Neuro-Oncology Advances

BackgroundGlioblastoma (GBM), Isocitrate dehydrogenase-wildtype (IDH-wt) is characterised by diffuse infiltration, with progression often arising from perilesional tissue and occult white-matter damage. We investigated whether radiomics from the T2/FLAIR-defined oedema and the structural disconnectome improve prediction of progression-free survival (PFS). MethodsWe retrospectively analysed 387 adults with newly diagnosed GBM, IDH-wt treated at a single tertiary centre (2005-2020). A deep-learning pipeline segmented enhancing tumour, non-enhancing tumour, and oedema on pre-operative MRI; lesion masks were propagated to normative tractography to derive disconnectome maps. 3-D shape radiomic features extracted for each segmented region underwent appropriate feature selection. Finally, 10 tumour and 9 oedema radiomics were combined with 6 clinical features to train 3 survival models (Random Survival Forest (RSF), XGBoost, Cox proportional hazards (CPH)) that were evaluated on a held-out 20% test set using Harrells C-index, Kaplan-Meier risk stratification and time-dependent ROC curves. ResultsThe best performance was achieved by RSF using all clinical and radiomic features (C-index 0.665 vs 0.595 for clinical features only, p=0.088). Models including oedema radiomics outperformed those using tumour radiomics alone, and disconnectome features, derived from both tumour and oedema regions, were repeatedly selected among the top predictors across algorithms. Combining radiomic and clinical features improved risk stratification and 12-month early-versus-late recurrence classification (AUC 0.704 vs 0.582 for clinical features alone). ConclusionsIntegrating perilesional oedema and white-matter disconnectome MR features with clinical and molecular data enhances prediction of PFS in GBM, IDH-wt. These network-aware, multimodal survival models may support personalised risk-adapted treatment strategies pending external validation. Key Points- GBM IDH-wt exhibits a high recurrence rate despite aggressive treatment. - Addition of high-dimensional oedema and disconnectome radiomic features to clinical features showed consistent improvement in the test performance of 3 ML models. - This can support informed clinical decision-making. Importance of the StudyPrediction of progression free survival (PFS) for a patient with highly recurrent glioblastoma IDH-wt traditionally relies on clinical history, demographics, and molecular markers of the tumour. Recent literature reveals the tumours disruptive nature through its invasion of white-matter tracts and identifies its microenvironment, particularly the perilesional oedema, as a harbour of treatment resistant tumour cells. This study is the first to combine high-dimensional radiomic features of the tumour, the oedema, and their disconnectome with clinical and treatment factors to predict PFS. Using 3 model architectures (XGBoost, RSF, and CoxPH), we demonstrate consistent directional improvements in performance, on addition of radiomic features to clinical baseline models. Furthermore, oedema and disconnectome radiomics are identified as top predictor features across algorithms. This proof-of-concept study provides a reproducible multimodal pipeline, reaffirms the usability of MR radiomics, and identifies features of the oedema and the structural connectome as promising biomarkers, demanding large-scale external validation.

BackgroundPATZ1 fusion-positive central nervous system (CNS) tumors frequently harbor MN1::PATZ1 fusions as driver mutations, provisionally classified as a rare DNA methylation class of low-grade neuroepithelial tumors. Radiographically, they resemble pilocytic astrocytomas with tumor and cystic components, but their supratentorial cortex location and higher recurrence rates are distinguishing features. An intermediate clinical course, despite focal high-grade histopathology, underscores the need for longitudinal molecular and immune analyses to refine classification and standard therapy. Case SummaryA female pediatric patient presented with neurological symptoms, including headache and right upper extremity weakness. MRI revealed a large cystic lesion in the left frontal lobe, leading to a differential diagnosis of low-grade glioma and ependymoma. Genomic analysis identified an MN1::PATZ1 fusion. The tumor recurred after gross total resection prompting a second resection. Transcriptomic and histopathologic assessments identified multiglial lineage, and high-grade features closely related to adult glioblastoma alongside pro-inflammatory activity in the primary tumor. The recurrent tumor showed reduced malignancy, and oligodendroglioma-like features. Increased MHC gene expression, immune checkpoint receptors (PDCD1, CTLA4, TIGIT,TIM3), T cell regulators (CXCR6), and elevated macrophage frequency, coupled with reduced PD-L1 in the recurrent tumor, suggest a complex anti-tumor immune response constrained by T cell dysregulation. This case, along with two other MN1::PATZ1 fusion-positive tumors, identifies a distinct transcriptomic subtype separate from circumscribed astrocytic glioma, highlighting upregulation of growth factor receptor pathways, like PI3K/AKT, and immune dysfunction linked to recurrence. ConclusionLongitudinal multi-omics analyses of recurrent MN1::PATZ1 fusion-positive CNS tumors revealed tumor maturation, immune dysfunction, and potential therapeutic targets. Introductory ParagraphPATZ1 fusion-positive central nervous system (CNS) tumors are rare, predominantly pediatric and frequently recurrent neoplasms provisionally classified as neuroepithelial tumors. Their pronounced histopathological and clinical heterogeneity, along with limited immunological characterization complicates their treatment standardization. We report a new case of an MN1::PATZ1 fusion-positive CNS tumor with recurrence, highlighting its radiographic similarities to low-to-intermediate grade pediatric glioma. Longitudinal multi-omics analyses of this case, along with additional MN1::PATZ1 fusion-positive CNS tumors, however, delineates a transcriptome subtype resembling adult high-grade glioma, with activated oncogenic and pro-inflammatory programs. The recurrent tumor exhibits features of decreased malignancy and enhanced glial differentiation, phenotypically shifting towards oligodendroglioma, suggesting tumor maturation. This was accompanied by increased antigen presentation programs, indicating immune engagement, while increased immune checkpoint expression and microglia/macrophage frequency indicate T cell exhaustion and immunomodulation, respectively. This longitudinal study highlights potential therapeutic strategies targeting both the tumor and its immune environment in MN1::PATZ1 fusion-positive CNS tumors.

Abstract Background: Conventional MRI cannot reliably distinguish radiation necrosis (RN) from recurrent metastasis after cranial radiotherapy, as both can show similar enhancement despite different biology. We tested whether these entities are mechanically non-equivalent in vivo and separable by MRE-derived viscoelastic metrics and perilesional interface-instability features. Methods: In a prospective, histopathology-anchored cohort, 11 post-radiotherapy enhancing lesions were classified as RN (n=3) or recurrent/progressive tumor (n=8). MRE was acquired at 3.0 T with single-frequency 60-Hz excitation to derive storage modulus (G'), loss modulus (G''), and complex shear modulus magnitude (|G*|). Co-primary endpoints were median tumor G' and |G*|, each tested one-sided (RN > tumor) with Holm correction across the two co-primary tests. Median tumor G'' was tested two-sided. A prespecified secondary 6-endpoint family (absolute and tumor/NAWM-normalized G', G'', and |G*|) was analyzed with Benjamini-Hochberg FDR control. Exploratory instability mapping in a 0- 6 mm peritumoral shell generated interface-topology metrics, including convexity. Results: Absolute tumor-core medians were higher in RN than tumor for |G*| (1.79 vs 1.32 kPa; Cliff's {delta} = 0.67; q = 0.10), G' (1.62 vs 1.09 kPa; {delta} = 0.50; q = 0.14), and G'' (0.81 vs 0.46 kPa; {delta} = 0.75; q = 0.10). NAWM normalization improved separation: tumor/NAWM |G*| (2.26 vs 1.41; {delta} = 0.92; q = 0.04) and tumor/NAWM G'' (2.67 vs 0.87; {delta} = 1.00; q = 0.04) were FDR-significant. Convexity also differentiated RN from tumor (0.49 vs 0.36; {delta} = 1.00; MWU p = 0.01). Conclusions: Tumor/NAWM G'', tumor/NAWM |G*|, convexity, and tumor G'' emerged as the strongest candidate features, indicating that RN is mechanically harder and more dissipative than recurrent metastasis. Signal strength was high (Cliff's {delta} up to 1.00) but should be interpreted cautiously given sample size. Exploratory analyses further suggest that instability mapping captures biologically relevant interface behavior. These findings support a mechanics-based RN-versus-recurrence framework and justify prespecified, preregistered external validation.

Lower-grade gliomas (World Health Organization [WHO] grades 2-3) exhibit variable treatment responses, yet clinical decisions remain guided by population-level trial results. Standard causal survival forests estimate treatment effects at individual time horizons but lack methodology to synthesize these into interpretable temporal trajectories. Here, we apply the Causal Analysis of Survival Trajectories (CAST) framework, a recently developed extension of causal survival forests that synthesizes horizon-specific causal effect estimates into smooth temporal curves while accounting for between-horizon covariances via bootstrap estimation and Ledoit-Wolf shrinkage. We apply CAST to estimate time-varying, heterogeneous effects of radiotherapy and chemotherapy in 776 patients with lower-grade gliomas from The Cancer Genome Atlas (TCGA; n=512) and the Chinese Glioma Genome Atlas (CGGA; n=264), analyzing six treatment-outcome scenarios and adjusting for age, sex, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19q codeletion, and extent of resection using elastic net propensity scores with overlap weighting. CAST curves reveal that chemotherapy provides consistent, sustained benefits across both cohorts; survival probability gains peak at 0.31 at 72-84 months for TCGA overall survival and 0.46 at 48 months for progression-free survival, with restricted mean survival time gains of 18.4 and 32.5 months at 10 years, respectively. CGGA chemotherapy shows delayed but large positive effects (survival probability peak 0.48 at 108 months). Radiotherapy effects are mixed, with modest E-values indicating sensitivity to residual confounding by indication. Subgroup CAST curves identify age at diagnosis as the dominant driver of treatment effect heterogeneity (46-56% of splits). All findings are robust to placebo permutation, simulated unobserved confounder, and negative control refutation tests. The CAST framework provides a general-purpose tool for temporal treatment effect visualization applicable beyond neuro-oncology.

IntroductionIdiopathic normal pressure hydrocephalus (iNPH) is a partially reversible neurological disorder in which imaging biomarkers support diagnosis and surgical decision-making. The callosal angle (CA) is one of the most robust radiological markers of iNPH and has also been associated with postoperative shunt outcome. However, several manual measurement variants exist and artificial intelligence (AI)-based tools now enable automatic CA measurement. Materials and MethodsIn total 71 patients (40 with confirmed iNPH and 31 controls) were included. Six predefined manual methods for measuring CA were applied to preoperative 3D T1-weighted MRI and evaluated for diagnostic performance and interobserver agreement. An AI-derived automatic CA (cMRI from Combinostics) was included as a seventh method and compared with the traditional manual method (perpendicular to the bicommissural plane and through the posterior commissure). Automatic measurements were additionally assessed in pre- and postoperative scans to evaluate robustness against shunt-related artifacts. ResultsAll seven CA variants significantly differentiated iNPH patients from controls (p < 0.05). The traditional method showed the highest discriminative performance (AUC = 0.986, SE = 0.012), while alternative planes demonstrated slightly lower accuracy (AUC range = 0.957-0.978). Interobserver agreement for manual measurements was good to excellent (ICC = 0.687-0.977). Automatic CA measurements showed excellent correlation with the traditional method, preoperative ICC = 0.92; postoperative ICC = 0.96. ConclusionAlthough several CA positions perform comparably, the traditional method remains marginally superior and is best supported by the literature. Automated CA measurements closely match expert manual assessment in pre- and postoperative imaging, supporting clinical implementation.

Rationale and ObjectivesContrast-enhanced (CE) MRI provides clear corticomedullary contrast for renal compartment delineation but may be contraindicated or undesirable in routine practice. We aimed to enable automated extraction of renal imaging biomarkers from routine non-contrast-enhanced (NCE) T1-weighted MRI by transferring CE-derived compartment labels. Materials and MethodsThis retrospective single-center study (January 2017 to December 2021) included 200 participants with paired arterial-phase CE and NCE T1-weighted MRI. Cortex, medulla, and sinus were manually segmented on CE MRI and rigidly transferred to NCE MRI to provide voxel-level reference labels. A hierarchical 3D Deep Neural Patchworks model was trained on 100 examinations (90 training/10 validation) and evaluated on an independent test set of 100 examinations using the transferred CE masks on NCE as reference. Performance was assessed using Dice similarity of segmentations and biomarker agreement using volumes and surface areas (Pearson/Spearman, MAE, Lins CCC, and Bland-Altman). ResultsWhole-kidney segmentation Dice was 0.950 (left) and 0.953 (right). Total kidney volume showed high agreement with minimal bias (MAE 8.76 mL, 2.5% of mean; CCC 0.983; bias -1.56 mL; 95% limits of agreement -28.81 to 25.69 mL). Cortex volume was modestly overestimated and medulla volume underestimated, shifting predicted compartment fractions toward cortex (74.7% vs. 72,1% in ground truth; medulla 21.5% vs. 24.3%; sinus 3.8% vs. 3.6%. Sinus volume maintained high concordance despite higher Dice dispersion. Surface area was systematically underestimated with low concordance. ConclusionCE-supervised knowledge transfer enables accurate, well-calibrated kidney volumetry from routine NCE MRI and supports contrast-free renal biomarker extraction. Surface area estimation remains challenging. Take-home MessagesO_LICE-supervised label transfer enables accurate, well-calibrated contrast-free kidney volumetry on routine non-contrast T1-weighted MRI. C_LIO_LICompartment volumetry is feasible but shows systematic cortex overestimation and medulla underestimation; surface area remains non-interchangeable due to boundary uncertainty. C_LI

BackgroundImmune checkpoint inhibition has transformed cancer therapy; however, many patients fail to respond to single-agent blockade, and combination strategies are often limited by toxicity. Central nervous system tumors exploit multiple immunosuppressive pathways, including the CD200 and PD-1/PD-L1 axis to evade anti-tumor immunity and support tumor aggressiveness. MethodsWe investigated ARL200, a peptide ligand targeting the CD200 activation receptor (CD200AR) using in vitro immune assays, murine syngeneic tumor models, phosphoproteomics, and correlative studies from a first-in-human trial in recurrent glioblastoma. ResultsARL200 exposure activated DAP10/12-dependent signaling and downregulated multiple inhibitory immune checkpoint receptors, including CD200R1, PD-1, and CTLA-4, and checkpoint ligands, CD200 protein and PD-L1, through suppression of the JAK1/3-SHP-STAT-IKK/{beta}-NF{kappa}B pathway. Distinct ARL200 variant peptides elicited unique immune responses. In patients with recurrent glioblastoma, ARL200 treatment was associated with immune activation, reduced inhibitory checkpoint expression, and evidence of antigen-specific memory responses without treatment-related toxicity. ConclusionsTargeting CD200AR enables coordinated modulation of multiple immune checkpoints with a single agent, representing a next-generation immunotherapeutic strategy opening a new pathway for treating aggressive malignancies. Key PointsO_LIARL200 elicits an active immune response for the development of a potent and durable anti-tumor response C_LIO_LIARL200 abolishes the suppressive effects of multiple immune checkpoint blockades C_LIO_LIDifferent ARL200 sequences drive alternative immune responses. C_LI Importance of the StudyTumors exploit multiple immune checkpoint pathways to suppress antitumor immunity, particularly within the immunosuppressive microenvironment of the central nervous system. Current immune checkpoint inhibitors often require combination therapy to achieve clinical efficacy, frequently at the cost of increased toxicity. In this study, we demonstrate that targeting the CD200 activation receptor (CD200AR) with a peptide ligand provides a novel strategy to simultaneously downregulate multiple inhibitory immune checkpoints, including CD200R1, PD-1, PD-L1, and CTLA-4, through a shared intracellular signaling pathway. ARL200 engagement activates DAP10/12-dependent signaling while suppressing the JAK1/3-SHP-STAT-IKK/{beta}-NF{kappa}B axis, thereby overriding tumor-mediated immunosuppression. Importantly, this multi-checkpoint modulation is achieved with a single therapeutic agent and translates to immune activation and clinical responses in patients with recurrent glioblastoma, with minimal treatment-related toxicity. These findings establish CD200AR targeting as a next-generation immunotherapeutic approach with the potential to improve the safety and efficacy of immune-based therapies for aggressive CNS malignancies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/26345679v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@17a5010org.highwire.dtl.DTLVardef@11e67eborg.highwire.dtl.DTLVardef@1387c07org.highwire.dtl.DTLVardef@156d418_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background and Purpose: Magnetic resonance imaging (MRI) for radiation therapy treatment planning is currently being used in many anatomical sites to better visualize soft tissue landmarks, a technique known as an MRI simulation. A core component of modern MRI simulation configurations are the use of external laser positioning systems (ELPS) to help set up the patient. Though necessary for accurate and reproducible patient setup, the ELPS, if left on during imaging, may interfere negatively with image quality due to leaking electronic noise, of which MRI is sensitive to. It is currently unknown whether this leakage of electronic noise may further affect quantitative values derived from clinically employed relaxometric, diffusion, and fat fraction sequences. Therefore, in this study, we aim to characterize the impact of MRI simulation lasers on general image quality and quantitative imaging accuracy. Materials and Methods: First, a cine acquisition was used to visualize the real-time changes in image signal-to-noise ratio (SNR) from when the ELPS was deactivated to activated. To validate this effect quantitatively, the SNR was measured using the American College of Radiology (ACR) recommended protocol in a homogeneous phantom with the integrated body, 18-channel UltraFlex small, 18-channel UltraFlex large, 32-channel spine, and 16-channel shoulder coils. Next, a geometric distortion algorithm was tested in two vendor-provided phantoms while using the integrated body coil and the ACR Large Phantom protocol was tested. Finally, a series of quantitative MRI scans were performed using a CaliberMRI Model 137 Mini Hybrid phantom to validate quantitative T1, T2, and ADC while a Calimetrix PDFF-R2* phantom was used for quantitative PDFF and R2*. All scans were performed with both the ELPS both deactivated and activated. Results: Visible electronic noise artifacts were seen when using the integrated body coil when the ELPS was activated on the cine acquisition which led to a four-fold decrease in SNR using the ACR protocol. This SNR drop was not seen when using the remaining tested coils. The automatic fiducial detection algorithm was affected negatively by ELPS activation leading to misidentification when identified perfectly with the ELPS deactivated. Degradation in image intensity uniformity, percent signal ghosting, and low contrast object detectability was seen during ACR Large Phantom testing using the 20-channel Head/Neck coil. Concordance across quantitative MRI values was similar when the ELPS was both deactivated and activated while a consistent increase in standard deviation inside the ADC vials was seen when the ELPS was activated. Discussion: The extra noise induced from the activation of the ELPS during imaging should be avoided due to its potential to unnecessarily increase image noise. This is particularly true when conducting mandatory quality assurance testing for image quality and geometric distortion which utilize the integrated body coil which is most susceptible to ELPS-induced noise. Clear clinical guidelines should be implemented to make this issue known to the MRI technologists, physicists, and other relevant staff using an MRI with a supplementary ELPS for patient alignment.

BackgroundAlthough deep learning models have improved individual PET analysis, image processing and quantification tasks, end-to-end automation from raw DICOM to quantitative clinical reporting remains limited, particularly in heterogeneous real-world settings. MethodsAs a proof-of-concept, an autonomous large language model (LLM)-orchestrated multi-tool agent for end-to-end PET/CT interpretation was developed. A reasoning-based text LLM selected appropriate series from raw DICOM, coordinated registration and SUV conversion, invoked segmentation and detection tools, generated maximum-intensity projections, called a vision-enabled LLM for interpretation, and synthesized structured draft reports. The system was retrospectively evaluated in 170 patients undergoing baseline FDG PET/CT for lung cancer staging, using expert reports as reference. ResultsThe agent successfully completed the full end-to-end workflow from raw DICOM selection to structured draft report generation without human intervention in all 170 examinations. Primary tumor detection achieved 100% sensitivity. For nodal involvement, sensitivity was 84.8% and specificity was 39.4%, whereas distant metastasis detection showed 70.2% sensitivity and 65.0% specificity. Discrepancy analysis of 58 nodal and 57 metastatic mismatch cases revealed systematic false-positive findings related to reactive or physiologic uptake and false-negative findings involving small-volume or anatomically atypical metastases. ConclusionLLM-orchestrated PET/CT agents can enable workflow-level automation from raw DICOM to quantification and structured draft reporting under real-world conditions. Although primary tumor detection was highly reliable, nodal and metastatic assessment revealed systematic limitations, supporting a collaborative role with continued expert oversight in complex clinical scenarios.

Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.

Current standard of care imaging practices cannot reliably differentiate among certain renal tumors such as benign oncocytoma and clear cell renal cell carcinoma (RCC), and between low and high grade RCCs. Previous work has explored using deep learning, radiomics, and texture analysis to predict renal tumor subtypes and differentiate between low and high grade RCCs with mixed success. To further this work, large diverse datasets are needed to improve model performance and provide strong evaluation sets. In this work, a dataset of 831 multi-phase 3D CT exams was curated. Each exam contains up to three contrast-enhanced CT phases. Tumor outlines or bounding boxes were annotated and registered to the image volumes. The pathology results for each tumor and relevant patient metadata are also included.

Radiogenomics enables the non-invasive characterisation of the genomic and molecular properties of tumours, with epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) being one of the most investigated applications. In this study, we evaluate radiomics, contrastive learning, and convolutional deep learning approaches to predict the EGFR mutation status from chest Computed Tomography (CT) images using the TCIA Radiogenomics dataset (n=115). Our results, using 10-fold cross validation, demonstrate the capacity of imaging models to predict mutation status from CT data in a manner consistent with existing literature. Among the evaluated methods, models integrating radiomic with clinical features achieved the best performance, with an AUC of 0.790 and AUPRC of 0.517, outperforming both contrastive learning (AUC=0.787) and convolutional architectures (AUC=0.763). Beyond methodological comparisons, we discuss the challenges related to clinical translation. Specifically, we contrast radiogenomics with conventional tissue biopsies, and identify scenarios where radiogenomics might be useful, either independently or in conjunction with other existing diagnostic technologies. Together these findings evidence the potential utility of radiogenomics EGFR models and provide direct architecture comparisons on the same dataset.

PurposePrognosis in metastatic non-seminomatous germ cell tumors (mNSGCT) is currently guided by the IGCCCG classification, which incorporates tumor markers, organs involved with metastatic disease, and primary site but not histologic subtype. We aimed to evaluate whether specific histological components provide additional prognostic information in a large international mNSGCT cohort. Patient and MethodsWe analyzed clinical, pathologic, and outcome data from 662 patients with mNSGCT across multiple international centers. Cox regression and multivariable stepwise models were used to evaluate the impact of age, tumor histology, serum markers, primary site of disease, chemotherapy, IGCCCG, and post-chemotherapy surgery on overall survival. Analyses were performed using both complete-case and imputed datasets to account for missing values. ResultsThe presence of any percentage of embryonal carcinoma (EC) was independently associated with improved overall survival HR 0.603 (95% CI: 0.37-0.98, p=0.040), whereas yolk sac tumor (YST) predicted worse prognosis in complete-case analysis HR 2.27 (95% CI: 1.43 - 3.61 p = 0.001). Choriocarcinoma was also associated with a HR 1.58 (95% CI: 1.08 - 2.32 p= 0.019) adverse outcomes. IGCCCG risk classification remained a strong predictor of mortality HR up to 8.9 for Poor vs Good risk, (95% CI: 4.63 - 17.09 p < 0.001), but histologic components added significant independent prognostic value. Post-chemotherapy retroperitoneal lymph node dissection (RPLND) conferred a substantial survival benefit HR 0.44 (95% CI: 0.258 - 0.754 p=0.003). Interestingly, teratoma was not associated with mortality but was linked to younger age, testicular primaries, and higher likelihood of residual disease requiring surgery. ConclusionsHistological composition, particularly the presence of EC or YST, has a significant and independent impact on survival in mNSGCT, beyond established risk classifications. Integration of histological subtypes may enhance prognostic accuracy and guide individualized treatment strategies in advanced germ cell tumors.

PurposeAqueous humor drains fluid from the eye not only via the conventional pathway through the trabecular meshwork and Schlemms canal, but also within the eye is known to occur via pathways through the posterior chamber and optic nerve to the cerebrospinal fluid (CSF) surrounding the optic nerve. The mechanism is poorly understood, and non-invasive method for evaluation in living humans has not been established. We previously showed that eye drops containing O-17-labeled water (H217O) distribute in the anterior chamber but not the vitreous. This study aimed to evaluate the distribution of H217O in the CSF along the optic nerve. MethodsFive ophthalmologically normal participants (20-31 years, all females) were selected from a previous prospective study based on 1H MR images of the eyes that included the optic nerve. They received eye drops of 10 mol% H217O in their right eye. Dynamic image time series was created by normalizing the signal of each 1H-T2WI by the pre-drop average signal. Region-of-interest analyses were performed for signal changes in the anterior chamber, vitreous, and CSF. ResultsIn the quantitative evaluation, the normalized intensity in the anterior chamber and CSF was significantly lower than that in the pre-drop signal (anterior chamber: 0.78 {+/-} 0.07, p < 0.005; CSF: 0.89 {+/-} 0.07, p < 0.05). No distribution was identified in the vitreous. Qualitatively, the distribution of H217O in the anterior chamber was detected in all five participants and in the CSF of four participants (80%). ConclusionH217O eye drops were distributed in the anterior chamber and CSF, but not in the vitreous. These findings suggest that the visualization of aqueous humor outflow, not via the Schlemms canal, may contribute to ocular fluid homeostasis, including the ocular glymphatic system.

BackgroundKidney volumetry derived from CT has been proposed as a surrogate of renal function in living kidney donor evaluation. However, clinical integration has been limited by reader-dependent workflows and semiautomatic methods susceptible to image quality. PurposeTo evaluate whether fully automated CT-based segmentation of renal cortex, medulla and total parenchymal volume provides reproducible volumetric biomarkers associated with global and split renal function in living kidney donor candidates. Materials and MethodsIn this retrospective single-center study, 461 living kidney donor candidates (2003-2021) underwent contrast-enhanced abdominal CT. A convolutional neural network was trained to automatically segment cortical, medullary, and total parenchymal volumes on arterial-phase images. Segmentation performance was evaluated against manual reference annotations. Volumes were indexed to body surface area. Associations with eGFR, 24-hour creatinine clearance, cystatin C, and tubular clearance were assessed using Spearman correlation coefficient ({rho}), and side-specific volume fractions were compared with scintigraphy -derived split function. ResultsAutomated segmentation achieved excellent agreement with expert reference segmentations (Dice 0.95 for cortex; 0.90 for medulla). eGFR correlated moderately with cortical ({rho} = 0.46) and total parenchymal volume ({rho} = 0.45), and modestly with medullary volume ({rho} = 0.30). Similar associations were observed for other global measures, with the strongest correlation for cortical volume and tubular clearance ({rho} = 0.53). Side-specific volume fractions correlated with scintigraphy-derived split renal function ({rho} = 0.49-0.56; all p < 0.001). ConclusionAutomated CT-based renal subcompartment segmentation provides reproducible volumetric biomarkers within routine donor evaluation. Cortical volume performs comparably to total parenchymal volume and tracks split renal function at the cohort level, suggesting potential utility in donor assessment.

PurposeTo describe the rationale, methods, and baseline sample descriptives of the Adolescent and Young Adult Tracking Engagement and Management Skills (AYA TEAMS) cohort. The AYA TEAMS study is a longitudinal observational cohort study that aims to identify determinants and patterns of self-management and engagement in cancer-related long-term follow-up (LTFU) care and validate a novel transition readiness assessment among adolescent and young adult (AYA) survivors of childhood cancer. ParticipantsAYA survivors of childhood cancer (ages 16-25) and their caregivers were enrolled from 3 large pediatric oncology centers across the United States from 2020-2022 and followed for 2 years (minimum) to 3 years and 3 months (if transferred to adult care). AYA inclusion criteria were: past childhood cancer diagnosis, at least 2 years off-treatment, 5 years since diagnosis, engaged with the participating pediatric health care system within the last 18 months, cognitively able to complete study procedures, and English speaking. AYA completed a comprehensive battery of measures including assessments of self-management and transition readiness at baseline and annually for 2 years. For AYA transferred to adult care, separate measures were administered at the time of transfer (following last pediatric visit) and 15 months post transfer. Caregivers (English or Spanish-speaking) completed a single survey at baseline to capture family functioning, psychosocial risk, and transition readiness. Cancer diagnosis, treatment modalities, treatment-related late effects, and engagement in LTFU care were captured via electronic medical record review. In total, 709 AYA were enrolled and 587 were included in the final cohort [Mage=19.7 years, 52.5% female, 38.2% from racial and/or ethnic minoritized groups, (REMG)]. The cohort was on average 7.3 years old at the time of diagnosis and 10.5 years off treatment. Half (52.5%) were survivors of leukemia/lymphoma, 38.0% solid tumors, and 9.5% central nervous system tumors. Three hundred and ninety-nine caregivers participated (90% mothers). Findings to DateEnrolled AYA excluded from the baseline cohort were more likely to be male, from REMG, and/or to enroll without a caregiver. Baseline cohort differences between sites emerged for age, race and ethnicity, socioeconomic status, and treatment modalities and intensity. Future PlansData collection was completed in April 2025. Findings from this cohort will elucidate important predictors of self-management and engagement in recommended annual LTFU and inform the design of interventions to reduce disengagement in LTFU. Strengths and LimitationsO_LIThis study is the first known prospective cohort of AYA-only long-term survivors of childhood cancer in the United States recruited from pediatric cancer centers. C_LIO_LIThis study achieved high enrollment and retention rates across a medically and demographically diverse sample. C_LIO_LIInformed by multiple theoretical self-management models, this study will be able to examine predictors and transactional relationships of AYA survivor self-management, including engagement in pediatric and adult cancer-related long-term follow-up care. C_LIO_LIReliance on English-speaking AYA and those currently engaged with the health care system are limitations. C_LI

BackgroundAI-based radiomics has demonstrated promising diagnostic performance for pancreatic cystic neoplasms, yet clinical translation remains limited. Whether this reflects insufficient model performance or structural limitations of the evidence base remains unclear. MethodsWe performed a systematic review and diagnostic test accuracy meta-analysis of AI-based radiomics in pancreatic cyst (2015-2025), addressing two clinically relevant tasks (Q1: cyst type differentiation/Q2: malignancy or high-grade dysplasia prediction). Training and validation datasets were synthesized independently using hierarchical models. Study evaluation extended beyond diagnostic performance to a four-dimensional framework integrating RQS 2.0, METRICS, TRIPOD+AI and PROBAST+AI explicitly contrasting pooled diagnostic performance with reporting quality, methodological rigor, and risk of bias. The review was pre-registered (PROSPERO) and conducted according to PRISMA 2020. ResultsTwenty-nine studies were included (Q1: n = 15; Q2: n = 14), predominantly retrospective and single center. Training-based analyses showed high apparent diagnostic performance for Q1 (pooled sensitivity/specificity: 0.89 [95% CI, 0.85-0.92]/ 0.90 [0.85-0.93]), but there was substantial heterogeneity ({tau}{superscript 2} = 0.56/0.78; {rho} = 0.38). Validation-based performance remained high (0.86 [0.82-0.89]/ 0.88 [0.81-0.93]), while heterogeneity persisted and prediction regions exceeded confidence regions. Training-based analyses demonstrated similarly high apparent performance (0.88 [0.79-0.95]/0.89 [0.81-0.94]) for Q2, with pronounced heterogeneity ({tau}{superscript 2} = 1.98/1.61; {rho} = 0.63). Validation-based performance was slightly lower, yet still clinically comparable (0.82 [0.75-0.89]/0.86 [0.80-0.91]), and heterogeneity persisted ({tau}{superscript 2} = 0.71/0.43; {rho} = 0.15). Across both tasks, high diagnostic accuracy occurred alongside incomplete reporting, limited validation and an elevated risk of bias. ConclusionAI-based radiomics for pancreatic cysts has reached a structural performance plateau. Further improvements in diagnostic accuracy alone are insufficient to achieve clinical translation and must be accompanied by a paradigm shift from performance-driven model development toward decision-anchored study designs, robust validation strategies, transparent reporting standard, and clinically integrated evaluation frameworks. SummaryAlthough pancreatic cystic lesions are increasingly being detected, imaging-based decision-making remains limited, particularly regarding differentiating between cyst types and stratifying malignancy risk. In this PRISMA-compliant and PROSPERO-registered systematic review and meta-analysis of diagnostic tests, we evaluated the use of AI-based radiomics for these two tasks, as well as its contextualized performance. In addition, a four-dimensional framework was employed to conduct the evaluation, incorporating diagnostic accuracy, reporting quality, risk of bias, and radiomics maturity. Across studies published between 2015 and 2025, the pooled diagnostic performance was consistently high, with only modest declines observed from the training to the validation stage. Nevertheless, considerable heterogeneity between studies and limited transportability remained evident. Multidimensional evaluation indicated a systematic dissociation between reported performance and methodological robustness, characterized by incomplete reporting, restricted validation, and an elevated risk of bias. These limitations were consistent across both clinical questions and were not resolved by increasing model complexity. The findings of this meta-analysis suggest that the structural performance of AI-based radiomics for pancreatic cysts has plateaued. To progress towards clinical translation, it is necessary to employ study designs anchored in decision-making processes, robust multi-center validation, and transparent, reproducible evaluation frameworks. This is preferred to further optimization of model architecture alone.

Medical oncology education faces a dual crisis: knowledge velocity that outpaces static curricula and large language model (LLM) risks--hallucination and automation bias--that threaten the fidelity of AI-assisted learning. We present Onco-Shikshak V7, an AI-native adaptive learning platform that addresses both challenges through a unified cognitive architecture grounded in learning science. The system replaces isolated educational modules with four authentic clinical workflows--Morning Report, Tumor Board, Clinic Day, and AI Textbook--each scaffolded by a nine-module pedagogy engine that integrates ACT-R activation dynamics (illness scripts), Item Response Theory (adaptive difficulty), the Free Spaced Repetition Scheduler (FSRS v4), Zone of Proximal Development (scaffolding), and metacognitive calibration training (Brier score). Six specialist AI agents--medical oncology, radiation oncology, surgical oncology, pathology, radiology, and oncology navigation--engage in multi-disciplinary deliberation with per-specialty retrieval-augmented generation (RAG) grounding across nine authoritative guideline sources including NCCN, ESMO, and ASTRO. The platform provides 18 clinical cases with decision trees across six cancer types, maps every interaction to 13 ACGME Hematology-Oncology milestones, and implements four closed-loop feedback mechanisms that connect session errors to targeted flashcards, weak domains to suggested cases, and all interactions to a persistent learner profile. Technical validation confirms algorithmic correctness across eight subsystems. To our knowledge, this is the first system to unify ACT-R, IRT, FSRS, ZPD, and metacognitive calibration in a single medical education platform. Formal learner evaluation via randomized controlled trial is planned.

Background and ObjectivesWhite matter hyperintensities (WMH) of presumed vascular origin are a neuroimaging hallmark of cerebral small vessel disease (CSVD). Their spatial heterogeneity may reflect different clinical phenotypes. Most prior studies relied on principal component analysis to characterise such heterogeneity, which has limited ability to stratify individuals into discrete and interpretable WMH subtypes. We therefore propose a data-driven framework to identify WMH spatial subtypes, characterise their demographic and clinical profiles, and investigate their predictive value for future WMH progression. MethodsWe analysed MRI scans from 63,338 individuals across 4 major cohorts (internal data): ADNI3, Insight46, SABRE and UK Biobank (UKB), and validated our findings in the OASIS-3 dataset (n=844). WMH were automatically segmented and regionally quantified using a 36-region bullseye framework. Clustering was applied to the relative regional distributions of WMH. A stability-based approach was used to identify robust WMH subtypes. Their associations with 19 risk factors of interest were analysed using multivariable regression. In a subset with follow-up MRI scans (internal: n=5,274, OASIS-3: n=182), we evaluated the predictive value of these subtypes combined with other volumetric or spatial WMH variables for WMH progression. ResultsFive WMH location patterns with different lesion burden and spatial distribution were identified (stability score 0.946) and reproduced in OASIS-3. These patterns showed distinct associations with demographic, vascular, metabolic, inflammatory and genetic risk factors. Higher-burden patterns were independently associated with older age, higher blood pressure, diabetes and smoking, indicating a gradient of vascular risk across spatial subtypes. WMH location patterns were largely preserved over 18-30 months, with most individuals remaining within the same pattern (71.5%). While global baseline WMH volume remained a strong predictor of future WMH progression (balanced accuracy 0.693, 95% CI: 0.664-0.723), models including baseline regional WMH volumes consistently outperformed other candidates (best balanced accuracy 0.737, 95% CI: 0.706-0.764). DiscussionWe presented a robust and scalable framework for spatial WMH phenotyping. We discussed clinical and prognostic implications of the spatial subtypes beyond total lesion burden. Our findings supported the value of WMH spatial characterisation in stratifying risk that may help guide personalised approaches to managing CSVD.

IntroductionThe precise determination of diagnostic cut-off points is essential for the development of multimarker panels in oncology. In previous work on pulmonary nodules, it was observed that the standard two-parameter logistic fit could be insufficient for biomarkers with asymmetric distributions. Furthermore, the calculation of empirical cut-off points based on graphical visualization presented limitations in precision and reproducibility. ObjectiveThis study presents a methodological advancement in the data analysis phase (Stage 1), introducing new Python algorithms for the direct analytical calculation of empirical intersections and robust mathematical modeling using Dual Annealing with both two-parameter and four-parameter logistic functions. This improved methodology feeds into the ThresholdXpert 1.0 software tool for combinatorial optimization of biomarker panels (Stage 2), and is applied here to the diagnostic challenge of hepatocellular carcinoma (HCC). MethodsThe methodology was first validated by re-analyzing a dataset of patients with pulmonary nodules (N=895). It was subsequently applied to an HCC dataset derived from the cohort of Jang et al. (208 HCC, 193 cirrhosis, 401 total), randomly divided into a training set (280) and an independent test set (121). Scripts were developed to compare the previous two-parameter logistic fit with the new two- and four-parameter logistic models. Finally, ThresholdXpert 1.0 was used for multimarker panel optimization. ResultsThe integration of empirical calculation, logistic modeling, and combinatorial optimization through ThresholdXpert 1.0 provides a robust and coherent framework for the development of multimarker diagnostic panels. The four-parameter logistic model provided additional validation without substantially modifying cut-off values for most biomarkers, confirming the stability of the approach while offering greater flexibility for complex distributions. When applied to hepatocellular carcinoma, the framework identified a molecular panel composed of AFP, PIVKA-II, OPN, and DKK-1 with sensitivity of 0.77 and specificity of 0.72, and an optimized panel incorporating inverse MELD that achieved the best overall balance (sensitivity 0.73, specificity 0.75) in independent external validation. These results demonstrate the potential of this approach as a generalizable tool for the optimized design of binary diagnostic systems in oncology. ConclusionThe integration of complementary mathematical modeling enhances the capability of ThresholdXpert 1.0 to identify robust diagnostic panels, as in some cases a single biomarker may outperform biomarker combinations, and vice versa. This approach enabled the integration of molecular biomarkers and clinical variables under a unified mathematical framework. Contactroberto117343@gmail.com