Glioblastoma remains a challenging disease to approach with immunotherapy due to pronounced antigen heterogeneity, immunosuppressive tumor microenvironment, and barriers to effective molecule delivery within the central nervous system. T-cell engagers provide an off-the-shelf approach to redirect endogenous T cells toward tumor cells. However, bispecific formats are constrained by intra- and interpatient antigen heterogeneity, which can limit therapeutic efficacy. In this review, we examine trispecific T-cell engagers (TriTEs) as an emerging strategy to address this limitation by simultaneously targeting multiple tumor-associated antigens. We discuss principles guiding antigen selection in glioblastoma, summarize available preclinical evidence supporting multispecific engagement, and outline key design considerations, including molecular architecture, stability, half-life extension, and safety optimization. We further review delivery strategies, such as gene-encoded expression, cellular carriers, and blood-brain barrier modulation, that may improve tumor access and the durability of TriTEs. Together, these considerations position TriTEs as a modular immunotherapy platform relevant to glioblastoma and other heterogeneous solid tumors.

Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers.

A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligible studies reported diagnostic accuracy metrics for AI models, with clinician comparator data extracted when available. A bivariate random-effects model was used to pool sensitivity, specificity, and AUC values. Subgroup analyses were conducted to examine diagnostic performance across different cancer types and imaging modalities, and to explore potential sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool.

A total of 110 studies were included in the meta-analysis. AI models achieved pooled sensitivity and specificity of 0.85 (95% CI: 0.83-0.87) and 0.83 (95% CI: 0.80-0.86), with an AUC of 0.91 (95% CI: 0.88-0.93). Clinicians demonstrated a pooled sensitivity of 0.82 (95% CI: 0.79-0.85) and specificity of 0.68 (95% CI: 0.62-0.73), with an AUC of 0.83 (95% CI: 0.80-0.86). Subgroup analyses indicated that AI models showed overall diagnostic advantages across cancer types and imaging modalities, particularly in specificity, AUC, and diagnostic odds ratios, although clinicians demonstrated higher sensitivity in the prostate cancer and MRI subgroups.

AI models demonstrate strong diagnostic performance across various urological cancers and imaging modalities, showing potential as supportive tools in radiological workflows. Further prospective, standardized, and multi-center evaluations are warranted to confirm AI's clinical utility across diverse diagnostic tasks in urological oncology.

Craniopharyngioma is histologically benign yet locally aggressive, with frequent recurrence. Long-term multicenter outcomes after stereotactic radiosurgery (SRS) remain incompletely defined.

We performed a retrospective multi-institutional cohort study through the International Radiosurgery Research Foundation including 296 patients from 13 centers. Median age at first SRS was 33.6 years. Median tumor volume was 1.32 cm³ and median margin dose was 12.0 Gy. The primary endpoint was local control (LC); secondary endpoints were progression-free survival (PFS) and overall survival (OS). Kaplan-Meier methods estimated outcomes, and Cox proportional hazards models evaluated predictors of LC.

Actuarial 1-, 5-, and 10-year LC was 93.5%, 76.2%, and 70.1%. Actuarial 1-, 5-, and 10-year OS was 98.2%, 93.6%, and 85.2%, and PFS was 92.4%, 73.6%, and 64.8%. Mixed solid-cystic phenotype had worse LC than non-mixed tumors (log-rank p = 0.025); non-mixed phenotype remained independently associated with improved LC (HR 0.53, p = 0.026). Visual fields improved in 10%, were unchanged in 86%, and deteriorated in 4%; visual acuity improved in 6%, was unchanged in 91%, and worsened in 3%. Ten-year freedom from endocrine deterioration was 96.7%. Diabetes insipidus improved in 6.3%, worsened in 0.5% and other pituitary dysfunction was noted in 2.6%, CONCLUSION: In this international multi-institutional experience, SRS achieved durable long-term control with favorable survival and low incidence of visual and endocrinologic dysfunction. Mixed phenotype was an important determinant of LC.

Not applicable.

Cancer mortality remains high in part because tumor-immune interactions can be unpredictable and may exhibit multistability, making malignant progression difficult to anticipate. We develop and analyze a tumor-immune model incorporating chemotherapy-induced toxicity and a Norton-Simon type tumor response, and show via deterministic analysis and continuation that increasing toxicity can generate hysteresis and bistability separating tumor dormancy from an uncontrolled full-growth state, so that small perturbations may precipitate abrupt progression. To capture uncertainty, we study environmental and demographic stochasticity and observe noise-induced switching in both cases; however, demographic noise sustains higher resilience of the tumor-dominant state under bistability, whereas stronger environmental noise tends to suppress uncontrolled tumor growth. We further assess early-warning signals using single and composite rolling-window indicators and find that they can provide advance warning of transitions from dormancy to full growth, with composite measures offering greater robustness across noise levels. Finally, we formulate an optimal control problem for combination immunotherapy and radiotherapy and demonstrate that appropriately timed treatment can substantially reduce tumor burden when initiated from either dormancy or full-growth conditions, highlighting how stochasticity-aware monitoring and optimized interventions may help prevent catastrophic tumor progression.

Exosome-mediated delivery of small interfering RNA (siRNA) has emerged as a promising therapeutic strategy for cancer treatment, offering precise gene silencing with minimal off-target effects. Exosomes, naturally secreted extracellular vesicles, provide biocompatible carriers that protect siRNA from enzymatic degradation and facilitate efficient uptake by tumor cells. Their natural tropism, driven by surface proteins such as integrins and tetraspanins, promotes cellular adhesion and interactions within the tumor microenvironment, facilitating the delivery of therapeutic cargo. Preclinical studies have demonstrated that exosome-delivered siRNAs can suppress oncogenes, inhibit tumor growth, reverse chemoresistance, and modulate immune responses by targeting stromal and immune components. Engineering approaches, including surface functionalization and hybrid exosome-nanoparticle systems, further enhance stability, payload capacity, and tumor-homing efficiency. Combination strategies with chemotherapy, immunotherapy, or phototherapy have shown synergistic effects, allowing simultaneous inhibition of survival pathways, promotion of apoptosis, and remodeling of the immunosuppressive microenvironment. Early-phase clinical studies indicate safety, effective biodistribution, and functional gene silencing, highlighting the translational potential of exosome-mediated siRNA therapeutics. Challenges such as scalable production, cargo heterogeneity, and regulatory considerations remain, but ongoing advances in exosome engineering and patient-derived vesicles are poised to overcome these barriers. This review aims to comprehensively summarize the current state, therapeutic applications, and translational prospects of exosome-mediated siRNA delivery in cancer.

Describe the implementation of robotic-assisted surgery for pediatric cysts and neoplasms and characterize perioperative outcomes.

A retrospective review was performed of pediatric patients undergoing robotic-assisted resection of cysts or neoplasms at a quaternary children's hospital from 2011 to 2026. Demographic, lesion, operative, and outcome data were analyzed.

Thirty-eight pediatric robotic cases were reviewed. Median age was 13 years (IQR 8.25-16). Eighteen intracavitary cysts and 20 neoplasms were resected. Operations for solid neoplasms consisted of thymectomies (n = 3), mediastinal teratoma excision, lung lobectomy, partial gastrectomy, left hepatectomy, distal pancreatectomy (n = 2), pancreatic uncinate resection, pancreaticoduodenectomy, nephrectomies (2 partial, 2 radical), adrenalectomy, periadrenal mass resection, ovarian cystectomy (n = 2), and radical cystoprostatectomy. Median EBL was 15 mL (IQR 5-25) and day of discharge 1 (IQR 0-2). The median opioid dose prescribed at discharge was 0.28 mg/kg oral morphine equivalents with duration of 1 (0-3) day. Thirty-one (82%) patients experienced no complications. Major complications occurred after pancreatic resections, including hemorrhagic pancreatitis following uncinate resection and delayed biliary stenosis with ductal leak after pancreaticoduodenectomy requiring revision 1.5 years later.

Robotic-assisted surgery is a safe and feasible option for selected pediatric cysts and neoplasms, offering low blood loss, short hospitalization, and minimal postoperative opioid requirements.

To assess the risk of suicide after a diagnosis of urinary bladder cancer (UBC) compared with the standard population and to identify potential risk factors and time dependence associated with suicide in patients with UBC.

We used data from the Bladder Cancer Data Base Sweden 2.0 (BladderBaSe), including all individuals aged 18 years or older, diagnosed with UBC between 1997 and 2019. Suicides were identified using ICD-10 codes X60-X84. We compared the number of suicides identified in BladderBaSe with the expected number in a Swedish standard population of the same age and sex distribution during the same period.  Outcome measurements: The risk of suicide was determined using standardized mortality ratios (SMR) with 95% confidence intervals (CI).

Among 53,298 patients with UBC included in our study, 106 suicides were identified compared with an expected 66 in the standard population (SMR: 1.6; 95% CI: 1.31-1.93). The risk of suicide was highest within the first 4 weeks after diagnosis (SMR: 4.66; 95% CI: 2.48-7.97), in patients with advanced UBC (SMR: 5.64; 95% CI: 2.06-12.3), those receiving palliative treatment (SMR: 5.03; 95% CI: 2.51-9.01), and those living alone (SMR: 2.87; 95% CI: 2.22 - 3.66).

The suicide risk among UBC patients is significantly elevated and shaped by multiple factors, with the highest risk observed in the first 4 weeks from diagnosis. Healthcare providers should maintain heightened vigilance for suicidal ideation, particularly during the critical period immediately following diagnosis, to ensure timely support and intervention.

Differentiated thyroid carcinoma (DTC) in children is a rare malignancy characterized by a high propensity for regional and distant metastases yet generally associated with a favorable long-term prognosis. Radioiodine-refractory (RAIR) disease represents a distinct clinical challenge, accounting for approximately 10-30% of pediatric DTC cases, and warrants comprehensive investigation of its clinical course, prognostic factors, and therapeutic options. This study aimed to perform an integrated assessment of treatment outcomes in children undergoing combined therapy for DTC (surgery and radioiodine therapy, RAI), with a particular focus on advanced and RAIR disease.

We retrospectively analyzed medical records of 278 patients aged 5-18 years who underwent primary surgical treatment between 2008 and 2022, followed by one or more courses of RAI at the Endocrinology Research Centre (Moscow, Russia) from December 2015 to March 2024. The study included patients with advanced disease (high risk of recurrence at diagnosis) fulfilling at least one RAIR criterion, with a median follow-up of 48.0 months [21.5; 62.0].

Among 278 patients, 39 (14%) were diagnosed with advanced disease. Of these, 4 achieved remissions, 29 had stable disease, and 6 experienced biochemical and/or structural progression. Progression-free survival in the RAIR cohort was 85%, while the 5-year overall survival reached 100%.

Based on study findings, we propose a novel classification system integrating both the baseline ability of metastases to accumulate ¹³¹I and the dynamic response to RAI (progression vs. stabilization). This framework is designed to optimize treatment and follow-up algorithms. The management of RAIR pediatric DTC should remain balanced: avoiding overtreatment in stable disease, while ensuring timely initiation of modern systemic therapies in patients with risk factors for progression.

The evidence linking occupational exposure to lead and cancer in humans is suggestive. Existing studies examine individual exposure to lead, ignoring the potential joint effects of mixed metal exposure.

We analyzed the data of a case-control study of lung cancer conducted in seven European countries and comprising 2,861 cases and 2,936 controls, with a detailed assessment of occupational exposure to arsenic, cadmium, chromium (VI), and lead, to estimate the odds ratio (OR) of lung cancer for combined exposure to lead and these other carcinogenic metals, after adjustment for potential confounders.

The results suggested a synergism between lead and arsenic or cadmium: ORs were around 1.0 for lead alone and above 2.0 for combined exposure. No interaction was suggested between lead and chromium (VI).

Our results provide suggestive evidence of a possible interaction between occupational exposure to lead, arsenic, or cadmium and lung cancer risk, and highlight the importance of considering mixed metal exposures in occupational epidemiology.

While the radiation dose to left anterior descending (LAD) artery is associated with overall survival (OS) in NSCLC patients, manual segmentation is labor-intensive. Several auto-segmentation models were developed on average 4DCT with promising geometric agreement, yet their feasibility in clinical outcome analysis on lung CT remains unclear.

This study evaluates whether automated LAD segmentation can reproduce, at the group level, the established dose-survival association in a multi-institutional clinical trial dataset.

Three deep learning-based auto-segmentation (DLAS) models for automatic segmentation of the LAD coronary artery were applied to 460 patients from the NRG Oncology/RTOG 0617 dataset. Kaplan-Meier curves and Cox proportional hazard ratio (HR) were calculated using each model's segmentation. The LAD V15 Gy was used to stratify patients into high-and low-risk groups, and the cut-off values were determined by minimizing mean absolute error (MAE) between DLAS-derived and manual OS curves.

All three models demonstrated significantly different OS curves between the high risk and low risk groups (p < 0.05). The HR was 1.40 (95% CI: 1.02-1.93; p = 0.0381; MAE = 7.1%), 1.32 (95% CI: 1.03-1.68; p = 0.0258; MAE = 9.6%), and 1.37 (95% CI: 1.03-1.84; p = 0.0331; MAE = 7.9%) for the three models using a common V15 Gy ≥ 10% cut-off. After optimizing the cut-off, one model aligned well with what was derived from manual segmentation (MAE = 4.3%).

Among the evaluated models, one DL model showed the strongest performance of survival groups separation and achieved group-level overall survival stratification comparable to that previously reported using manual contours in the multi-institutional cohort. Further validation with paired manual contours and pre-specified cutoffs is warranted.