Objective:To explore the prognostic value of pretreatment albumin in extranodal nasal type NK/T cell lymphoma (ENKTL).Methods:The clinical data of 184 ENKTL patients in Shanxi Province Cancer Hospital from January 2002 to December 2018 were retrospectively analyzed. The Contal-O'Quigley change point method was used to determine the optimal cut-off value of albumin for predicting the prognosis of patients. The propensity score matching (PSM) was used to minimize selection biases. The Kaplan-Meier method was used for survival analysis, and Cox proportional hazards model was used to determine the factors affecting survival. The time-dependent receiver operating characteristic curve, Akaike information criterion and integrated Brier score were used to evaluate the efficacy of international prognostic index (IPI), Korean prognostic index (KPI) and prognostic index of NK cell lymphoma (PINK) models incorporating albumin for predicting the prognosis of patients.Results:The optimal cut-off value of pretreatment albumin for predicting the prognosis of ENKTL patients was 37.5 g/L. The 3-year and 5-year overall survival (OS) rates in >37.5 g/L group (126 cases) were 66.2% and 60.3%, and the progression-free survival (PFS) rates were 58.8% and 49.6%; the 3-year and 5-year OS rates in ≤37.5 g/L group (58 cases) were 35.0% and 32.4%, and the PFS rates were 32.5% and 30.0%. The OS and PFS in > 37.5 g/L group were better than those in ≤37.5 g/L group (both P<0.001). After PSM, the OS and PFS in >37.5 g/L group were still better than those in ≤37.5 g/L group (both P = 0.002). Multivariate analysis showed that albumin was an independent influencing factor for OS ( RR = 0.419, 95% CI 0.266-0.660, P < 0.001) and PFS ( RR = 0.493, 95% CI 0.322-0.755, P < 0.001). After PSM, albumin was still an independent influencing factor for OS ( RR = 0.305, 95% CI 0.156-0.598, P = 0.001) and PFS ( RR = 0.341, 95% CI 0.185-0.627, P = 0.001). The prognostic prediction performance of the IPI, KPI and PINK models incorporating albumin were all improved. Conclusions:Pretreatment albumin is an important prognostic indicator for ENKTL.

Objective:To investigate the value of derived neutrophil-to-lymphocyte ratio (dNLR) in predicting the prognosis of extensive-stage small cell lung cancer (ES-SCLC) patients treated with the first-line atezolizumab immunotherapy and chemotherapy.Methods:From the Project Data Sphere platform, the clinical data and laboratory test data of 53 ES-SCLC patients who received the first-line atezolizumab immunotherapy and chemotherapy in the global multicenter phase Ⅱ prospective study NCT03041311 from February 2017 to February 2022 were collected. The Contal-O'Quigley method was used to calculate the optimal cut-off value of baseline dNLR for determining the overall survival (OS) of patients. The dNLR higher than or equal to the optimal cut-off value was defined as high dNLR, and less than the optimal cut-off value was defined as low dNLR. According to optimal cut-off value, the dNLR levels at baseline and after 4 cycles of chemotherapy were determined, and dynamic dNLR grouping was performed (low risk: low dNLR at baseline and after 4 cycles of chemotherapy; intermediate risk: high dNLR at baseline or after 4 cycles of chemotherapy; high risk: high dNLR at baseline and after 4 cycles of chemotherapy). The differences in clinicopathological features between the baseline high dNLR group and low dNLR group were analyzed. Kaplan-Meier method was used to draw the OS and progression-free survival (PFS) curves, and log-rank test was used to compare the differences between the two groups. Univariate Cox proportional hazards model was used to analyze the influencing factors of OS and PFS. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the predictive value of baseline dNLR grouping and dynamic dNLR grouping for 1-year OS rate in ES-SCLC patients receiving the first-line atezolizumab immunotherapy and chemotherapy.Results:Among the 53 patients, 34 (64.20%) were male and 19 (35.80%) were female; 27 (50.90%) were < 65 years old and 26 (49.10%) were ≥65 years old. The optimal cut-off value of baseline dNLR for determining the OS was 1.79. There were 17 cases in low dNLR group and 36 cases in high dNLR group at baseline. The proportion of patients with elevated serum lactate dehydrogenase (LDH) in the baseline high dNLR group was higher than that in the baseline low dNLR group [58.33% (21/36) vs. 17.65% (3/17), χ2 = 7.72, P = 0.005]. The 1-year OS rates of the baseline high and low dNLR groups were 44.0% and 81.9%, and the 1-year PFS rates were 2.5% and 17.6%. The differences in OS and PFS between the two groups were statistically significant (both P < 0.05). There were 38 patients with complete dynamic dNLR data, including 9 cases of low-risk, 19 cases of medium-risk and 10 cases of high-risk, and the 1-year OS rates of the three groups were 90.0%, 67.5% and 33.3%, the difference in OS between the three groups was statistically significant ( P = 0.011). Univariate Cox regression analysis showed that baseline dNLR (low dNLR vs. high dNLR) was the influencing factor for OS of patients ( HR = 0.163, 95% CI 0.057-0.469, P = 0.001) and PFS ( HR = 0.505, 95% CI 0.268-0.952, P = 0.035). Time-dependent ROC curve analysis showed that the area under the curve (AUC) of baseline dNLR grouping and dynamic dNLR grouping for predicting 1-year OS rate of ES-SCLC patients receiving the first-line atezolizumab combined with chemotherapy was 0.674 (95% CI 0.575-0.887) and 0.731 (95% CI 0.529-0.765). Conclusions:Baseline and dynamic dNLR grouping may be effective markers for predicting the prognosis of ES-SCLC patients receiving the first-line atezolizumab immunotherapy and chemotherapy.

Objective:To investigate the predictive value of enhanced CT-based radiomics for brain metastasis (BM) and selective use of prophylactic cranial irradiation (PCI) in limited-stage small cell lung cancer (LS-SCLC).Methods:Clinical data of 97 patients diagnosed with LS-SCLC confirmed by pathological and imaging examination in Shanxi Provincial Cancer Hospital from January 2012 to December 2018 were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) Cox and Spearman correlation tests were used to select the radiomics features significantly associated with the incidence of BM and calculate the radiomics score. The calibration curve, the area under the receiver operating characteristic (ROC) curve (AUC), 5-fold cross-validation, decision curve analysis (DCA), and integrated Brier score (IBS) were employed to evaluate the predictive power and clinical benefits of the radiomics score. Kaplan-Meier method and log-rank test were adopted to draw survival curves and assess differences between two groups.Results:A total of 1272 radiomics features were extracted from enhanced CT. After the LASSO Cox regression and Spearman correlation tests, 8 radiomics features associated with the incidence of BM were used to calculate the radiomics score. The AUCs of radiomics scores to predict 1-year and 2-year BM were 0.845 (95% CI=0.746-0.943) and 0.878 (95% CI=0.774-0.983), respectively. The 5-fold cross validation, calibration curve, DCA and IBS also demonstrated that the radiomics model yielded good predictive performance and net clinical benefit. Patients were divided into the high-risk and low-risk cohorts based on the radiomics score. For patients at high risk, the 1-year and 2-year cumulative incidence rates of BM were 0% and 18.2% in the PCI group, and 61.8% and 75.4% in the non-PCI group, respectively ( P<0.001). In the PCI group, the 1-year and 2-year overall survival rates were 92.9% and 78.6%, and 85.3% and 36.8% in the non-PCI group, respectively ( P=0.023). For patients at low risk, the 1-year and 2-year cumulative incidence rates of BM were 0% and 0% in the PCI group, and 10.0% and 20.2% in the non-PCI group, respectively ( P=0.062). In the PCI group, the 1-year and 2-year overall survival rates were 100% and 77.0%, and 96.7% and 79.3% in the non-PCI group, respectively ( P=0.670). Conclusion:The radiomics model based on enhanced CT images yields excellent performance for predicting BM and individualized PCI.