Integrative Nomogram of Computed Tomography Radiomics, Clinical, and Tumor Immune Features for Analysis of Disease-Free Survival of NSCLC Patients with Surgery. (21st February 2023)

• Record Type: Journal Article
• Authors: Xiu, Dianhui; Mo, Yan; Liu, Chaohui; Hu, Yu; Wang, Yanjing; Zhao, Yiming; Guo, Tiantian; Cheng, Kailiang; Huang, Chencui; Liu, Lin; Cheng, Min
• Other Names: Pan Jinghua Academic Editor.
• Is Part Of: Journal of oncology. Volume 2023(2023)
• Publication Date: 2023-02-21
• Subjects: Oncology -- Research -- Periodicals; Tumors -- Periodicals; Neoplasms; Oncology -- Research; Tumors; Periodicals; Periodicals; 616.994
• DOI: 10.1155/2023/8607062
• Languages: English
• ISSNs: 1687-8450

Abstract

Abstract : To improve prognosis of cancer patients and determine the integrative value for analysis of disease-free survival prediction, a clinic investigation was performed involving with 146 non-small cell lung cancer (NSCLC) patients (83 men and 73 women; mean age: 60.24 years ± 8.637) with a history of surgery. Their computed tomography (CT) radiomics, clinical records, and tumor immune features were firstly obtained and analyzed in this study. Histology and immunohistochemistry were also performed to establish a multimodal nomogram through the fitting model and cross-validation. Finally, Z test and decision curve analysis (DCA) were performed to evaluate and compare the accuracy and difference of each model. In all, seven radiomics features were selected to construct the radiomics score model. The clinicopathological and immunological factors model, including T stage, N stage, microvascular invasion, smoking quantity, family history of cancer, and immunophenotyping. The C-index of the comprehensive nomogram model on the training set and test set was 0.8766 and 0.8426 respectively, which was better than that of the clinicopathological-radiomics model (Z test, P =0.041<0.05), radiomics model and clinicopathological model (Z test, P =0.013<0.05 and P =0.0097<0.05). Integrative nomogram based on computed tomography radiomics, clinical and immunophenotyping can be served as effective imaging biomarker to predict DFS of hepatocellular carcinoma after surgical resection.