Immune checkpoint inhibitors (ICI) have created an advanced shift in the treatment of lung cancer (LC), but the existing biomarkers were not in clinical and widespread use. The purpose of this study was to develop a new nomogram with immune factors used for monitoring the response to ICI therapy. LC patients with PD-1/PD-L1 inhibitors treatment were included in this analysis. The immune biomarkers and clinicopathological characteristic values at baseline were used to estimate the tumor response. The nomogram was based on the factors that were determined by univariate and multivariate Cox hazard analysis. For internal validation, bootstrapping with 1000 resamples was used. The concordance index ( C -index) and calibration curve were used to determine the predictive accuracy and discriminative ability of the nomogram. Overall survival (OS) was estimated using the Kaplan-Meier method. Patients with lung metastasis ( P = 0.010 ), higher baseline neutrophil-lymphocyte ratio (NLR) level ( P < 0.001 ), lower baseline lymphocyte-monocyte (LMR) ( P = 0.019 ), and lower CD3+CD8+ T cell count ( P = 0.009 ) were significantly related to the tumor response. The above biomarkers were contained into the nomogram. The calibration plot for the probability of OS showed an optimal agreement between the actual observation and prediction by nomogram at 3 or 5 years after therapy. The C -index of nomogram for OS prediction was 0.804 (95% CI: 0.739-0.869). Decision curve analysis demonstrated that the nomogram was clinically useful. Moreover, patients were divided into two distinct risk groups for OS by the nomogram: low-risk group (OS: 17.27 months, 95% CI: 14.75-19.78) and high-risk group (OS: 6.11 months, 95% CI: 3.57-8.65), respectively. A nomogram constructed with lung metastasis baseline NLR, LMR, and CD3+CD8+ T cell count could be used to monitor and predict clinical benefit and prognosis in lung cancer patients within ICI therapy.
Background Microvascular invasion (MVI) plays an important role in tumor progression. The aim of this study is to establish and validate an effective hematological nomogram for MVI prediction in hepatocellular carcinoma (HCC). Methods A retrospective study was performed in a primary cohort that includes 1306 patients clinicopathologically diagnosed with HCC, and a validation cohort contained 563 continuous patients. Univariate logistic regression was used to assess the association between variables included both clinicopathologic factors and coagulation parameters (prothrombin time, activated partial thromboplastin time, fibrinogen, and thrombin time [TT]) and MVI. Multiple logistic regression was used to construct a prediction nomogram. We tested the accuracy of the nomogram by discrimination and calibration, and then plotted decision curves to assess the benefits of the nomogram-assisted decisions in a clinical context. Results In the two cohorts, patients without MVI had the longest overall survival (OS), compared the OS with MVI. The multivariate analysis indicated that age, sex, tumor node metastasis (TNM) stage, aspartate aminotransferase, alpha fetoprotein, C-reactive protein, and TT were identified as significant independent predictors of MVI of HCC patients. The Hosmer–Lemeshow test showed good point estimate associated P value between predicted risk and observed risk across the deciles. Moreover, the calibration performance of the nomogram risk scores in each decile of the primary cohort was within 5 percentage points of the mean predicted risk score, and in the validation cohort, the observed risk in 90% decile was within 5 percentage points of the mean predicted risk score. Conclusions A noninvasive and easy-to-use nomogram was established and may be used to predict preoperative MVI in HCC.
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