observed in 29 lesions (19.1%) of 20 patients (33.9%). Among 43 patients with 112 multiple primary lung cancer lesions, 15 lesions (13.4%) of 13 patients (30.2%) were PD-L1 positive; and among 16 patients with 40 pulmonary metastatic lesions, 14 lesions (35.0%) of 7 patients (43.8%) were PD-L1-positive. Among 43 patients with multiple primary lung cancer, there was disagreement of PD-L1 expression in 12 patients (27.9%, κ = 0.104). On the contrary, among 16 patients with pulmonary metastasis, disagreement of PD-L1 expression was observed only in 1 patient (6.3%, κ = 0.871). In pulmonary metastatic lesions, the frequency of PD-L1 positivity among lesions with wild-type EGFR was significantly higher than among lesions with mutated EGFR (66.7% versus 0%: p < 0.001). / CONCLUSIONS:This study provides important evidence of higher levels of agreement of PD-L1 expression in pulmonary metastasis compared with in multiple primary lung cancer, and high positivity of PD-L1 expression in pulmonary metastatic lesions with wild-type EGFR in an Asian population

Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer

Abstract There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I–IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23–89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC