Details of Research Outputs

Background: Previous studies have shown that neoadjuvant immune checkpoint inhibitors (ICIs) combined with chemotherapy in patients with stage IB-IIIA non-small cell lung cancer (NSCLC) significantly improved the major pathological response (MPR) and the pathological complete response (pCR) rates. However, high-level evidence-based medical data confirming this effect are still lacking. In addition, there is an urgent need to develop an appropriate strategy to predict the benefit for patients receiving ICIs. In this study, we describe an ongoing study on the effect of neoadjuvant therapy with camrelizumab, nab-paclitaxel, and carboplatin on stage IB-IIIA NSCLC patients. The aim of this study is to establish a multiomics artificial intelligence system for predicting neoadjuvant therapy efficacy and assisting decision-making. Methods: This prospective, single-arm, multicenter, phase II trial will enroll a total of 40 patients who will undergo surgery after three cycles of neoadjuvant therapy with camrelizumab, nab-paclitaxel, and carboplatin. The MPR rate is the primary endpoint, while the rates of pCR, complete resection, objective response, disease-free survival (DFS), adverse events (AEs), and quality of life (QOL) are secondary endpoints. Exploratory endpoints will serve to establish a multiomics artificial intelligence system for neoadjuvant therapy effect prediction and decision-making assistance based on radiomics, metabolism, genetic, and clinic-pathological characteristics and to explore the mechanisms of drug resistance. Discussion: The efficacy of ICIs is influenced by many factors, including patient's driver genes and smoking status. Thus, further subgroup analysis is needed. This study will indicate if our new multiomics artificial intelligence system constitutes a valid strategy for neoadjuvant therapy effect prediction and decision-making assistance in the context of neoadjuvant camrelizumab, nab-paclitaxel, and carboplatin treatment for patients with stage IB-IIIA NSCLC.