Predictive value of inflammation and nutritional index in immunotherapy for stage IV non-small cell lung cancer and model construction.

Identifying individuals poised to gain from immune checkpoint inhibitor (ICI) therapies is a pivotal element in the realm of tailored healthcare. The expression level of Programmed Death Ligand 1 (PD-L1) has been linked to the response to ICI therapy, but its assessment typically requires substantial tumor tissue, which can be challenging to obtain. In contrast, blood samples are more feasible for clinical application. A number of promising peripheral biomarkers have been proposed to overcome this hurdle. This research aims to evaluate the prognostic utility of the albumin-to-lactate dehydrogenase ratio (LAR), the Pan-immune-inflammation Value (PIV), and the Prognostic Nutritional Index (PNI) in predicting the response to ICI therapy in individuals with advanced non-small cell lung cancer (NSCLC). Furthermore, the study seeks to construct a predictive nomogram that includes these markers to facilitate the selection of patients with a higher likelihood of benefiting from ICI therapy. A research initiative scrutinized the treatment records of 157 advanced NSCLC patients who received ICI therapy across two Jiangxi medical centers. The cohort from Jiangxi Provincial People's Hospital (comprising 108 patients) was utilized for the training dataset, while the contingent from Jiangxi Cancer Hospital (49 patients) served for validation purposes. Stratification was based on established LAR, PIV, and PNI benchmarks to explore associations with DCR and ORR metrics. Factorial influences on ICI treatment success were discerned through univariate and multivariate Cox regression analysis. Subsequently, a Nomogram was devised to forecast outcomes, its precision gauged by ROC and calibration curves, DCA analysis, and cross-institutional validation. In the training group, the optimal threshold values for LAR, PIV, and PNI were identified as 5.205, 297.49, and 44.6, respectively. Based on these thresholds, LAR, PIV, and PNI were categorized into high (≥ Cut-off) and low (< Cut-off) groups. Patients with low LAR (L-LAR), low PIV (L-PIV), and high PNI (H-PNI) exhibited a higher disease control rate (DCR) (P < 0.05) and longer median progression-free survival (PFS) (P < 0.05). Cox multivariate analysis indicated that PS, malignant pleural effusion, liver metastasis, high PIV (H-PIV), and low PNI (L-PNI) were risk factors adversely affecting the efficacy of immunotherapy (P < 0.05). The Nomogram model predicted a concordance index (C-index) of 0.78 (95% CI: 0.73-0.84). The areas under the ROC curve (AUC) for the training group at 6, 9, and 12 months were 0.900, 0.869, and 0.866, respectively, while the AUCs for the external validation group at the same time points were 0.800, 0.886, and 0.801, respectively. Throughout immunotherapy, PIV and PNI could act as prospective indicators for forecasting treatment success in NSCLC patients, while the devised Nomogram model exhibits strong predictive performance for patient prognoses.

Efficacy, safety, and predictive model of Palbociclib in the treatment of HR-positive and HER2-negative metastatic breast cancer.

PURPOSE: This research designeded to: 1. Analyze the efficacy and safety of Palbociclib treatment in HR-positive and HER2-negative (HR + /HER2-) metastatic breast cancer(MBC) patients. 2. Establish and validate a nomogram model for predicting the progression-free survival (PFS) rates of 6 months, 12 months, and 18 months in HR + /HER2- MBC patients after receiving Palbociclib plus endocrine therapy (ET). PATIENTS AND METHODS: 1. This research retrospectively analyzed the efficacy and safety of Palbociclib combined with ET in 214 patients with HR + /HER2- MBC. 2. A nomogram was designed and constructed with the retrospective clinical data of 214 patients with HR + /HER2- MBC who received Palbociclib plus ET at Zhejiang Cancer Hospital in China from August 2018 to August 2022. Among these patients, 161 were randomly assigned to the training cohort, while 53 to the validation cohort. The predictive accuracy of the nomogram was assessed through the analysis the area under the receiver operating characteristic(ROC) curve, calibration curve, and decision curve analysis(DCA). RESULTS: 1. Median PFS was 7.17 months (95% CI: 7.61-10.05 months), with an objective response rate (ORR) of 2.80% and a disease control rate (DCR) of 34.58%. The most prevalent grade 3-4 adverse event was neutropenia (38.79%). 2. Multiple variable analysis of the training set revealed that age < 60 years old, PR < 20%, Ki-67 ≥ 20%, luminal B molecular subtype, primary resistance to ET, receipt of late-stage chemotherapy, and presence of liver metastasis or ≥ 2 visceral metastases were independent prognostic factors associated with poor PFS (P < 0.05). Then, the predictive model underwent development and validation utilizing the aforementioned parameters. On the one hand, the area under the ROC curve (AUC) values of the training set at 6 months, 12 months, and 18 months were 0.771, 0.783, and 0.790, respectively, indicating a strong predictive ability of the developed model. On the other hand, the AUC of the validation set at 6 months, 12 months, and 18 months were 0.720, 0.766, and 0.754, respectively, suggesting the favorable discriminatory ability of the model. The calibration curves also exhibited a good fit with the ideal curves, and the DCA demonstrated the clinical applicability of the model. The nomogram's different scores could distinguish PFS. CONCLUSION: This retrospective study demonstrates the efficacy of Palbociclib in Chinese breast cancer patients. Moreover, the clinical parameters showed a significant association with the prognosis of HR + /HER2- MBC, and the prognostic models constructed based on these variables also displayed robust predictive power, which could offer more intuitive and convenient references for clinical doctors to formulate follow-up treatment plans.