Background: Manual assessment of lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) is inconsistent, and this study is aimed to develop a deep learning tool, DeepLNM, for LNM identification in ESCC. Methods: Consecutive ESCC patients with radical esophagectomy at Sichuan Cancer Hospital (2009–2018) comprised the internal cohort to develop DeepLNM, with three independent external test sets. Regional lymph nodes (LNs) with nodal stations were automatically segmented on computed tomography (CT) scans, and manually annotated according to histopathology. The DeepLNM diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC) with its 95% confidence interval (CI), and compared to physicians. Findings: The internal set comprised 815 ESCC patients (2,631 LN masks) for DeepLNM development, with 657 patients (4,314 annotations) in the external set. DeepLNM achieved an AUC of 0.92 (95% CI: 0.90, 0.94) for lesion-level LNM identification in the internal dataset with the most optimal sensitivity 0.85 and specificity 0.86, and AUCs of 0.89 (95% CI: 0.86, 0.92), 0.90 (95% CI: 0.86, 0.93), and 0.89 (95% CI: 0.86, 0.91) in the external test set 1, 2, and 3, respectively. For patient-level N-stage classification, DeepLNM demonstrated an AUC of 0.86 (95% CI: 0.84, 0.89) in the internal dataset with the most optimal sensitivity 0.73 and specificity 0.85, and AUCs of 0.84 (95% CI: 0.79, 0.88), 0.85 (95% CI: 0.77, 0.92), and 0.81 (95% CI: 0.75, 0.86) in the external test set 1, 2, and 3, respectively. DeepLNM outperformed oncologists in AUCs of LNM identification (0.91 vs 0.66–0.78, P < 0.05) and N-stage classification (0.83 vs 0.53–0.67, P < 0.05). Interpretation: DeepLNM accurately detected LNM and classified N-stage in patients with ESCC using pretreatment CT, highlighting its clinical value in personalized risk stratification and risk-adapted treatment.

OBJECTIVES:Radiotherapy is one of the most important treatment modalities for malignant tumors, with 60% to 70% of cancer patients requiring radiotherapy during the course of their disease. This study aims to comprehensively investigate and analyze the current status of radiotherapy in Hunan Province in 2025, providing evidence for optimizing the allocation of radiotherapy resources, enhancing oncology service capacity, and improving the regional radiotherapy system across the province. METHODS:Based on the standardized radiation therapy basic information survey form developed by the Hunan Provincial Medical Quality Control Center for Cancer Diagnosis and Treatment, a combined approach of online data submission and offline verification was used to conduct a survey among medical institutions in Hunan Province providing radiation therapy services. Data were collected through a combination of online reporting and on-site verification. Survey items included institutional characteristics, workforce allocation, radiotherapy equipment and auxiliary facilities, implementation of radiotherapy technologies, patient volume, and quality control system development. The survey was conducted from July to September 2025 under the coordination of municipal cancer diagnosis and treatment quality control centers. After data collation and verification, the authenticity of the reported information was further validated through random on-site inspections conducted by the Hunan Provincial Quality Control Center for Cancer Diagnosis and Treatment. RESULTS:As of September 2025, Hunan Province had 90 radiotherapy institutions, representing an increase of 14 institutions compared with 2022. Among them, 70 (77.78%) were tertiary hospitals and 74 (82.22%) were public hospitals, which remained the major service providers of radiotherapy services. The proportion of annual radiotherapy patients treated in non-capital-city hospitals increased from 26.5% to 29.3%. A total of 2 057 radiotherapy professionals were employed province-wide, including 889 physicians, 231 medical physicists, 433 radiation therapists, 444 nurses, and 60 engineers, yielding a physician-to-physicist ratio of 3.8꞉1. In terms of equipment, there were 127 external beam radiotherapy units, including 115 medical accelerators (of which 4 were Tomotherapy [TOMO] systems) and 12 Gamma Knife units, as well as 33 brachytherapy afterloading systems. Auxiliary equipment included 85 simulation units, 210 treatment planning systems, 199 dosimeters, and 114 verification systems. Compared with 2022, the numbers of linear accelerators, CT simulators, and dose verification devices increased by 30.59%, 61.54%, and 93.22%, respectively. The province achieved 1.73 linear accelerators per million population, although regional disparities in resource distribution persisted. The proportion of domestically manufactured linear accelerators increased from 10.22% in 2022 to 14.41% in 2025. Regarding radiotherapy technologies, a precision radiotherapy system centered on intensity-modulated radiation therapy (IMRT) had been established, with an IMRT implementation rate of 84.4%. Approximately 45% of radiotherapy institutions had adopted image-guided radiation therapy (IGRT), stereotactic body radiotherapy (SBRT), and respiratory motion management technologies. The annual number of patients receiving radiotherapy across the province reached approximately 48 000. The most common cancer types treated were lung cancer, breast cancer, cervical cancer, nasopharyngeal carcinoma, and rectal cancer, accounting for 60.11% of all radiotherapy cases. Following the implementation of Hunan Medical Insurance Policy [2025] No. 18, precision radiotherapy techniques, including IMRT, volumetric modulated arc therapy (VMAT), TOMO, and SBRT, were incorporated into the Category B reimbursement list of the provincial medical insurance program, substantially improving their accessibility and affordability. CONCLUSIONS:The radiotherapy service capacity of Hunan Province improved substantially in 2025 compared with 2022, as evidenced by increases in the number of radiotherapy institutions and equipment, wider adoption of precision radiotherapy technologies, and further enhancement of quality control systems. A modern precision radiotherapy framework centered on IMRT has been largely established. Optimization of medical insurance reimbursement policies has further improved access to advanced radiotherapy technologies. Nevertheless, challenges remain, including unequal regional distribution of radiotherapy resources, suboptimal utilization efficiency of equipment in primary-level institutions, and shortages of highly qualified medical physicists. Future efforts should focus on strengthening regional coordination and resource allocation, enhancing radiotherapy capacity at the primary care level, improving the standardization of quality control practices, and promoting balanced development of the radiotherapy service system.

Background: Colorectal cancer (CRC) exhibits significant molecular heterogeneity, leading to diverse clinical outcomes and highlighting the need for more accurate prognostic biomarkers. This study aimed to identify a novel gene signature to improve risk stratification and to explore its underlying biological relevance, particularly in relation to the tumor immune landscape. Results: A novel four-gene signature comprising SLC16A8, MAGEA1, LINC00634, and PPFIA4 was developed and validated. This signature effectively stratified patients into high- and low-risk groups with markedly different overall survival (p < 0.0001). The model demonstrated strong predictive accuracy for 1-, 3-, and 5-year survival (AUCs = 0.706, 0.735, 0.693, respectively). Importantly, multivariate Cox regression confirmed the signature as a powerful and independent prognostic factor (HR = 3.50, 95% CI = 2.10-5.80, p < 0.001). A clinically practical nomogram integrating the signature was constructed and showed excellent calibration. Furthermore, the risk score was significantly correlated with the infiltration levels of several key immune cells, suggesting that the signature reflects the host's anti-tumor immune status. Conclusions: We have successfully established and validated a novel four-gene signature that serves as an independent and powerful prognostic biomarker for CRC. This signature not only improves personalized risk stratification but also provides a potential link between the tumor's intrinsic molecular features and the surrounding immune landscape. The constructed nomogram offers a valuable tool to aid in clinical decision-making for CRC patients. Methods: Based on an integrated analysis of transcriptome data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project, we identified a pool of candidate prognostic genes. A robust prognostic signature was constructed using LASSO-Cox regression analysis. The signature's performance was comprehensively validated in the TCGA cohort, and its independence from conventional clinicopathological factors was assessed. A nomogram was developed to enhance its clinical utility. The CIBERSORT algorithm was used to investigate the association between the signature and tumor-infiltrating immune cells.

Accurately predicting the survival of patients with esophageal cancer after esophagectomy is crucial for clinical precision treatment. However, the existing methods of predicting Overall Survival time (OStime) mostly build supervised learning with the uncensored data, ignoring the potential information hidden in the censored data. To utilize the information hidden in the clinically abundant censored data, we propose a Semi-Supervised Learning with Adaptive pseudo-label Selection and Correction (SSLASC) to predict the OStime of esophageal cancer using both uncensored and censored data. Specifically, we first transform the OStime regression problem to a classification task followed by Softmax Expected Value Refinement (SEVR) and train a Transformer network using the uncensored data, which is then used to predict the OStime for the censored data. Secondly, we design an adaptive pseudo-label selection strategy to dynamically select more classes and more balanced samples from the predicted censored data by allocating adaptive thresholds for different classes of samples when performing pseudo-label selection. Finally, a distribution correction and a meta label correction modules are proposed to make the selected pseudo-labels closer to the real overall OStime. We test SSLASC on an internal dataset and two external datasets with sample sizes of 327, 104, and 16, respectively. The experimental results demonstrate that SSLASC achieves Mean Absolute Error (MAE) of 12.23, 12.64, and 12.47 months on the three test datasets. Compared to the optimal State-Of-The-Art (SOTA) method, SSLASC improves performance by 1.09, 1.07, and 1.09 months, respectively. In addition, SSLASC also achieves the best performance in dichotomized survival analysis.

Accurately predicting lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) is crucial for planning patient treatments. However, this task remains challenging, complicating treatment decision-making; this is particularly concerning for patients classified as pN0 owing to insufficient lymph node dissection (< 15 lymph nodes), as the effectiveness of postoperative adjuvant therapy (POAT) for these patients remains controversial. Therefore, we aimed to develop a CT-based predictive model to improve LNM detection in ESCC patients and identify pN0 patients who can benefit from POAT. We retrospectively enrolled 974 ESCC patients who underwent radical esophagectomy with adequate lymph node dissection (≥ 15 lymph nodes), dividing them into training (432 patients), internal validation (185 patients), and external validation cohorts (357 patients). To predict LNM, we developed a Stacking model using multiple instance and ensemble learning, leveraging the visible lymph nodes, primary tumor features, and clinical characteristics of each patient. Additionally, we separately enrolled 386 pN0 patients who underwent insufficient lymph node dissection and classified them into low-risk and high-risk groups on the basis of the optimal cut-off value from the Stacking model. Kaplan‒Meier and Cox models were used to assess the impact of POAT on patient survival. The Stacking model achieved area under the curve (AUC) values of 0.883, 0.834, and 0.819 in the training, internal validation, and external validation cohorts, respectively, outperforming traditional models based separately on tumor features, clinical characteristics, or the features of the largest lymph node. pN0 patients identified by the Stacking model as high risk had worse overall survival than low-risk patients did. POAT provided survival benefits for the high-risk patients but had no significant effect on the survival of low-risk patients. Our Stacking model achieved excellent LNM prediction in ESCC patients and holds promise for guiding personalized treatment strategies, particularly for pN0 patients with insufficient lymph node dissection.

Background and purpose:Clinical prognostic models for nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT) with or without chemotherapy remain insufficient to capture tumour heterogeneity. We investigated whether computed tomography (CT)-based signatures add prognostic value for overall survival, progression-free survival, local control and distant control in NPC patients. Materials and methods:The study population consisted of 1360 patients with stage I-IVa NPC treated with (chemo)IMRT (2013-2017). Radiomic and deep-learning features were analysed with twelve clinical variables. Radiomic models were built using bootstrap resampling feature selection and multivariable Cox regression; deep-learning models used 3D ResNet-18 or DenseNet-121. Models were evaluated on an internal hold-out test set (n = 409; training set n = 951) with the concordance index and compared against clinical-only reference models. Decision curve analysis was used to assess clinical utility. Results:Adding radiomic primary tumour features (Neighbouring Gray Tone Difference Matrix - coarseness) improved local control concordance index from 0.51 to 0.60 (p = 0.02). A DenseNet-121 combining clinical data with composite primary tumour and lymph node masks achieved the highest distant control (0.68 vs 0.66, p = 0.01). For overall survival and progression-free survival, the improvements were not significant. Decision curve analysis demonstrated net benefit of the DenseNet-121 distant control model over treat-all and treat-none strategies at threshold probabilities of 10-25%. Conclusions:Incorporating CT-based radiomic and deep-learning features into prognostic models significantly improved prediction of local and distant control in NPC, supporting their potential as imaging biomarkers for refined risk stratification.

Currently, the American Joint Committee on Cancer staging systems do not reliably predict outcomes in nasopharyngeal carcinoma as patients with similar tumour stages and treatments often experience divergent outcomes. This study aimed to develop prediction models for overall survival (OS), progression-free survival (PFS), locoregional control (LRC), and distant control (DC) based on routinely collected clinical parameters in nasopharyngeal carcinoma patients treated with (chemo)radiation to support personalised treatment strategies. Data from 996 patients treated with intensity-modulated radiotherapy at Hunan Cancer Hospital were used for model training, with 409 patients reserved for internal testing. An external cohort of 484 patients from the Cancer Hospital of Shantou University Medical College was used to validate the OS model. Eleven clinical variables were included with multivariate imputation applied to missing values. Highly correlated variables were excluded through pre-selection, followed by stepwise forward Cox regression modelling. Model performance was compared to AJCC staging for each endpoint. The OS model achieved good performance in both the training set (C-index = 0.73, 95

Nuclear protein in testis carcinoma (NUTc) is a rare and highly aggressive salivary gland tumor predominantly affecting young patients. It typically involves midline head and neck structures, progresses rapidly, and is associated with a median survival of 6 to 9 months, resulting in an 80

Research probing the clinical utility of immune checkpoint inhibitors (ICIs) supports the development of novel, efficacious approaches to treating colorectal cancer (CRC) patients without chemotherapy. Therapeutic innovations, however, need to be evaluated in light of real-world economic considerations. This study compared the cost-effectiveness of first-line dual-immunotherapies, single immunotherapy, and chemotherapies in the treatment of microsatellite-instability-high/mismatch repair deficient (MSI-H/dMMR) advanced CRC from the perspective of American healthcare systems. Individual patient data (IPD) from the KEYNOTE-177 and CheckMate-8HW trials were collected with IPDfromKM and used to develop a Markov model with a 30-year duration and three mutually exclusive health states, providing a framework for the evaluation of the cost-effectiveness of first-line nivolumab together with ipilimumab, pembrolizumab, and chemotherapy for treating MSI-H/dMMR advanced CRC. Primary outcomes consisted of total costs, life years (LYs), quality-adjusted LYs (QALYs), incremental cost-effectiveness ratio (ICER) values, and incremental net-health benefits (INHB) at willingness-to-pay (WTP) thresholds of 100,000/QALY in the USA. Nivolumab plus ipilimumab (482,416 [18.72 QALYs]) and pembrolizumab (336,617 [12.21 QALYs]) increased cost (effectiveness) by145,800 (5.30 QALYs) than chemotherapy (374,728 [10.07 QALYs]), respectively. The corresponding ICER was13,670/QALY and -14,768/QALY, with INHB of 2.52 and 6.80 QALYs, respectively. It was further found that the ICER and INHB of nivolumab plus ipilimumab versus pembrolizumab were22,386/QALY and 3.84 QALYs, respectively. In addition, the established model is stable. First-line immunotherapeutic treatments for MSI-H/dMMR advanced CRC cases in the USA appears to be cost-effective, with a dual-immunotherapeutic regimen consisting of nivolumab plus ipilimumab being preferable.

Breast cancer is one of the most prevalent malignancies worldwide. Modified radical mastectomy, as a conventional treatment for breast cancer, often leads to body image disturbance, which in turn can trigger negative psychosocial changes (such as anxiety, low self-esteem, and social withdrawal) and significantly impairs patients' long-term quality of life. This study compared the differences in quality of life (QoL) and psychosocial adaptability between patients who underwent modified radical mastectomy and those who received breast reconstruction, with the aim of elucidating the long-term effects of these two surgical procedures on patients' post-operative physical and mental well-being. Retrospective data were collected from breast cancer patients who underwent modified radical mastectomy or breast reconstruction at our institution between 2014 and 2020, and patients were assigned to the corresponding surgical groups. Propensity score matching was used to balance baseline characteristics (e.g., age, tumor stage, and education level) between the two groups. All included patients completed an online survey, which included the Functional Assessment of Cancer Therapy-Breast (FACT-B, a validated QoL scale with a total score range of 0-144, where higher scores indicate better QoL) and the Psychosocial Adaptation Questionnaire (PAQ, a scale evaluating psychosocial adaptability with a total score range of 0-100, where higher scores indicate stronger adaptability). Statistical analyses were performed using the Wilcoxon rank-sum test (for non-normally distributed data) and two-sample independent t-test (for normally distributed data). A total of 260 matched patients (130 in each group) were included in the final analysis. Compared with the modified radical mastectomy group, the breast reconstruction group showed significantly better outcomes: (1) FACT-B score: the reconstruction group had a mean score of (107.58 ± 16.2), while the mastectomy group had a mean score of (100.18 ± 8.5),P < .01.(2) The total PAQ scores of the two groups were 176 (163,186) and 164 (158,172), respectively, with statistically significant differences (P < .01). Subgroup analysis further confirmed that the advantages of breast reconstruction in QoL and psychosocial adaptability were consistent across patients with different post-operative follow-up periods (< 3 years, 3-5 years, > 5 years). Breast reconstruction was associated with enhanced self-acceptance and self-identity, reduced psychological burden, and improved physical condition, and help them achieve a better quality of life and psychosocial adaptability. These findings provide evidence for optimizing breast cancer treatment decision-making in clinical practice. Healthcare providers (including nurses, surgeons, and psychologists) should fully inform patients of the psychosocial and functional benefits of breast reconstruction, and develop personalized care plans to support patients in selecting surgical options that match their physical conditions and psychological needs, ultimately improving their long-term post-operative recovery outcomes.

BackgroundThe LUNAR trial demonstrated the significant efficacy and safety of Tumor Treating Fields (TTFields) plus standard-of-care (SOC) [immune checkpoint inhibitor (ICI) and docetaxel (DTX)] for patients with previously treated metastatic non-small cell lung cancer (mNSCLC). However, it remains uncertain as to whether the high costs are justified by the corresponding survival benefits. Here, the cost-effectiveness of using TTFields plus SOC for treating mNSCLC was evaluated from the perspective of the Chinese healthcare system.MethodsA Markov model with a 15-year time horizon was established and used to comparedeveloped to enable the simulation of treatment-associated costs and patient outcomes when comparing TTFields plus SOC to SOC alone. Primary outcomes for these analyses included total costs, life-years (LYs), quality-adjusted LYs (QALYs), and incremental cost-effectiveness ratio (ICER) values. The impact of paramere uncertainty on model outcomes was evaluated through sensitivity analyses. Additional subgroup and scenario analyses were also performed to extend these results.ResultsWhile TTFields plus SOC exhibited a $74,688 increase in total costs relative to SOC ($96,092 vs. $21,404), it was associated with 0.38 additional QALYs (1.08 vs. 0.82 QALYs) for an ICER of $284,490/QALY. This value exceeded the $35,983/QALY willingness-to-pay (WTP) threshold selected for these analyses by a wide margin. Relative to ICI and DTX treatment, the incremental costs of TTFields plus ICI and TTFields plus DTX were $78,115 and $71,307, respectively, with corresponding gains of 0.42 and 0.13 QALYs, yielding ICERs of $187,434/QALY, and $546,386/QALY. The parameter that most strongly impacted the results of these analyses was the cost of TTFields.ConclusionThe results indicated that given current treatment costs, TTFields plus SOC was insufficiently cost-effective in treating patients with mNSCLC in China, although TTFields plus ICI yields substantial health benefits.

Background: There is no standard management for small cell esophageal carcinoma (SCEC). The purpose of this multicenter, retrospective study (ChiSCER) was to investigate the treatment, outcomes, and risk factors impacting on survival endpoints in patients with limited-stage SCEC (LS-SCEC). Materials and Methods: Consecutive patients with LS-SCEC from 14 institutions between 2000 to 2020 in China were enrolled. Survival curves were constructed using the Kaplan-Meier method and compared by log-rank test. Univariate and multivariate Cox regression models and propensity score matching (PSM) analysis were adopted in prognostic analysis. Results were reported as hazard ratio (HR), 95% confidence interval (CI), and P value. Statistical significance was set as P value<0.05 in a two-tailed test. Results: Among 458 LS-SCEC patients, the median age was 63 (interquartile range [IQR], 57-68) years, 318 (69%) were males. Eighty-four (18%), 167 (36%), and 207 (45%) patients received chemotherapy (CT) alone, CT plus definitive radiotherapy (CT+RT), and CT plus radical surgery (CT+S), respectively. With a median follow-up time of 58.7 (95% CI, 48.9-68.6) months, the median OS and 3-year OS rate for all patients 24.3 (95% CI, 21.6-27) months and 37.3% (95% CI, 32.8%-42.5%), respectively. Multivariate analysis indicated that treatment modes, Karnofsky performance status (KPS), TNM stage, and CT cycle were independent prognostic factors for overall survival (OS) (P<0.05). Compared with CT alone, patients treated with CT+RT (HR, 0.57, 95% CI, 0.41-0.8, P=0.001) or CT+S (HR, 0.59, 95% CI, 0.42-0.82, P=0.002) had an improved OS, with no significant survival differences between CT+S and CT+RT groups after multivariate and PSM analyses (P>0.05). Subgroup analysis indicated that compared with CT+RT, patients with tumor location at lower 1/3 (HR, 0.59, 95% CI, 0.37-0.93, P=0.03) or tumor length>5 cm (HR 0.52, 95% CI, 0.3-0.9, P=0.02) could obtain significant OS benefit from CT+S. Patients with tumor location at middle 1/3 (HR 1.55, 95% CI, 1.03-2.36, P=0.04) or tumor length≤5 cm (HR 1.49, 95% CI, 1.02-2.17, P=0.04) favored CT+RT. Distant metastasis accounted for 73.7% of all treatment failures after multidisciplinary treatments. Conclusion: Surgery and RT were equally effective local therapies for patients with LS-SCEC. The personalized decision of local therapy should be made after comprehensive considerations on tumor location, length, comorbidities, and organ preservation.

Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively use the complementary information between the cross-modality MRI to improve PSP prediction is still a challenging task. To address this challenge, we propose a cross-modality feature interaction network for PSP prediction. Firstly, we propose a triple-branch multi-scale module to extract low-order feature representations and a skip-connection multi-scale module to extract high-order feature representations. Then, a cross-modality interaction module based on attention mechanism is designed to make the complementary information between cross-modality MRI fully interact. Finally, the high-order cross-modality interaction information is fed into a multi-layer perceptron to achieve the PSP prediction task. We evaluate the proposed network on a private dataset with 52 subjects from Hunan Cancer Hospital and validate it on a private dataset with 30 subjects from Xiangya Hospital. The accuracy of our proposed network on the datasets is 0.954 and 0.929, respectively, which is better than most typical convolutional neural network and interaction methods.

Mitochondria are crucial organelles in eukaryotic cells that maintain cellular homeostasis through various quality control mechanisms. One such mechanism, mitocytosis, is newly identified, yet its biological significance in cancer remains unclear. This study investigates the role of mitocytosis-related genes (MYO19, KIF5B, DNM1L, DYNLL2, TSPAN4, TSPAN9) in pan-cancer. We performed an extensive analysis of expression profiles and clinical data derived from samples in The Cancer Genome Atlas (TCGA) database. This study entailed a comparison of gene expression between malignant and normal tissues, an investigation into the correlation between gene expression and clinical prognosis through Kaplan-Meier and Cox regression analyses, and a functional enrichment analysis to elucidate the potential signaling pathways involved. We employed TIMER2.0 to assess the correlations between gene expression and immune infiltration levels in various cancers.Additionally, a nomogram predicted three- and five-year overall survival rates. The study also examined the influence of CTLA-4 and PD-1 statuses on Immunoscore (IPS) in KIRC patients, revealing that these genes are overexpressed in renal clear cell carcinoma and significantly associated with Clinical prognosis and drug response.

EDITORIAL article Front. Oncol., 18 August 2023Sec. Cancer Imaging and Image-directed Interventions Volume 13 - 2023 | https://doi.org/10.3389/fonc.2023.1257447

Introduction: The RATIONALE-309 trial confirmed the significant efficacy and safety of tislelizumab plus chemotherapy in patients with recurrent or metastatic nasopharyngeal carcinoma (R/M NPC). However, the economic benefits of this regimen are unclear. Therefore, this study aimed to evaluate the cost-effectiveness of adding tislelizumab to chemotherapy for R/M NPC from the perspective of the Chinese healthcare system.Methods: A Markov model was established to simulate the costs and outcomes of tislelizumab plus chemotherapy versus chemotherapy. The survival data came from the RATIONALE-309 trial. Only direct medical costs were considered, and utility values were referred to the literature. The incremental cost-effectiveness ratio (ICER) was used as the main outcome measure. Sensitivity analysis was performed to assess the effect of parameter uncertainty on the model. Additionally, subgroup analyses were performed.Results: The basic analysis showed that the cost of tislelizumab plus chemotherapy ($33,693) was $17,711 higher than that of chemotherapy ($15,982), but it also gained 1.05 QALYs more (2.72 QALYs vs. 1.67 QALYs), with an ICER of $16,859/QALY, which was lower than the willing-to-pay (WTP) of $36,289/QALY. The factors that most influenced the model were the utility of PD, the cost of tislelizumab, and the risk of platelet count decreased in tislelizumab plus chemotherapy group. The subgroup analysis also demonstrated that tislelizumab plus chemotherapy was cost-effective in the whole population regardless of EBV DNA level and PD-L1 expression level.Conclusion: Compared with chemotherapy alone, tislelizumab plus chemotherapy was cost-effective for the treatment of R/M NPC in China.