Objectives To develop and validate a preoperative CT-based nomogram combined with radiomic and clinical–radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. Methods This was a retrospective, diagnostic study conducted from August 1, 2018, to May 1, 2020, at three centers. Patients with a solitary pulmonary nodule were enrolled in the GDPH center and were divided into two groups (7:3) randomly: development (n = 149) and internal validation (n = 54). The SYSMH center and the ZSLC Center formed an external validation cohort of 170 patients. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to feature signatures and transform them into models. Results The study comprised 373 individuals from three independent centers (female: 225/373, 60.3%; median [IQR] age, 57.0 [48.0–65.0] years). The AUCs for the combined radiomic signature selected from the nodular area and the perinodular area were 0.93, 0.91, and 0.90 in the three cohorts. The nomogram combining the clinical and combined radiomic signatures could accurately predict interstitial invasion in patients with a solitary pulmonary nodule (AUC, 0.94, 0.90, 0.92) in the three cohorts, respectively. The radiomic nomogram outperformed any clinical or radiomic signature in terms of clinical predictive abilities, according to a decision curve analysis and the Akaike information criteria. Conclusions This study demonstrated that a nomogram constructed by identified clinical–radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key Points • The radiomic signature from the perinodular area has the potential to predict pathology invasiveness of the solitary pulmonary nodule. • The new radiomic nomogram was useful in clinical decision-making associated with personalized surgical intervention and therapeutic regimen selection in patients with early-stage non-small-cell lung cancer.
e21551 Background: Molecular characterization studies revealed recurrent KEAP1/ NFE2L2 alterations in non-small cell lung cancer (NSCLC). Previous studies have confirmed that KEAP1/NFE2L2 mutations are a poor prognostic factor for chemotherapy in patients with NSCLC. Nevertheless, it is unclear whether KEAP1/NFE2L2 mutations (MUT) of liquid biopsy can predict the efficacy of immunotherapy in NSCLC. Methods: Two independent cohorts (the OAK and POPLAR study cohort) with data from approximately 853 patients with advanced NSCLC were used to analyze the prognostic effect of KEAP1/NFE2L2 on immunotherapy. In addition, based on a deconvolution algorithm (known as CIBERSORT), we comprehensively analyzed the tumor-infiltrating immune cells present in NSCLC. The fraction of 22 immune cells subpopulations was evaluated to determine the associations between each cell type and KEAP1/NFE2L2 mutation status utilizing data from 1268 patients by lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (LUSC) in TCGA pan-cancer cohort. Results: The OAK and POPLAR study cohort of NSCLC patients showed that KEAP1/NFE2L2 MUT was associated with poorer overall survival (OS), and progression-free survival (PFS) (OS: HR = 1.7, P < 0.001; PFS:HR = 1.4, P < 0.001) on immunotherapy, even after EGFR and ALK mutations were excluded, significant difference can also be gained (OS:HR = 1.8, P < 0.001; PFS:HR = 1.5, P < 0.001). Then, the NSCLC patients were subdivided into LUAD and LUSC, the OS and PFS of patients with KEAP1/NFE2L2 MUT is lower than wild-type (WT) (OS:HR = 1.8, P < 0.001; PFS:HR = 1.4, P = 0.0014) in LUAD, significant differences were obtained even when EGFR and ALK mutations were excluded (OS:HR = 1.9, P < 0.001;PFS:HR = 1.6, P < 0.001). In LUSC, patients with KEAP1/NFE2L2 MUT have lower OS (HR = 1.4, P = 0.0473),and there was no difference on PFS (HR = 1.2, P = 0.1588) between KEAP1/NFE2L2 MUT and WT, when EGFR and ALK mutations were excluded, the survival results did not change significantly. In addition, KEAP1/NFE2L2 MUT was positively correlated with infiltrating levels of plasma cells, T cells CD4 memory activated, T cells follicular helper, and Macrophages M1, but negatively correlated with infiltrating levels of T cells CD4 memory resting, monocytes, Dendritic cells activated, Mast cells resting, and Neutrophils in NSCLC. The immunoinfiltration of LUAD was significantly different from that of LUSC. Conclusions: These findings suggest that KEAP1/NFE2L2 can be used as a poorer biomarker for determining prognosis on immunotherapy and immune infiltration in NSCLC.
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