Sellar region neurocytoma (SELN) is a rare neoplasm whose relationship to other neurocytomas within the central nervous system (CNS) has remained unclear. Prior reports have variably classified SELN as a variant of extraventricular neurocytoma (EVN), while immunohistochemical and ultrastructural studies have suggested a hypothalamic origin. Here, we performed unsupervised clustering of DNA methylation data across a large pan-cancer reference set and identified SELN (n = 20) as distinct from other neurocytomas and regional mimics, as well as clustering with neuroendocrine tumors from other organ sites. SELN exhibited a CIMP-like phenotype, TTF1 negativity (0/8), and AVP (vasopressin) promoter hypomethylation, implicating a magnocellular hypothalamic cell of origin. In evaluable cases, a neuronal/neuroendocrine immunophenotype was observed (synaptophysin 8/8, chromogranin A 5/5) with absent pituitary transcription factor expression (PIT1 and TPIT negative 0/4). DNA sequencing (n = 5) and RNA-based fusion profiling (n = 4) did not detect recurrent mutations or gene fusions, respectively. Patients often presented with visual disturbances or headaches and spanned pediatric and older age groups (median 42 years, range 12.5–75), with no sex predilection. Despite locally aggressive imaging features in some cases (cavernous sinus invasion, carotid encasement, hydrocephalus), disease-free survival (n = 13) was comparable to central neurocytoma, with no disease-related deaths during the limited follow-up. Together, these findings support SELN as a molecularly distinct hypermethylated neuroendocrine-like epitype and clinicopathologic entity.

BACKGROUND:Accurate molecular classification of medulloblastoma is critical for prognosis and treatment planning, but current methods rely on surgical tissue sampling and molecular profiling. This study evaluated whether in vivo proton MR spectroscopy (¹H-MRS) can provide noninvasive metabolic markers to support presurgical molecular group stratification. METHODS:In this single-center retrospective study, pretreatment ¹H-MRS data were analyzed from 95 pediatric patients with medulloblastoma (median age 7.4 years; 56 male). Single-voxel point-resolved spectroscopy (TE = 35 ms, TR = 1.5-2.0 s) were acquired during routine clinical MRI, adding approximately 5 minutes of scan time. Absolute metabolite concentrations and selected ratios were quantified using automated spectral fitting. Metabolic profiles were compared across molecular groups (group 3, n = 22; group 4, n = 35; sonic hedgehog [SHH], n = 26; wingless [WNT], n = 12) and assessed for qualitative concordance with prior ex vivo high-resolution HR-MAS NMR findings. Group differences were tested using Kruskal-Wallis with Dunn post hoc correction. RESULTS:Significant metabolic differences were observed across molecular groups, with strong group effects for taurine, creatine, choline, glutamate, and γ-aminobutyric acid (GABA) (all P < .0015). Taurine was elevated in group 3 and group 4 relative to SHH (log₂FC = 1.77 and 1.40, adjusted P < 4 × 10⁻⁵). SHH tumors exhibited lower creatine compared with group 3, group 4, and WNT (adjusted P < .05). Glutamate was higher in SHH than in WNT, while WNT tumors showed increased choline and GABA relative to other groups (adjusted P < .05). In vivo patterns were qualitatively concordant with ex vivo NMR findings. CONCLUSIONS:In vivo ¹H-MRS is a widely available, clinically feasible imaging biomarker that complements existing diagnostics and supports presurgical stratification of medulloblastoma.

BACKGROUND:IDH-mutant astrocytomas are classified as WHO grade 4 in the presence of conventional high-grade histologic features and/or homozygous CDKN2A/B deletion in the 5th edition of the WHO Classification of Central Nervous System Tumour guidelines. However, work over the past decade has indicated a number of other molecular alterations that warrant consideration as potential prognostic markers. METHODS:We used univariate Kaplan-Meier and multivariate Cox proportional hazards regression analysis to evaluate the prognostic effects of homozygous CDKN2A/B deletion, CDK4 amplification, CCND2 amplification, PDGFRA amplification/mutation, PIK3R1 mutation, PIK3CA mutation, MYCN amplification, EGFR amplification/mutation, TERT promoter mutation, and grade 4 histologic features in two independent cohorts of WHO grade 2-4 IDH-mutant astrocytoma (n = 840 and n = 367). RESULTS:The presence of CDK4 amplification, CCND2 amplification, PDGFRA alteration, PIK3R1 mutation, MYCN amplification, and EGFR alteration were each associated with reduced overall survival compared to WHO grade 2/3 astrocytomas without these molecular features. 17.7% (148/837) of otherwise grade 2/3 astrocytomas had one or more of these molecular criteria, with resulting intermediate clinical outcome in terms of overall survival (median survival of 67.3-82.0 months) compared to grade 2/3 astrocytomas without these molecular features (median survival of 135.0-140.7 months) and grade 4 astrocytomas (median survival of 35.3-45.0 months). CONCLUSIONS:The presence of CDK4, CCND2, PDGFRA, PIK3R1, MYCN, and EGFR alterations result in an intermediate patient survival in IDH-mutant astrocytoma. Adding these molecular alterations should be considered in future diagnostic classification systems to improve stratification of high-risk patients.

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality. While for Stage IA LUAD surgery is often curative, recurrence rates remain significant. Liquid biopsy enables monitoring residual disease and predicts recurrence, however utility in Stage IA LUAD is not well-established. Tumor-informed whole genome sequencing (WGS) represents a highly sensitive liquid biopsy method for the analysis of circulating-tumor cell-free DNA (ctDNA) without designing and maintaining patient-specific probes. We aimed to determine the prognostic value of genome-wide tumor-informed minimal residual disease (MRD) monitoring in Stage IA NSCLC using WGS of ctDNA. WGS was performed on 42 patients with Stage IA NSCLC. Tumor was sequenced at 40x and germline DNA and plasma derived ctDNA at 20x and patient specific mutational signatures were developed from the tumor-normal comparison and applied to ctDNA WGS at each time point using AI-supported pattern recognition algorithm. ctDNA results were compared with clinical recurrence. WGS ctDNA was able to predict recurrence with 0.75 sensitivity and 0.83 specificity, with median 16.7 months lead time compared to clinical or imaging recurrence. ctDNA WGS was able to distinguish between a second primary and recurrence in histologically or clinically challenging cases. Whole genome sequencing of ctDNA can predict recurrence in the earliest clinical stage of NSCLC, identifying patients who will recur, and providing information to help guide radiological follow-up and adjuvant therapy.

Papillary renal neoplasm with reverse polarity (PRNRP) has been proposed as a distinct subtype of renal cell neoplasm with recurrent KRAS mutations and indolent behavior. However, its epigenetic landscape is poorly understood. In this study, 12 PRNRPs and a PRNRP initially diagnosed as "papillary adenoma" were analyzed. All 13 cases underwent targeted next-generation sequencing for driver mutations. Eleven PRNRPs were profiled using the Illumina MethylationEPIC array and compared with a reference cohort of 71 common renal cell tumors. KRAS mutations were identified in 12 of 13 (92%) cases of PRNRP. Copy-number analysis from methylation profiling showed that 9 of 11 (82%) PRNRPs lacked copy-number changes. Two cases showed a focal loss of chromosome 8 and a gain of chromosome 16, respectively. Unsupervised clustering based on methylation data showed that PRNRPs form a distinct epigenetic group, separate from papillary renal cell carcinomas (pRCCs) and other major renal tumors, but with the closest affinity to clear cell papillary renal cell tumors. In addition, DNA methylation analysis suggested PRNRP may arise from the distal nephron, in contrast to pRCC, which appears to recapitulate proximal tubules. These findings support PRNRP as a subtype of renal cell neoplasm with a distinct epigenetic signature.

Background:Previous machine learning models to intraoperatively predict the molecular status of gliomas using stimulated Raman histology (SRH), such as DeepGlioma, have achieved high performance (91.5% accuracy) on curated datasets. However, when used intraoperatively, DeepGlioma (162M parameters) runs slowly on current SRH hardware and underperforms due to its lack of an image rejection mechanism and its validation on curated images. Here, we introduce SRH-Informed Glioma classificatioN with Attention Learning (SIGNAL) (27M parameters), a lighter model with a built-in attention-based rejection mechanism that outperforms DeepGlioma on uncurated clinical datasets. Methods:SIGNAL was developed using 1.56 million SRH fields-of-view from 967 adult diffuse glioma patients collected between December 2017 and July 2025. We used 412 patients from NYU for training and internal validation and a multi-institutional, international cohort of 555 patients for testing. SIGNAL uses a ResNet50 backbone pretrained using a hierarchical contrastive loss function followed by a multi-head multi-layer perceptron (MLP). Using a patch-based attention threshold of 0.6, a final MLP was trained to predict glioma subtypes: glioblastoma, oligodendroglioma, or astrocytoma. Results:SIGNAL outperformed DeepGlioma, achieving greater overall accuracy (90.10% vs. 72.59%) while running faster (16.0 vs. 6.7 patches/s). SIGNAL also outperformed DeepGlioma on all three molecular classification tasks, including IDH mutation (accuracy: 93.51% vs. 79.22%), 1p19q codeletion (93.51% vs. 88.31%), and ATRX loss (89.61% vs. 83.98%). SIGNAL's attention mechanism had a strong positive linear correlation with mean patch cellularity (r=0.96, p<0.001) and a strong negative correlation with patch blood coverage (r=-0.99,p<0.001). Finally, subtype and molecular accuracy between tumor core and margin samples were equivalent despite significantly lower patch retention in tumor margins (44.5% vs 60.2%, p<0.0001). Conclusion:SIGNAL is a lightweight model for intraoperative molecular classification of gliomas using SRH imaging. Its attention-based image quality filter allows for excellent performance, quick processing, and highly interpretable outputs critical for reliable use in intraoperative workflows. Brief 1-2 Sentence Description:We present SIGNAL, a lightweight machine learning model for intraoperative molecular classification of diffuse gliomas using stimulated Raman histology, whose core innovation is a learned attention mechanism that filters diagnostically uninformative tissue, such as blood and acellular regions, before classification, enabling robust real-world generalizability. Validated on 555 patients across four international centers, SIGNAL outperforms the previous state-of-the-art model DeepGlioma on glioma subtype classification (90.10% vs. 72.59% accuracy) while running 2.4 times faster on intraoperative hardware.

BACKGROUND:Replication-repair-deficiency is associated with increased risk of developing malignant gliomas. The aim of this study was to investigate primary mismatch repair deficient gliomas (PMMRDGs), a group of IDH-wildtype and H3-wildtype gliomas that is enriched among patients with CMMRD and Lynch syndrome. METHODS:We investigated how PMMRDGs differ from other gliomas with respect to DNA methylation profile, genomic alterations, histopathology, and clinical outcomes. RESULTS:PMMRDGs occur in pediatric, adolescents and the elderly, falling in two related methylation clusters and are characterized by a high frequency of replication repair deficiency. Histology showed multinucleated giant cells, and immunohistochemistry demonstrated loss of MMR protein expression. Survival analysis revealed long-term survival in patients with high mutational burden (>50 mut/Mb) and an intact chromosome 9p region, which was validated in an independent reference cohort. CONCLUSIONS:Overall, our findings indicate that PMMRDGs represent a distinct type of IDH-wildtype gliomas with potential for long-term survival likely driven by immune activation.

2090 Background: A subset of meningiomas has an aggressive clinical course requiring multimodal therapies. Yet, treatment options beyond surgery and radiation are limited. Somatostatin receptor 2 (SSTR2) is uniformly expressed in meningiomas and can be targeted by the radiopharmaceutical agent [ 177 Lu]Lu-DOTA-TATE ( 177 Lu-Dotatate). [ 68 Ga]Ga-DOTA-TATE ( 68 Ga-Dotatate) PET is emerging as a promising imaging modality to assess treatment response in SSTR2-expressing tumors. However, prospective clinical data supporting the use of 177 Lu-Dotatate and 68 Ga-Dotatate in treatment-refractory meningiomas is limited. Methods: In this multicenter, open-label, phase 2 clinical study (NCT03971461), adult patients with progressive intracranial meningiomas received 177 Lu-Dotatate at 7.4 GBq (200 mCi) every 8 weeks for 4 doses. The primary endpoint was 6-month progression-free survival (PFS-6). Up to 5 meningioma lesions per patient were assessed by gadolinium-enhanced T1 MRI every 12 weeks. 68 Ga-Dotatate PET was obtained before/after 177 Lu-Dotatate treatments. Lesional 68 Ga-Dotatate PET activity was assessed relative to liver/spleen or superior sagittal sinus (SSS). Results: 32 patients (female = 21, male = 11) with progressive meningiomas (WHO grade 1 = 7, grade 2 = 24, grade 3 = 1) were enrolled. Median age was 65.5 (range 41-78) years. All patients had at least one prior tumor surgery and at least one course of radiation. Overall, 177 Lu-Dotatate was well tolerated and the most common grade 3/4 adverse events were transient cytopenia in 15 patients (47%). One patient (3%) discontinued treatment due to grade 4 thrombocytopenia. PFS-6 was reached in 22 patients (69%), which includes 5 of 7 (71%) with grade 1 and 17 of 24 (71%) with grade 2 meningiomas. Best radiographic response by MRI was partial response (PR) in 5 (16%) and stable disease (SD) in 20 (63%) patients. Eight patients (16 lesions) were evaluable by paired MRI and 68 Ga-Dotatate PET pre- and post-treatment. Referenced to SSS, 68 Ga-Dotatate avidity reduced by ≥50% in 2 lesions (PR), 25-49% (minor response, MR) in 3 lesions and remained stable ( < 25% reduction and < 25% increase) in 11 lesions. Using liver/spleen as a reference (6 patients, 14 lesions), 68 Ga-Dotatate uptake was reduced in 9 lesions and stable in 5 lesions. 68 Ga-Dotatate PET assessments were generally concordant with MRI-based treatment response assessments. Conclusions: The primary endpoint (PFS-6) was met in this phase 2 study with 69% of patients achieving PFS-6, thereby surpassing established historical benchmarks. 177 Lu-Dotatate was generally well tolerated and deserves further study in patients with advanced meningiomas. Exploratory 68 Ga-DOTATATE PET studies showed decreased uptake in a subset of lesions and bears promise as an imaging biomarker to assess treatment response. Clinical trial information: NCT03971461 .

Histopathological grading of IDH-mutant astrocytomas demonstrates limited prognostic accuracy. However, DNA methylation subclassification has demonstrated improved prognostication beyond histological grading. This study aimed to investigate the associations between imaging features, tumor volumetric data, and DNA methylation grade in IDH-mutant astrocytomas. We analyzed imaging features and volumetric data for 72 patients diagnosed with IDH-mutant astrocytomas, who underwent preoperative MRI and DNA methylation profiling. VASARI features and multicompartmental volumetrics were evaluated. Logistic regression was used to identify imaging predictors of methylation subclass, WHO histologic grade, copy number variation (CNV), and CDKN2A/B homozygous deletion. Univariable and multivariable Cox proportional hazard models were also developed to assess these variables’ influence on overall survival and progression-free survival. Patients were classified into 27 methylation high-grade (A_IDH_HG) and 45 methylation low-grade (A_IDH_LG) tumors. Tumor volumes and proportions varied by methylation grade, CNV status, and WHO histologic grade, but not by CDKN2A/B status. Imaging features distinguished methylation subclasses with 75

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality. Although for stage IA, LUAD surgery is often curative, recurrence rates remain significant. Liquid biopsy enables monitoring residual disease and predicts recurrence; however, utility in stage IA LUAD is not well established. Tumor-informed whole-genome sequencing (WGS) represents a highly sensitive liquid biopsy method for the analysis of circulating tumor cell-free DNA (ctDNA) without designing and maintaining patient-specific probes. This study aimed to determine the prognostic value of genome-wide tumor-informed minimal residual disease monitoring in stage IA non-small-cell lung cancer (NSCLC) using WGS of ctDNA. WGS was performed on 42 patients with stage IA NSCLC. Tumor was sequenced at 40× and germline DNA and plasma derived ctDNA at 20×, and patient-specific mutational signatures were developed from the tumor-normal comparison and applied to ctDNA WGS at each time point using an artificial intelligence-supported pattern recognition algorithm. ctDNA results were compared with clinical recurrence. WGS ctDNA was able to predict recurrence with 0.75 sensitivity and 0.83 specificity, with median 16.7 months lead time compared with clinical or imaging recurrence. ctDNA WGS was able to distinguish between a second primary and recurrence in histologically or clinically challenging cases. Whole-genome sequencing of ctDNA can predict recurrence in the earliest clinical stage of NSCLC, identifying patients who will recur, and providing information to help guide radiological follow-up and adjuvant therapy.

BACKGROUND AND PURPOSE:A small subset of isocitrate dehydrogenase-wild-type (IDH-wt) glioblastomas (GBMs) initially present as nonenhancing, T2 FLAIR hyperintense cortical/superficial lesions on MRI, potentially leading to misdiagnosis on the initial imaging and hence delayed treatment. This study aimed to characterize the clinical and MRI features of nonenhancing IDH-wt GBMs to help radiologists in differentiating them from nonmalignant mimic diagnoses (eg, encephalitis). Additionally, the histologic, genomic, and survival profiles of nonenhancing GBMs were compared with those of enhancing GBMs. MATERIALS AND METHODS:Clinical and MRI features from 32 patients, each with nonenhancing and enhancing GBMs, and 16 patients with nonmalignant mimic differential diagnoses from a single institution and publicly available data set were retrospectively analyzed. Imaging features were reviewed using the Visually Accessible Rembrandt Images features and the split ADC sign. χ2 tests and a binary logistic regression model were used to compare nonenhancing IDH-wt GBMs with nonmalignant mimics. Histopathologic and genomic analyses were performed on institutional cases. Overall survival between nonenhancing and enhancing GBMs was compared using Kaplan-Meier analysis. RESULTS:No significant difference in age, clinical presentation, or duration of symptoms was found between nonenhancing GBMs and nonmalignant mimics. Imaging features favoring nonenhancing GBMs included a greater proportion of non-contrast-enhancing tumor (OR, 7.4), larger anterior-posterior tumor dimension (OR, 8.4), restricted diffusion (OR, 3.6), and eloquent brain involvement (OR, 3.0) while features favoring mimics included greater edema (OR, 0.07), infiltrative T1 FLAIR ratio (OR, 0.68), hemorrhage (OR, 0.76), satellite lesions (OR, 0.84), and the split ADC sign (OR, 0.89). The logistic regression model achieved a mean area under the receiver operator characteristic curve of 0.89 (SD, 0.20) (accuracy 0.84, sensitivity 0.91, specificity 0.70, and precision 0.88). Twelve of 18 nonenhancing GBMs lacked histologic evidence of necrosis or microvascular proliferation ("molecular GBMs"). Genomic profiles were similar between nonenhancing and enhancing GBMs. Median overall survival was nonsignificantly longer in nonenhancing GBMs compared with enhancing GBMs (39 versus 21 months, P = .078). CONCLUSIONS:Nonenhancing GBMs demonstrate distinct MRI features that must be recognized for early diagnosis and differentiation from nonmalignant mimics. Nonenhancing GBMs demonstrated longer overall survival compared with enhancing GBMs, though they were not statistically significant.

Abstract Spinal tumor surgery requires rapid tissue diagnosis to guide surgical decisions and further treatment strategies, yet current intraoperative methods are time-intensive and require specialized expertise. No AI systems exist for real-time spinal tumor classification during surgery. We developed SpineXtract, the first AI-powered system for rapid intraoperative spinal tumor diagnosis using stimulated Raman histology (SRH) — a label-free Raman spectromics imaging technique without tissue processing available during surgery. We created a transformer-based classifier optimized for spinal tissue characteristics to identify common tumor types: meningioma, schwannoma, ependymoma, and metastasis. The system was tested in an international, multicenter, simulated, single-arm study using existing SRH datasets (44 patients, 142 slide-images) from three international institutions, with final pathological diagnosis as reference standard. SpineXtract achieved a 92.9% macro-average balanced accuracy (95% CI: 85.5–98.2) within 5 minutes (tumor-specific accuracy range, 84.2–98.6%), while providing quantitative microscopic feedback for granular tissue analysis. Performance remained consistent across institutions (macro balanced accuracy 91.4–92.0%) and outperformed existing brain tumor classifiers by 15.6%. Our results demonstrate clinical applicability, enabling rapid intraoperative diagnosis with performance exceeding current methods, potentially transforming intraoperative diagnostic workflows in spinal tumor surgery.

Dysembryoplastic neuroepithelial tumors (DNTs) are low-grade glioneuronal tumors with FGFR1 alterations. They show significant histologic and molecular overlap with other glioneuronal tumors, complicating diagnosis. We analyzed 44 tumors that were either classified as DNT by DNA methylation (n = 37), or were diagnosed histologically as DNT but did not classify as DNT by DNA methylation (n = 7). 13/37 (35%) DNT-classifying tumors were histologically diagnosed as DNTs. High-confidence DNTs (score >0.9, 23 cases, 62%) demonstrated variable histology, most frequently DNT (39%), oligodendroglioma, and ganglioglioma and most frequently harbored FGFR1 alterations. Lower-confidence DNTs (score < 0.9, 14 cases, 38%) showed greater heterogeneity; their histologic diagnoses included papillary glioneuronal tumor, extraventricular neurocytoma, and pilocytic astrocytoma. Tumors with low confidence score exhibited diverse molecular alterations including BRAF V600E mutations, PDGFRA amplification, or multiple gene fusions. Among 7 histologically diagnosed DNTs that did not classify as DNT by methylation, most grouped with the myxoid glioneuronal PDGFRA-mutant class despite lacking canonical PDGFRA mutations. Thus, DNTs with high confidence scores are relatively homogenous but DNTs with low methylation confidence scores are heterogenous, highlighting the importance of integrated molecular profiling. Our findings also suggest that the myxoid glioneuronal tumor methylation class may require further classification of underlying drivers.