The emergence of effective immunotherapies has revolutionized therapies for many types of cancer. However, current immunotherapy has limited efficacy in certain patient populations and displays therapeutic resistance after a period of treatment. To address these challenges, a growing number of immunotherapy drugs have been investigated in clinical and preclinical applications. The diverse functionality of peptides has made them attractive as a therapeutic modality, and the global market for peptide-based therapeutics is witnessing significant growth. Peptides can act as immunotherapeutic agents for the treatment of many malignant cancers. However, a systematic understanding of the interactions between different peptides and the host’s immune system remains unclear. This review describes in detail the roles of peptides in regulating the function of the immune system for cancer immunotherapy. Initially, we systematically elaborate on the relevant mechanisms of cancer immunotherapy. Subsequently, we categorize peptide-based nanomaterials into the following three categories: peptide-based vaccines, anti-cancer peptides, and peptide-based delivery systems. We carefully analyzed the roles of these peptides in overcoming the current barriers in immunotherapy, including multiple strategies to enhance the immunogenicity of peptide vaccines, the synergistic effect of anti-cancer peptides in combination with other immune agents, and peptide assemblies functioning as immune stimulators or vehicles to deliver immune agents. Furthermore, we introduce the current status of peptide-based immunotherapy in clinical applications and discuss the weaknesses and future prospects of peptide-based materials for cancer immunotherapy. Overall, this review aims to enhance comprehension of the potential applications of peptide-based materials in cancer immunotherapy and lay the groundwork for future research and clinical applications.

Background: Diabetes is prevalent among patients with hepatocellular carcinoma (HCC) and is associated with a poor prognosis. Although the hypoglycemic drug metformin has shown antitumor effects, its potential positive effect on patients with HCC and diabetes undergoing transarterial chemoembolization (TACE) remains unclear. Therefore, this study aimed to investigate the efficacy and safety of metformin in patients with HCC and type II diabetes who are receiving TACE. Materials and methods: This retrospective study involved 372 consecutive patients with HCC and type II diabetes across three medical centers between January 2014 and June 2021. All patients underwent TACE. Propensity score matching (PSM) was used to reduce selection bias. Cox proportional hazards regression was employed to compare all-cause death between the metformin and nonmetformin groups while competing risk regression was performed to assess cancer-specific death. Results: Among 372 patients included in the study, 208 patients (177 male patients and 31 female patients) with a mean age of 59.6 (10.3) years received metformin, and 164 patients (139 male patients and 25 female patients) with a mean age of 60.3 (10.0) years did not. Before PSM, patients with metformin had significantly longer median overall survival (mOS) and median progression-free survival (mPFS) than those without metformin (mOS: 34 months, 95% CI: 25.6–42.4 vs. 20 months, 95% CI: 15.3–24.7; P <0.001; mPFS: 11 months, 95% CI: 9.3–12.7 vs. 8 months, 95% CI: 5.9–10.1; P <0.001). Similar results were observed after PSM. Multivariate regression analysis indicated that metformin was associated with a reduced risk of all-cause mortality (HR: 0.589, 95% CI: 0.454–0.763; P <0.001) and tumor progression (HR: 0.667, 95% CI: 0.526–0.845; P =0.001) before PSM. After excluding deaths related to other factors, metformin continued to demonstrate a reduction in cancer-specific mortality risk among the patients. Subgroup analysis further revealed that patients using metformin had lower all-cause mortality risk and tumor progression risk than those without metformin in most subgroups. Adverse event evaluation suggested that metformin could lead to elevated nausea incidence. Conclusion: Metformin may confer survival benefits to patients with HCC and type II diabetes undergoing TACE. Metformin may simultaneously address multiple aspects of treatment in these patients.

A global shortage of radiologists has increased the burden of chest X-ray interpretation, particularly in primary and resource-limited settings. Although artificial intelligence systems can assist with report generation, most lack rigorous prospective validation in real clinical environments. Here we show that Janus-Pro-CXR, a lightweight artificial intelligence system optimized for chest radiograph interpretation, improves report quality and workflow efficiency in a multicenter prospective study (NCT07117266). Developed through domain-specific fine-tuning of a multimodal foundation model, Janus-Pro-CXR achieved strong diagnostic performance for key thoracic findings and generated clinically structured reports aligned with expert standards. In real-world deployment involving 296 patients, AI assistance significantly improved report quality scores and reduced interpretation time by 18.3% compared with standard practice. The system operates efficiently on standard hardware, supporting practical implementation in resource-constrained settings. These findings demonstrate the clinical value of lightweight, human–AI collaborative systems in radiology practice.

Objective: To construct a radiomics-based model for predicting postoperative recurrence in hepatocellular carcinoma (HCC) patients with vessels encapsulating tumor clusters (VETC) positive based on CT scan. Methods: This retrospective study enrolled patients who underwent surgical resection between January 2016 and January 2024 at Union Hospital, with pathologic confirmation of HCC and VETC status. An external test set was drawn from Chegu Hospital, covering January 2018 to January 2022. Tumor segmentation was performed on portal venous phase CT scan, and then radiomics features were extracted. These features were further analyzed using the LASSO algorithm and combined with clinical features to construct a radiomics-clinical combination model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Patients were divided into high- and low-risk groups based on model scores, and Kaplan-Meier (KM) curves were compared. Results: A total of 243 patients were included (median age 56.7 years, 211 males). Nine radiomics features and two clinical features were selected to construct the combined model. The area under the ROC curve (AUC) for predicting 1-year recurrence was 0.898 (95% CI: 0.797– 0.999) in the internal test set and 0.804 (95% CI: 0.641– 0.967) in the external test set. Calibration curves and DCA demonstrated high net clinical benefit of the combined model. The median recurrence-free survival (RFS) of patients in the high-risk group was significantly lower than that in the low-risk group (internal test set: 13.5 vs 30.0 months, respectively. P=0.004; external test set: 13.0 vs 31.0 months, respectively. P< 0.0001). Conclusion: The radiomics-clinical combination model showed high accuracy for preoperatively predicting recurrence in patients with VETC-positive HCC receiving hepatectomy.