Cost-effectiveness analysis of tislelizumab in combination with chemotherapy for the first-line treatment of patients with metastatic or recurrent nasopharyngeal carcinoma in China.

BACKGROUND: The combination of tislelizumab and gemcitabine plus cisplatin (GP) in the first-line treatment of patients with recurrent or metastatic nasopharyngeal carcinoma (R/M NPC) has yielded significant results. However, it is not clear whether this treatment option is cost-effective in China. The purpose of this study is to evaluate the cost-effectiveness of tislelizumab plus GP for the first-line treatment of R/M NPC from the perspective of the Chinese healthcare system. METHODS: A partitioned survival model with three discrete health states was constructed to evaluate the cost-effectiveness of tislelizumab plus GP versus GP in patients with R/M NPC. The target population enrolled in the RATIONALE-309 trial had previously not treated for R/M NPC. Drug costs were obtained from relevant databases, and the remaining cost and health utility data were collected from the literature. The main outcomes include the expected life years, quality-adjusted life years (QALYs), total cost, and incremental cost-benefit ratio (ICER). RESULTS: The tislelizumab plus GP regimen produced an additional cost ($18392.76) and additional 1.57 QALYs compared with GP used alone. The ICER was $18392.75/QALYs. Sensitivity analysis showed that the analysis was robust and the utility of PD status was most sensitive to the model results. The possibility of tislelizumab plus GP being cost-effective at the willingness-to-pay (WTP) threshold of $37 653/QALY was 99.8%. Subgroup analysis showed that high PD-L1 expression had little impact on the ICER of this regimen. CONCLUSION: In patients with R/M NPC, the regimen of tislelizumab plus GP, as the first-line treatment, is more cost-effective than the GP regimen in China.

Cost-effectiveness analysis of sintilimab plus IBI305 versus sorafenib for unresectable hepatic cell carcinoma in China.

BACKGROUND: Sintilimab combined with IBI305 treatment regimen had potential clinical benefits than sorafenib in the first-line treatment of patients with unresectable hepatic cell carcinoma (HCC). However, whether sintilimab plus IBI305 has economic benefits in China remains unclear. METHODS: From the perspective of Chinese payers, we used the Markov model to simulate patients with HCC receiving treatment with sintilimab plus IBI305 and sorafenib. The transition probability between health states was estimated using the parametric survival model, and the cumulative medical costs and utility of the two treatment methods were estimated. Considering the incremental cost-effectiveness ratios (ICERs) as the evaluation index, sensitivity analyses were performed to explore the impact of uncertainty on the results. RESULTS: Compared to sorafenib, sintilimab plus IBI305 generated an additional $17552.17 and 0.33 quality-adjusted life years, resulting in an ICER of $52817.89. The analysis outcomes were most sensitive to the total cost of sintilimab plus IBI305. With a willingness-to-pay threshold of $38,334, sintilimab plus IBI305 showed a 1.28% probability of being cost-effective. The total cost of sintilimab plus IBI305 should be reduced by at least 31.9% to be accepted by Chinese payers. CONCLUSIONS: Regardless of whether the price of sintilimab plus IBI305 and sorafenib is covered by Medicare, sintilimab plus IBI305 is unlikely to be cost-effective for first-line treatment of patients with unresectable HCC.

Identification of a seven autophagy-related gene pairs signature for the diagnosis of colorectal cancer using the RankComp algorithm.

Based on the colorectal cancer microarray sets gene expression data series (GSE) GSE10972 and GSE74602 in colon cancer and 222 autophagy-related genes, the differential signature in colorectal cancer and paracancerous tissues was analyzed by RankComp algorithm, and a signature consisting of seven autophagy-related reversal gene pairs with stable relative expression orderings (REOs) was obtained. Scoring based on these gene pairs could significantly distinguish colorectal cancer samples from adjacent noncancerous samples, with an average accuracy of 97.5% in two training sets and 90.25% in four independent validation GSE21510, GSE37182, GSE33126, and GSE18105. Scoring based on these gene pairs also accurately identifies 99.85% of colorectal cancer samples in seven other independent datasets containing a total of 1406 colorectal cancer samples.

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy.

Objectives: In radiomics, high-throughput algorithms extract objective quantitative features from medical images. In this study, we evaluated CT-based radiomics features, clinical features, in-depth learning features, and a combination of features for predicting a good pathological response (GPR) in non-small cell lung cancer (NSCLC) patients receiving immunotherapy-based neoadjuvant therapy (NAT). Materials and methods: We reviewed 62 patients with NSCLC who received surgery after immunotherapy-based NAT and collected clinicopathological data and CT images before and after immunotherapy-based NAT. A series of image preprocessing was carried out on CT scanning images: tumor segmentation, conventional radiomics feature extraction, deep learning feature extraction, and normalization. Spearman correlation coefficient, principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) were used to screen features. The pretreatment traditional radiomics combined with clinical characteristics (before_rad_cil) model and pretreatment deep learning characteristics (before_dl) model were constructed according to the data collected before treatment. The data collected after NAT created the after_rad_cil model and after_dl model. The entire model was jointly constructed by all clinical features, conventional radiomics features, and deep learning features before and after neoadjuvant treatment. Finally, according to the data obtained before and after treatment, the before_nomogram and after_nomogram were constructed. Results: In the before_rad_cil model, four traditional radiomics features ("original_shape_flatness," "wavelet hhl_firer_skewness," "wavelet hlh_firer_skewness," and "wavelet lll_glcm_correlation") and two clinical features ("gender" and "N stage") were screened out to predict a GPR. The average prediction accuracy (ACC) after modeling with k-nearest neighbor (KNN) was 0.707. In the after_rad_cil model, nine features predictive of GPR were obtained after feature screening, among which seven were traditional radiomics features: "exponential_firer_skewness," "exponential_glrlm_runentropy," "log- sigma-5-0-mm-3d_firer_kurtosis," "logarithm_skewness," "original_shape_elongation," "original_shape_brilliance," and "wavelet llh_glcm_clustershade"; two were clinical features: "after_CRP" and "after lymphocyte percentage." The ACC after modeling with support vector machine (SVM) was 0.682. The before_dl model and after_dl model were modeled by SVM, and the ACC was 0.629 and 0.603, respectively. After feature screening, the entire model was constructed by multilayer perceptron (MLP), and the ACC of the GPR was the highest, 0.805. The calibration curve showed that the predictions of the GPR by the before_nomogram and after_nomogram were in consensus with the actual GPR. Conclusion: CT-based radiomics has a good predictive ability for a GPR in NSCLC patients receiving immunotherapy-based NAT. Among the radiomics features combined with the clinicopathological information model, deep learning feature model, and the entire model, the entire model had the highest prediction accuracy.