BACKGROUNDAbout 80% lung cancer is non-small cell lung cancer (NSCLC) and 70%-80% are in advanced stage. Chemotherapy is main treatment method. The aim of this study is to compare the therapeutic effects and toxicity of NP regimen combined with Shenqifuzheng injection on elder patients with advanced NSCLC.

METHODSTotally 69 patients enrolled into this study and were randomized into two groups: treatment group (35 patients) and control group (34 patients). Each patient received NVB 25mg/m² intravenously at days 1 and 8 and DDP 30mg intravenously from 1st day to 4th day. Shenqifuzheng injection was used in the treatment group by 250mL per day for 10 days.

RESULTSThere was no significant difference of the response rate between two groups (45.7% vs 41.2%, P > 0.05). The hematological toxicity, nausea and vomiting in the treatment group were lower than those in the control group with significant difference (P < 0.05). The adverse effects were well tolerable.

CONCLUSIONSNP regimen combined with Shenqifuzheng injection on elder patients with advanced NSCLC is effective and safe. Shenqifuzheng injection has definite toxicity relieving effect on treating elder patients with advanced NSCLC.

Objective:To build a predictive model for symptomatic radiation pneumonitis(RP) using the pretreatment CT radiomics features, clinical and dosimetric data of lung cancer patients by using machine learning method.Methods:A retrospective analysis of 103 lung cancer patients who underwent radiotherapy in the Affiliated Hospital of Jiangnan University from November 2018 to April 2020 was performed. Total normal lung tissues were segmented as an interested volume in pretreatment CT images, and then 250 radiomics features were extracted. The correlations of RP and clinical or dosimetric features were firstly investigated with univariate analysis. Then all clinical data, dosimetric data and CT radiomics features were collected and considered as predictors for modeling of RP grade ≥ 2. Features were selected through LASSO machine learning method, and the predictive model was built. Finally, nomogram for risk of RP were obtained according to the selected features.Results:The result of univariate analysis showed that symptomatic RP was significantly correlated with lung dosimetric parameters including mean lung dose (MLD), V20 Gy and V30 Gy( t=2.20, 2.34 and 2.93, P<0.05). Four features, including lung dose volume percentage V30 Gyand three radiomics features, entropy feature of GLCM, mean and median feature of wavelet histogram were selected among all clinical, dosimetric features and radiomics features. AUC of the predicted model obtained from selected features reached 0.757. For convenient clinical use, the nomogram were obtained, and then personalized RP risk prediction and early intervention could be performed according to this nomogram. Conclusions:Pretreatment CT radiomics and dosimetric features can be used in predicting symptomatic RP, which will be useful for advanced intervention treatment.