Search Results (364)

Urban perception research has long focused on global metropolises, but has overlooked many cities with complex functions and spatial structures, resulting in insufficient universality of existing theories when facing diverse urban contexts. This study constructed an analytical framework that integrates street scene images and interpretable machine learning. Taking Zhengzhou City as the research object, it extracted street visual elements based on deep learning technology and systematically analyzed the formation mechanism of multi-dimensional urban perception by combining the LightGBM model and SHAP method. The main findings of the research are as follows: (1) The urban perception of Zhengzhou City shows a significant east–west difference with Zhongzhou Avenue as the boundary. Positive perceptions such as safety and vitality are concentrated in the central business district and historical districts, while negative perceptions are more common in the urban fringe areas with chaotic built environments and single functions. (2) The visibility of greenery, the openness of the sky and the continuity of the building interface are identified as key visual elements affecting perception, and their directions and intensifies of action show significant differences due to different perception dimensions. (3) The influence of visual elements on perception has a complex mechanism of action. For instance, the promoting effect of greenery visibility on beauty perception tends to level off after reaching a certain threshold. The research results of this study can provide quantitative basis and strategic reference for the improvement in urban space quality and humanized street design. Full article

This study employed machine learning to investigate the mechanical behavior of one-part geopolymer (OPG)-stabilized soil subjected to acid erosion. Based on the unconfined compressive strength (UCS) data of acid-eroded OPG-stabilized soil, eight machine learning models, namely, Adaptive Boosting (AdaBoost), Decision Tree (DT), Extra Trees (ET), Gradient Boosting (GB), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), along with hyper-parameter optimization by Genetic Algorithm (GA), were used to predict the degradation of the UCS of OPG-stabilized soils under different durations of acid erosion. The results showed that GA-SVM (R² = 0.9960, MAE = 0.0289) and GA-XGBoost (R² = 0.9961, MAE = 0.0282) achieved the highest prediction accuracy. SHAP analysis further revealed that solution pH was the dominant factor influencing UCS, followed by the FA/GGBFS ratio, acid-erosion duration, and finally, acid type. The 2D PDP combined with SEM images showed that the microstructure of samples eroded by HNO₃ was marginally denser than that of samples eroded by H₂SO₄, yielding a slightly higher UCS. At an FA/GGBFS ratio of 0.25, abundant silica and hydration products formed a dense matrix and markedly improved acid resistance. Further increases in FA content reduced hydration products and caused a sharp drop in UCS. Extending the erosion period from 0 to 120 days and decreasing the pH from 4 to 2 enlarged the pore network and diminished hydration products, resulting in the greatest UCS reduction. The results of the study provide a new idea for applying the ML model in geoengineering to predict the UCS performance of geopolymer-stabilized soils under acidic erosion. Full article

In inland waters, Atmospheric Correction (AC), including Adjacency Effect (AE) correction, is a major challenge for water quality retrieval using optical satellite data. This study evaluated three image pre-processing options for turbidity retrieval in the St. Lawrence River using Sentinel-2 (S2) and Landsat 8/9 (L8/9) imagery with the Light Gradient Boosting Machine (LightGBM) model: (1) No pre-processing, i.e., use of Top-of-Atmosphere (TOA) reflectance, (2) AC pre-processing, obtaining water-leaving reflectance (R_w) from AC for the Operational Land Imager lite (ACOLITE)’s Dark Spectrum Fitting (DSF) technique, and (3) AE pre-processing, correcting for the AE using T-Mart before obtaining R_w from DSF. Results demonstrated that AE pre-processing outperformed the other two options. For L8/9, AE pre-processing reduced the Root Mean Square Error (RMSE) and improved the median symmetric accuracy (ε) by 48.8% and 19.0%, respectively, compared with AC pre-processing, and by 48.5% and 50.7%, respectively, compared with No pre-processing. For S2, AE pre-processing performed better than AC pre-processing and also outperformed No pre-processing, reducing RMSE by 28.4% and ε by 50.8%. However, No pre-processing yielded the lowest absolute symmetric signed percentage bias (|β|) among all pre-processing options. Analysis indicated that AE pre-processing yielded superior performance within 0–300 m from shore than other options, where the AE influence is strongest. Turbidity maps generated using AE pre-processing were smoother and less noisy compared to the other pre-processing options, particularly in cloud-adjacent regions. Overall, our findings suggest that incorporating AE correction through T-Mart improves the performance of the LightGBM model for turbidity retrieval from both L8/9 and S2 imagery in the St. Lawrence River, compared to the alternative pre-processing options. Full article

This study explored radiomic features that help identify non-contrast-enhancing tumors (nCET) by analyzing regions where contrast-enhancing tumors (CET) transformed into nCET after Bevacizumab (BEV) treatment. The BEV cohort included 24 recurrent GBM (rGBM) patients treated with BEV, showing reduced contrast-enhancement on gadolinium-enhanced T1-weighted imaging (T1Gd) imaging. The 11C-methionine positron emission tomography (Met-PET) cohort consisted of 24 newly diagnosed GBM (nGBM) patients with available Met-PET data. VOIs were created from T2WI, FLAIR, T1Gd, and Met-PET to analyze nCET and T2/FLAIR lesions. After significant radiomic features were identified, a prediction model for nCET was developed in the BEV cohort and subsequently evaluated in the Met-PET cohort. A total of 37 and 46 significant radiomic features were found in the BEV and Met-PET cohorts, respectively. The key feature, T2WI_whole_GLCMcorrelation_1, was selected for predictive modeling. The model demonstrated high accuracy (AUC = 0.93, p < 0.0001) in the BEV cohort, with sensitivity and specificity of 0.91, while the Met-PET cohort showed moderate accuracy (AUC = 0.74, p = 0.0053). Image reconstruction using these features also effectively visualized nCET in nGBM. These findings suggest that radiomic features in CET regions transforming to nCET after BEV treatment harbors valuable information for identifying nCET in GBM. Full article

Glioblastoma (GBM) is the most aggressive primary cancer with poor survival. In the absence of an effective treatment and a high probability of recurrence, new therapeutic approaches are urgently needed. This study focused on targeting microRNA-10b (miR-10b) highly expressed in GBM cells that has been identified as one of the key drivers of GBM progression. Inhibiting miR-10b using antisense oligonucleotides (ASOs) has shown promise, but its delivery is challenging due to short circulation half-life, degradation by nucleases, and limited blood–brain barrier (BBB) permeability. To overcome these barriers, we employed a magnetic nanoparticle (MN) platform to deliver anti-miR-10b ASOs (MN-anti-miR10b). In addition to serving as a delivery vehicle, these nanoparticles can be used for monitoring delivery using magnetic resonance imaging (MRI). In therapeutic studies in orthotopic models of GBM presented here we used MN-anti-miR10b as well as TTX-MC138, a clinically tested anti-miR10b nanotherapeutic now in Phase I trials in patients with solid (non-GBM) cancers. Both formulations showed efficient delivery, as demonstrated by imaging and improved survival, leading to target inhibition and increased apoptosis. This approach may offer a novel strategy for delivering therapeutics to GBM and improving patient outcomes in one of the most aggressive and treatment-resistant forms of brain cancer. Full article

Cereal leaf beetle (CLB, Oulema melanopus L.) is an important pest that damages cereals. Insecticide use against CLB could be reduced with targeted treatments. Our aims were to develop a methodology to map CLB damage on cereal fields using remote sensing. We investigated the suitability of four vegetation indices (VIs: the Visible Atmospherically Resistance Index (VARI), the Green Chromatic Coordinate (GCC), the Green Leaf Index (GLI), and the Normalized Green–Red Difference Index (NGRDI)) derived from RGB images (drone (UAV) imagery). Study sites were located in different regions of Hungary in 2024. Images were taken at different phenological stages of cereals. Suitability of VIs was analyzed with ANOVA and MANOVA. Machine learning models were developed to classify damaged field sections with random forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms. Results show that VARI, GCC, GLI, and NGRDI contain complementary features for early detection of CLB damage. Difference in sample points’ VI from field median is advantageous for the LGBM algorithm (F1_damaged = 0.64–0.72), while the best RF models were obtained with more features (F1_damaged = 0.66). Random test data splits had optimistic results (overall accuracy: RF = 0.63–0.80, LightGBM = 0.63–0.79) compared to spatially controlled test splits (overall accuracy: RF = 0.53–0.70, LightGBM = 0.53–0.62). Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

Background: Necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disorder in neonates, particularly preterm infants. Early identification of infants requiring surgical intervention remains challenging due to nonspecific clinical manifestations and rapid disease progression. Methods: We conducted a retrospective cohort study of 320 preterm infants with NEC (gestational age <37 weeks) who were admitted to the NICU of the Capital Center for Children’s Health, Capital Medical University, Beijing, China, between June 2017 and December 2024. Forty-three clinical, laboratory, and imaging variables were collected. Feature selection was performed using LASSO regression and the Boruta algorithm. Four machine learning (ML) models—LightGBM, XGBoost, Random Forest, and Neural Network—were constructed. Model performance was evaluated using ROC-AUC, PR-AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and SHAP-based interpretability. Results: Among 320 infants, 119 underwent surgery and 201 received non-operative management. Thirteen consensus features were selected for modeling, including gestational age, CRP, lactic acid, peritoneal irritation signs, pneumatosis intestinalis, and hematologic parameters. The Neural Network achieved the highest overall classification performance (accuracy 0.875, sensitivity 0.824, specificity 0.903, balanced accuracy 0.863); Random Forest achieved the highest ROC-AUC (0.922), and XGBoost showed the highest PR-AUC (0.867). SHAP analysis identified CRP, peritoneal irritation signs, and gestational age as the most influential predictors. Conclusions: ML models integrating clinical, laboratory, and imaging variables can accurately predict the need for surgical intervention in preterm NEC patients. These models provide objective decision-support tools to improve early identification and optimize surgical management. Full article

The ICESat-2 satellite collects LiDAR data along linear orbital tracks using a photon-counting green wavelength (532.27 nm) instrument. The utility of combining ICESat-2 data with satellite imagery for training and subsequently applying satellite-derived bathymetry models to provide estimates of shallow water depth is well-established. However, automating and improving the accuracy of the identification of ICESat-2 photon events (PEs) representing bathymetry remains a challenge. This article presents an algorithm for automated extraction of PEs reflected from the ocean floor (rather than the ocean surface or noise in the water column). The algorithm is unique in examining both the density of PEs surrounding a subject PE and their position relative to the subject PE. This is accomplished by establishing three concentric ellipses around the subject PE, dividing them into radial “sectors” in 2D space (along-track vs. PE depth/height), recording the number of neighboring PEs in each sector and using this information to fit a LightGBM model. Agreement with PEs identified by an image interpreter is approximately 98%. Testing suggests that the accuracy of the algorithm is relatively insensitive to the size and shape of the ellipses used to define a PE’s neighborhood and to the number of radial sectors used. The model produced also appears to be robust across different geographic areas and data densities. Full article

Glioblastoma (GBM) is one of the most common and aggressive primary malignant tumors of the central nervous system, accounting for about half of all gliomas in adults. Despite intensive research and advances in molecular biology, genomics, and modern neuroimaging techniques, the prognosis for patients with GBM remains extremely poor. Despite the implementation of multimodal treatment involving surgery, radiotherapy, and chemotherapy with temozolomide, the average survival time of patients is only about 15 months. This is primarily due to the complex biology of this cancer, which involves numerous genetic and epigenetic abnormalities, as well as a highly heterogeneous tumor structure and the presence of glioblastoma stem cells with self renewal capacity. Mutations and abnormalities in genes such as IDH-wt, EGFR, PTEN, TP53, TERT, and CDKN2A/B are crucial in the pathogenesis of GBM. In particular, IDH-wt status (wild-type isocitrate dehydrogenase) is one of the most important identification markers distinguishing GBM from other, more favorable gliomas with IDH mutations. Frequent EGFR amplifications and TERT gene promoter mutations lead to the deregulation of tumor cell proliferation and increased aggressiveness. In turn, the loss of function of suppressor genes such as PTEN or CDKN2A/B promotes uncontrolled cell growth and tumor progression. The immunosuppressive tumor microenvironment also plays an important role, promoting immune escape and weakening the effectiveness of systemic therapies, including immunotherapy. The aim of this review is to summarize the current state of knowledge on the epidemiology, classification, pathogenesis, diagnosis, and treatment of glioblastoma multiforme, as well as to discuss the impact of recent advances in molecular and imaging diagnostics on clinical decision-making. A comprehensive review of recent literature (2018–2025) was conducted, focusing on WHO CNS5 classification updates, novel biomarkers (IDH, TERT, MGMT, EGFR), and modern diagnostic techniques such as liquid biopsy, radiogenomics, and next-generation sequencing (NGS). The results of the review indicate that the introduction of integrated histo-molecular diagnostics in the WHO 2021 classification has significantly increased diagnostic precision, enabling better prognostic and therapeutic stratification of patients. Modern imaging techniques, such as advanced magnetic resonance imaging (MRI), positron emission tomography (PET), and radiomics and radiogenomics tools, allow for more precise assessment of tumor characteristics, prediction of response to therapy, and monitoring of disease progression. Contemporary molecular techniques, including DNA methylation profiling and NGS, enable in-depth genomic and epigenetic analysis, which translates into a more personalized approach to treatment. Despite the use of multimodal therapy, which is based on maximum safe tumor resection followed by radiotherapy and temozolomide chemotherapy, recurrence is almost inevitable. GBM shows a high degree of resistance to treatment, which results from the presence of stem cell subpopulations, dynamic clonal evolution, and the ability to adapt to unfavorable microenvironmental conditions. Promising preclinical and early clinical results show new therapeutic strategies, including immunotherapy (cancer vaccines, checkpoint inhibitors, CAR-T therapies), oncolytic virotherapy, and Tumor Treating Fields (TTF) technology. Although these methods show potential for prolonging survival, their clinical efficacy still needs to be confirmed in large studies. The role of artificial intelligence in the analysis of imaging and molecular data is also increasingly being emphasized, which may contribute to the development of more accurate predictive models and therapeutic decisions. Despite these advancements, GBM remains a major therapeutic challenge due to its high heterogeneity and treatment resistance. The integration of molecular diagnostics, artificial intelligence, and personalized therapeutic strategies that may enhance survival and quality of life for GBM patients. Full article

Background: Chronic total occlusion (CTO) percutaneous coronary intervention (PCI) remains among the most complex procedures in interventional cardiology, with variable technical success and heterogeneous long-term outcomes. Conventional angiographic scores such as J-CTO and PROGRESS-CTO provide only modest predictive accuracy and neglect critical patient and operator-related factors. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools, capable of integrating multimodal data and offering enhanced diagnostic, procedural, and prognostic insights. Methods: We performed a structured narrative review of the literature between January 2010 and September 2025 using PubMed, Scopus, and Web of Science. Eligible studies were peer-reviewed original research, reviews, or meta-analyses addressing AI/ML applications in CTO PCI across imaging, procedural planning, and prognostic modeling. A total of 330 records were screened, and 33 studies met the inclusion criteria for qualitative synthesis. Results: AI applications in diagnostic imaging achieved high accuracy, with deep learning on coronary CT angiography yielding AUCs up to 0.87 for CTO detection, and IVUS/OCT segmentation demonstrating reproducibility > 95% compared with expert analysis. In procedural prediction, ML algorithms (XGBoost, LightGBM, CatBoost) outperformed traditional scores, achieving AUCs of 0.73–0.82 versus 0.62–0.70 for J-CTO/PROGRESS-CTO. Prognostic models, particularly CatBoost and neural networks, achieved AUCs of 0.83–0.84 for 5-year mortality in large registries (n ≈ 3200), surpassing regression-based methods. Importantly, comorbidities and functional status emerged as stronger predictors than procedural strategy. Future Directions: AI integration holds promise for real-time guidance in the catheterization laboratory, robotics-assisted PCI, federated learning to overcome data privacy barriers, and multimodality fusion incorporating imaging, clinical, and patient-reported outcomes. However, clinical adoption requires prospective multicenter validation, harmonization of endpoints, bias mitigation, and regulatory oversight. Conclusions: AI represents a paradigm shift in CTO PCI, providing superior accuracy over conventional risk models and enabling patient-centered risk prediction. With continued advances in federated learning, multimodality integration, and explainable AI, translation from research to routine practice appears within reach. Full article

Background: Glioblastoma (GBM) is an aggressive and treatment-resistant brain tumor with few effective therapies. Tubulin polymers are crucial for maintaining cell–cell signaling, cell proliferation, and cell division. Therefore, tubulin has been targeted by medicinal chemists to develop novel therapeutics to treat cancer. In this regard, we developed novel small-molecule tubulin inhibitors as potential therapeutics to treat GBM. Methods: We synthesized a focused library of pyrimidine-containing dihydroquinoxalinone-based analogs and tested nine compounds for cytotoxicity in GBM cell lines using the Sulforhodamine B (SRB) cell viability assay. We identified compound 8c as the most promising compound and evaluated the in vitro effects of 8c on GBM cell growth using live cell imaging and assessed apoptosis using a cell death ELISA. We then tested its anticancer activity in vivo on GBM xenografts grown in immunocompromised mice. Results: Several compounds demonstrated nanomolar IC₅₀ values in cell viability assays and outperformed temozolomide (TMZ), the current standard treatment for GBM patients. We identified compound 8c, which is a pyrimidine analog with a secondary amine, as the lead candidate for GBM studies in vitro and in vivo. Compound 8c reduced cell viability in a dose-dependent manner and induced complete growth arrest within 48 h at 3–10 nM concentrations in GBM cell lines. ELISA confirmed that compound 8c triggered dose-dependent apoptosis, whereas TMZ failed to induce apoptosis at nM concentrations. In vivo, compound 8c significantly inhibited GBM xenograft growth in immunocompromised mice by 66%. Conclusions: The potent tubulin inhibitor compound 8c has strong anti-GBM activity in vitro and in vivo and merits further preclinical development. Full article

Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index (NDVI) is a key indicator for assessing the growth status of vegetation. However, the insufficiency of existing NDVI datasets in terms of spatiotemporal continuity has limited the accuracy of long-term vegetation change studies. This study proposed a machine learning-based downscaling framework that integrates the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI and the Global Inventory Monitoring and Modeling System (GIMMS) NDVI data to reconstruct a long-term, high-resolution NDVI dataset. Unlike conventional statistical fusion approaches, the proposed framework employs machine learning-based nonlinear relationships to generate long-term, high-resolution NDVI data. Three machine learning algorithms—Random Forest (RF), LightGBM, and CatBoost—were evaluated. Their performance was validated using the MODIS NDVI as reference, with the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (R) as evaluation metrics. Based on model comparison, the CatBoost model was identified as the optimal algorithm for spatiotemporal data fusion (R² = 0.9014, RMSE = 0.0674, MAE = 0.0445), significantly outperforming RF and LightGBM models and demonstrating stronger capability for NDVI spatiotemporal reconstruction. Using this model, a long-term, 1 km monthly GIMMS-MODIS NDVI dataset from 1982 to 2014 was successfully reconstructed. On the basis of this dataset, the spatiotemporal variation characteristics of vegetation in the TRSR from 1982 to 2014 were systematically analyzed. The research results show that: (1) The constructed long-series high-resolution NDVI dataset has a high consistency with MODIS NDVI data; (2) From 1982 to 2014, the NDVI in the TRSR showed an increasing trend, with an average growth rate of 0.0020/10a (p < 0.05). NDVI showed obvious spatial heterogeneity, characterized by a decreasing gradient from southeast to northwest. (3) The Yellow River source exhibited the most evident vegetation recovery, the Yangtze River Source area showed a moderate improvement, whereas the Lancang River Source area displayed little noticeable change. (4) Broad-leaved forests experienced the most significant growth, while cultivated vegetation displayed a marked tendency toward degradation. This study provides both a high-accuracy long-term NDVI product for the TRSR and a methodological foundation for advancing vegetation dynamics research in other high-altitude regions. Full article

Background/Objectives: Glioblastoma (GBM) is a lethal form of primary brain tumor. There has been minimal improvement in overall GBM survival in recent years. To increase survival in patients with GBM, it is important to study novel GBM molecular antigens to form the basis of better diagnostics, prognostic measures, and therapeutic advancements. Our goal is to find more robust GBM-specific antigenic biomarkers to eventually improve GBM outcomes. Here, we initiated an investigation into cell-surface PCNA (csPCNA), a potential GBM biomarker and antigenic target. Methods: We utilized flow cytometry, imaging flow cytometry, and cell-surface Western blot to identify the expression of csPCNA on GBM cell lines (LN-229, LN-18) and a primary patient-derived tumor specimen. We then employed flow cytometry to study the associative co-expression of csPCNA with other biomarkers of GBM stem cells (CD44, CD49f) and GBM immunosuppression (PD-L1, TGFβRII). Results: We elucidated that LN-229, LN-18, and the primary GBM patient cells express csPCNA. We found that csPCNA is co-expressed with CD44, CD49f, PD-L1, and TGFβRII on the primary patient-derived GBM specimen. Conclusions: Our findings that csPCNA is expressed in GBM and is co-expressed with stem cell and immunosuppressive biomarkers indicate that csPCNA may be a potentially useful clinical–pathological biomarker for GBM stemness and immunosuppression. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor with dismal prognosis and limited treatment options. Immunotherapy, including personalized approaches using tumor-infiltrating lymphocytes (TILs) and allogeneic natural (NK) or engineered killer cells (chimeric antigen receptor NK, NK-CAR), and oncolytic viruses (OV), has shown some potential in GBM. Combining different therapeutic strategies may enhance treatment efficacy. Here, we present a xenograft GBM mouse model with multiparametric detection for various immunotherapy research applications. Methods: In a xenograft GBM NOD-Prkdcs scid Il2rgem1/Smoc (NSG) mouse model based on orthotopic transplantation of patient-derived GBM cultures retaining tumor heterogeneity, intravenous and intratumor immunotherapeutic interventions by TIL and OV therapy were performed. Xenograft engraftment was evaluated using intravital MRI; delivery of OV and TILs to the tumor and changes in the tumor and peritumoral space were assessed using intravital confocal microscopy; and metabolic and structural changes in the tumor and peritumoral environment were assessed via fluorescence lifetime imaging microscopy (FLIM) and optical coherence tomography (OCT). The intravital imaging data were compared with the results of preliminary and final histological and immunocytochemical data. Results: Both OV and TILs demonstrated tumor-specific targeting and delivery across the blood–brain barrier. Further, we showed that in this model the xenograft response to both therapeutic treatments can be assessed using FLIM and OCT. Conclusions: Overall, this work presents an optimized mouse model suitable for assessing the effect of combined TIL immunotherapy and OV on GBM in translational studies. Full article