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Hongyan Xu, Wenjing Wang, Junlei Yin, Chengcun Song, Lin Li, Zhi Sun Department of Chemotherapy, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, People’s Republic of ChinaCorrespondence: Hongyan Xu, Department of Chemotherapy, Weihai Municipal Hospital, No. 248, Tongyi Road, Huancui District, Weihai, Shangdong, 264200, People’s Republic of China, Tel +860631-5287510, Email xuhongyan19701212@163.comAim: To investigate the efficacy and safety of preoperative neoadjuvant therapy (PD-1 inhibitor plus nab-PTX and nedaplatin) for resectable stage III lung squamous cell carcinoma (SCC) patients.Methods: Patients with locally advanced lung SCC (stage IIIA, IIIB) who received PD-1 inhibitor combined with nab-PTX and NED between February 2019 and June 2021 in Weihai Municipal Hospital were included and underwent surgical treatment 4 weeks after 2– 4 cycles neoadjuvant therapy. The rate of resection R0, the effective rate, the complete pathological remission rate (pCR) and the rate of major pathological remission (MPR) were observed.Results: A total of 14 initially unresectable male patients with lung SCC were included and received neoadjuvant treatment after evaluation. Nine out of 14 patients (64.3%) experienced treatment-related adverse events (TRAE), among which 8 (57.1%) experienced grade (G) I–II TRAEs including nausea, vomiting, fatigue, constipation, elevated ALT and AST, hyperthyroidism, hypothyroidism, rash, granulocytopenia, and thrombocytopenia, and 1 (7.1%) experienced grade III–V TRAEs (G), including granulocytopenia and atelectasis. Thirteen patients (92.86%) achieved RECIST-assessed partial remission (PR), while 1 patient (7.14%) achieved stable disease (SD) on imaging assessment after neoadjuvant treatment and continued to be progression-free for 26 months. Of the 11 patients who underwent resection, all were alive and recurrence/progression-free. MPR and pCR were observed in 2 (18.18%) and 9 (81.82%), respectively. IHC results exhibited that all NSCLC patients exhibited positive PD-L1 expression (9/14, TPS ≥ 50% or greater; 5/14, 1% < TPS < 50%). Two were negative for ALK, EGFR, and ros-1, and the rest were not examined for driver oncogene mutation.Conclusion: The neoadjuvant therapy of the PD-1 inhibitor combined with nab-PTX and NED demonstrated remarkable therapeutic efficacy and good safety on stage III lung SCC without increasing the risk of TRAE, mortality and surgery-related complications, or impede surgery feasibility.Keywords: lung squamous cell lung cancer, neoadjuvant therapy, immunotherapy, complete pathological remission, pCR, major pathological remission, MPR

Lung cancer is one of the leading causes of cancer death and impose an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after the initial diagnosis of lung cancer. The Cox proportional hazards model is mostly utilized in survival analysis. However, real-world medical data is always incomplete, which poses a great challenge to the application of the Cox proportional hazards model. The commonly used imputation methods cannot achieve sufficient accuracy in the issue of missing data, which drives us to investigate the novel imputation methods for the development of clinical prediction models. In this article, we present a novel missing data imputation method: Bayesian networks for inferring missing covariates. We collected a total of 5,240 patients diagnosed with lung cancer from Weihai Municipal Hospital, China. Then we applied a joint model that combined a Bayesian network and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved a good predictive performance in discrimination and calibration. Through experiments, we proved that the Bayesian network methodology is a robust and accurate approach to addressing the issue of missing data. We showed that combining the Bayesian network with the Cox proportional hazards model is highly beneficial, providing a more efficient tool for risk prediction.