Background Metastatic castration-resistant prostate cancer (mCRPC) is a lethal disease with limited treatment options. While Radium-223 dichloride (223Ra) is the only approved α-emitter improving overall survival (OS) in bone-dominant mCRPC, its efficacy is restricted to osseous lesions. Actinium-225 ([225Ac]Ac-PSMA), a PSMA-targeted α-emitter, shows promise in treating both bone and soft-tissue metastases but has limited data as a first-line radiopharmaceutical therapy (RPT). This systematic review and meta-analysis presents separate, noncomparative pooled summaries of the efficacy and safety profiles of [225Ac]Ac-PSMA and 223Ra as the initial RPT in RPT-naive patients with mCRPC. Methods Following PRISMA guidelines, PubMed/Medline, EMBASE, and Cochrane Library were searched (up to January 18, 2026). 13 eligible studies included prospective/retrospective trials of 1492 RPT-naive mCRPC patients receiving [225Ac]Ac-PSMA or 223Ra. Data on OS, PSA50 response, and adverse events (AEs) were analyzed. Results The pooled median OS was 16.76 months for [225Ac]Ac-PSMA and 16.32 months for 223Ra. The pooled PSA50 response rate was 0.74 in the [225Ac]Ac-PSMA group and 0.07 in the 223Ra group. Regarding AEs, [225Ac]Ac-PSMA was primarily characterized by xerostomia, whereas 223Ra commonly caused anemia and anorexia. Findings are limited by retrospective designs, small sample sizes in the [225Ac]Ac-PSMA cohort, and baseline heterogeneity. Conclusions In RPT-naive mCRPC patients, the PSA50 response and survival profiles presented in this study represent separate, noncomparative pooled literature summaries rather than a direct comparison of clinical superiority between [225Ac]Ac-PSMA and 223Ra. Due to inherent data limitations and baseline imbalances in the existing literature, prospective randomized controlled trials with aligned patient characteristics, such as trials only involving symptomatic bone metastases patients, are urgently needed to evaluate and compare the efficacy and safety profile of the two α-emitters as well as offer evidence for individualized application of RPT.
Objective To establish a prediction model for late-onset urotoxicity after radiotherapy for cervical cancer based on bladder dosimetric parameters and clinical features, and to evaluate the predictive contribution and adjusted associations of dosimetric parameters using SHapley Additive exPlanations (SHAP) and double machine learning (DML). Methods This single-center retrospective study included 258 patients treated from June 2014 to December 2015. Late-onset urotoxicity was defined as urinary adverse reactions occurring ≥ 3 months after radiotherapy. Clinical data and bladder dose-volume histogram (DVH) parameters, including Bladder_V40, Bladder_V45, Bladder_V50, Bladder_Dmean, and Bladder_D2cc (expressed as EQD2), were collected. Variables were selected using univariate Logistic regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by multivariate Logistic regression. Model performance was evaluated using receiver operating characteristic (ROC) curve, Bootstrap validation, calibration, Brier score, and decision curve analysis. Results Late-onset urotoxicity occurred in 65 patients (25.2%). Patients with urotoxicity had higher bladder dosimetric parameters. Multivariate Logistic regression identified Bladder_D2cc as an independent predictor [odds ratio= 1.61, 95% confidence interval (95% CI): 1.42–1.83, P < 0.001]. The model showed good discrimination, with an area under the ROC curve (AUC) of 0.943 and a Bootstrap mean AUC of 0.953 (95% CI: 0.928–0.974). Calibration and Brier score (0.083) indicated good agreement and clinical benefit. SHAP showed that Bladder_D2cc contributed most to prediction, while DML suggested positive adjusted associations between all bladder dosimetric parameters and late-onset urotoxicity, especially Bladder_V50. Conclusion This model effectively identified high-risk patients after cervical cancer radiotherapy. Bladder_D2cc was retained as a representative high-dose predictor and showed robust predictive performance, while Bladder_V50 yielded the largest standardized DML association estimate.
Background Axillary lymph node metastasis of breast cancer is vital for early treatment decision-making, but puncture biopsy may induce a series of adverse reactions in patients. This study aimed to noninvasively identify axillary lymph node metastasis (ALNM) using a deep learning radiomics nomogram model based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI). Methods Clinical and MRI data of 396 patients with invasive breast cancer (2018–2024) were retrospectively analyzed and split into training (n = 277) and test (n = 119) sets. Radiomics and ResNet50 deep learning features were extracted and selected via LASSO regression model. Eight models were constructed and interpreted by SHAP and Grad-CAM. Multivariate regression was performed to identify independent risk factors for ALNM. A (Deep Learning Radiomics) DLR-score-based nomogram was developed and assessed by area under the curve (AUC), sensitivity, specificity and accuracy. Results The clinical model yielded AUCs of 0.704 and 0.642, the radiomics model 0.813 and 0.804, and the DLR model 0.903 and 0.816 in the training and test sets, respectively. Carcinoma in situ, tumor location and pathological type were independent risk factors for axillary lymph node metastasis. The integrated nomogram achieved an AUC of 0.921, sensitivity of 0.821, specificity of 0.870, and accuracy of 0.848 in the training set, and 0.834, 0.821, 0.714, and 0.765 in the test set. Conclusion The nomogram based on DCE-MRI deep learning radiomics can effectively predict ALNM in breast cancer patients.
Background Head and neck squamous cell carcinoma (HNSC) is a frequent malignant tumor. Identifying molecular biomarkers associated with radiosensitivity and prognosis is crucial to guide personalized treatment strategies for HNSC patients. Objectives This study aimed to identify radioresistance-related genes and establish a gene-based prognostic model for HNSC patients. Methods Differentially expressed genes (DEGs) between radiosensitive and radioresistant samples were identified based on the GEO and TCGA-HNSC datasets, and functional annotation was performed on these DEGs. From the DEGs, six key genes (JAK2, FAM172A, TXLNA, PLAA, MED18, MYO5A) were selected via univariate Cox, LASSO, and stepwise regression to construct a prognostic risk model. The model was evaluated using Kaplan-Meier (KM) analysis, ROC curves, repeated training-validation splits, and multivariate Cox regression analyses. Based on the risk model, patients were divided into high- and low-risk groups, and CIBERSORT, ESTIMATE, TIDE, and pRRophetic algorithms were used to compare immune microenvironment characteristics, immunotherapy-related features, and predicted drug sensitivity between the two risk groups. In vitro experiments were performed to further assess the effects of MYO5A silencing on HNSC cell phenotypes. Results DEGs were enriched in cell cycle and mitosis pathways. Patients in the high-risk group had significantly worse PFS and OS (p < 0.05), and repeated internal validation showed stable prognostic performance of the model. High-risk patients showed lower ImmuneScore and reduced infiltration of CD8 + T-cells and B-cells (p < 0.05). TIDE analysis demonstrated differences in immune exclusion and dysfunction scores between the two risk groups (p < 0.05), whereas the overall TIDE score did not differ significantly. Drug sensitivity prediction showed that low-risk patients had lower IC50 values for rapamycin (p < 0.05). MYO5A silencing markedly suppressed the malignant phenotype of HNSC cells (p < 0.05). Conclusions This study established a six-gene prognostic model based on genes related to radioresistance in HNSC. The model was associated with patient prognosis, immune microenvironment characteristics, and predicted drug sensitivity. These findings may provide a potential framework for prognostic stratification in HNSC, although further external validation and radiotherapy-focused functional studies are required.
Background Radiotherapy is crucial for treating cancer but commonly causes radiation-induced skin injuries in up to 95% of patients. Objective This study evaluates the anti-inflammatory and antioxidant effects of a chitosan-based hydrogel loaded with Nano-curcumin liposomes in a rat model of electron radiation-induced skin damage. Methods Forty-two male rats were randomly assigned to seven groups (n = 6 per group): control, radiation-only, radiation plus curcumin ointment, radiation plus empty hydrogel, and radiation plus Nanocurcumin hydrogel at three doses. Radiation groups received 6 Gy of 6 MeV electrons. Skin samples were collected 48 h post-irradiation for oxidative stress and histology. Histopathology, cytokine analysis, and antioxidant parameters were assessed to determine efficacy, with data analyzed by ANOVA or the Kruskal-Wallis test, according to data normality. Results Radiation significantly increased malondialdehyde and interleukin-1β (P < 0.001) while reducing glutathione and superoxide dismutase (P < 0.001). These markers were accompanied by marked histological changes. All treatment groups (empty hydrogel, curcumin ointment, and Nanocurcumin hydrogel at three doses) inhibited glutathione reduction (P < 0.001) and IL-1β elevation (P < 0.05). Only the 10 mg Nanocurcumin hydrogel significantly increased superoxide dismutase activity and reduced malondialdehyde (P < 0.001), suggesting leading to improved oxidative balance. Histological evaluations indicated favorable outcomes for the hydrogel containing 10 mg. Conclusion Nanocurcumin hydrogel significantly ameliorated oxidative stress and inflammation. Findings suggest its potential as a short-term protective agent against radiation-induced skin injury and radiotherapy-associated dermatitis.
