Precise molecular characterization of glioblastoma (GB) is fundamental for accurate risk stratification and therapeutic planning. DNA methylation profiling reliably identifies key molecular features, including O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and specific molecular subtypes, such as receptor tyrosine kinase (RTK) I and II, and the mesenchymal (MES) subtype. In this study, we investigated the hypothesized correlation between these molecular profiles and preferential tumor locations, which could reveal a link to underlying tumor biology. We analyzed 227 GB patients characterized by DNA methylation profiling. To map significant clusters of tumor occurrence across subtypes and subcomponents, we performed voxel-wise analysis of differential involvement, utilizing 500 permutations to correct for multiple comparisons. While uncorrected frequency differential maps suggested localization tendencies for the RTK I, RTK II, and MES subtypes, stringent statistical correction revealed only one robust association: the non-enhancing component of MES tumors showed significant clustering in the left frontal lobe, the insula, and the temporal lobe. Contrary to prior literature, we observed no significant hemispheric preference regarding MGMT promoter methylation status. Our findings challenge prior assumptions regarding the spatial distinctiveness of GB subtypes and highlight the need to further elucidate the mechanisms governing tumorigenesis and spatial growth patterns.

Purpose Meningiomas (MNGs) occur in different histopathological subtypes. The WHO grading system classifies a subset as grade 2 and 3, indicating a more aggressive course. Recent advances in risk stratification introduces an integrated molecular-morphological score (IntS), offering improved risk prediction over the traditional WHO classification. This study aims to evaluate the prognostic utility of IntS in the context of the timing of adjuvant radiotherapy (RT). Methods This retrospective study analyzed 55 patients with histologically diagnosed WHO grade 2 and 3 MNG treated with adjuvant RT. Molecular analyses using Illumina 450k Human BeadChip and Illumina 850k EPIC stratified patients into 3 risk groups (low, intermediate and high) using an integrated model that combines WHO grading, Copy Number Variations (CNVs), and Methylation Families (MF). Results After 5 years a local failure-free survival (LFFS) rate of 0% in MF-malignant MNG contrasts with a LFFS rate of 77% and 58% in MF-benign and MF-intermediate MNG. A significant correlation between CNVs and LFFS was also observed in the adjuvant setting. The IntS model revealed distinct 5-year LFFS disparities across different risk categories, underscoring the impact of combined morphological and molecular characteristics on outcome. Conclusion The integration of DNA-methylation and CNV-profiles into the IntS unified risk score offer an enhanced prognostic differentiation of MNG patients. This approach shows a promising direction for guiding the optimal timing of adjuvant RT, offering a path toward more tailored treatment strategies for meningiomas.

Leptomeningeal metastatic disease (LMD) of solid tumors represents a cancer stage with high unmet therapeutic need. Here we report results from the dose escalation part of a multicenter phase 1 trial investigating intraventricular nivolumab, now continuing in the expansion part. Eligible participants had LMD from tumors with an approval for intraveneous PD1/PDL1 therapy or high tumor mutational burden. The primary endpoint was safety across four dose levels (20, 30, 40 and 50 mg) with each cohort reviewed by an independent data safety monitoring board before escalation. The secondary endpoint was overall survival. Exploratory endpoints included participant-reported outcome measures. Of 30 enrolled participants, 24 received at least one dose (intention-to-treat population) and 18 completed predefined safety evaluations (per-protocol population). One dose-limiting toxicity occurred at 40 mg. The primary endpoint was met and the recommended fixed dose for the ongoing expansion part is 50 mg. Median overall survival (OS) was 6.6 months. The 6-month, 12-month and 18-month OS rates were 55.0% (95% confidence interval (CI): 37.5-80.6%), 33.3% (95% CI: 17.8-62.4%) and 16.7% (95% CI 6.03-46.1%), respectively. Participant-reported quality of life remained stable. Intraventricular nivolumab has demonstrated safety and feasibility (ClinicalTrials.gov: NCT05112549 ).

Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, increase clinician trust compared to traditional explainability approaches? Findings: In this randomized trial of 356 clinicians generating 7,476 trust ratings, atomic fact-checking produced a large effect on trust (Cohen's d = 0.94), increasing the proportion of clinicians expressing trust from 26.9 Meaning: Decomposing AI recommendations into individually verifiable claims linked to source guidelines produces substantially higher clinician trust than traditional explainability approaches in high-stakes clinical decisions.

This summary statement of the European Lung Cancer Conference (ELCC) 2026 presents an overview of current debates in thoracic oncology from the radiation therapy perspective. A major topic of interest in small cell lung cancer (SCLC) were bispecific T-cell engagers (biTe) in extensive stage carcinomas, especially the use of Tarlatamab. Non-small cell lung cancer (NSCLC) was discussed from the viewpoint of multidisciplinary decision making as investigated in the MDT-Bridge study. Another focus was placed on platform drug-RT trials, such as the CONCORDE study. As for thymomas and thymic cancers, the retrospective evidence available to date was summed up with three ongoing trials for PORT expected to finish recruitment within the next five years. The role of SBRT in oligometastatic disease as local ablative treatment, either as a strategy to delay the shift to the next line of systemic treatment or - upfront - in order to block oligoprogression, was discussed.

Background Large language models (LLMs) can synthesize clinical guidelines and generate diagnostic and treatment recommendations, yet clinician trust remains a barrier to adoption. Traditional approaches emphasize natural language explanations of LLM-aided recommendations and source citations, but their effectiveness in high-stakes clinical settings is uncertain. Objective To determine whether atomic fact-checking (AFC), which decomposes AI recommendations into individually verifiable claims linked to source guideline documents, increases clinician trust compared to explanations, citations, and other traditional transparency interventions. Methods A randomized controlled trial was conducted with 356 clinicians (160 radiologists, 85 radiation oncologists, 111 medical oncologists). Participants evaluated AI-generated recommendations for 21 oncology cases across seven cancer types (prostate, breast, lung, colorectal, kidney, liver, lymphoma), yielding 7,476 trust ratings. Participants were randomly assigned to one of two arms (with or without natural language explanations) and further sub-randomized to one of five transparency conditions: (1) recommendation only, (2) recommendation with explanation, (3) recommendation with source citation, (4) recommendation with explanation and citation, or (5) recommendation with explanation, citation, and AFC. The primary outcome was trust measured on a validated 5-point Likert scale. Results AFC produced significantly higher trust than all control conditions. Mean trust scores were 2.59 (95% CI, 2.54–2.64) for recommendation only, 2.84 (2.79–2.90) for explanation, 3.01 (2.97–3.05) for source citation, 3.09 (3.04–3.14) for combined explanation and citation, and 3.80 (3.76–3.84) for AFC. The effect size for AFC versus pooled controls was Cohen’s d = 0.94 (95% CI, 0.88-1.00; P < .001). The proportion expressing trust (score ≥ 4) increased from 26.9% to 66.5% with AFC (absolute increase: 39.5 percentage points; number needed to treat: 2.53). Effects were consistent across specialties (d = 0.80–1.03), cancer types (d = 0.79–1.10), and experience levels (d = 0.62–1.41). Conclusions AFC substantially increases clinician trust in AI-generated oncology recommendations. Decomposing AI outputs into verifiable claims with linked guideline sources produces larger effects than traditional transparency mechanisms.

​Background:Synchronous oligometastatic non–small cell lung cancer (sOMD) is a heterogeneous stage IV subgroup where multimodal treatment may improve outcomes.Methods:Patients with sOMD (≤5 metastases in ≤3 organs) diagnosed between July 2015 and May 2020 at five BZKF centers were retrospectively analyzed. All received systemic therapy with or without local ablative treatment (surgery or radiotherapy); those with local therapy alone were excluded. Progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan–Meier analysis, with multivariable Cox regression for prognostic factors.Results:Among 293 patients (median follow-up 33.5 months), 74% received combined systemic and local therapy. Median PFS was 9.9 months overall and significantly longer with combined therapy than systemic therapy alone (10.2 vs. 8.4 months, p=0.02). Median OS was 26.4 months, with a trend favoring combined therapy (31.1 vs. 20.0 months). Surgery as local treatment was associated with better PFS and OS than radiotherapy. Omission of local therapy increased risks of progression (HR 1.55) and death (HR 1.52). Poor ECOG status and older age were also linked to worse outcomes.Conclusion:Combining systemic therapy with local ablative treatment improves outcomes in selected patients with sOMD compared to systemic therapy alone. These findings support integrating local treatments into care strategies and highlight the need for prospective studies on optimal sequencing and patient selection.

PURPOSE:Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT). The ability to predict RP would facilitate treatment modifications. This study investigates the predictive capacity for symptomatic RP (Common Terminology Criteria for Adverse Events ≥ 2) employing Radiomics and Dosiomics models. METHODS AND MATERIALS:Computed tomography scans, along with physical and 2-Gy equivalent dose volumes (EQD2), dose-volume histograms, and clinical parameters, were evaluated for 708 multicenter lung cancer patients, among whom 89 developed RP ≥ 2. The training cohort consisted of 441 patients from the prospective RTOG 0617 trial. External validation was carried out on 267 patients from the prospective REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) study. A Random Forest classifier was employed, with feature selection executed within the inner loop of a 10x5-fold nested cross-validation (nCV) utilizing the minimum-redundancy-maximum-relevance algorithm. To address class imbalances, synthetic oversampling and undersampling were implemented using SMOTE-Tomek. The QUANTEC Normal Tissue Complication Probability model served as a reference. Additionally, the experiments were stratified by subgroups (standard/high-dose and 3-dimensional conformal RT (3D-CRT)/intensity-modulated RT (IMRT). RESULTS:The best radiomics model identified in the nCV was trained on the standard-dose subgroup achieved a test ROC-AUC of 0.56. The baseline Normal Tissue Complication Probability model showed a predictive performance with a ROC-AUC of 0.56, which was largely dependent on radiation technique (ROC-AUCS: 3D-CRT: 0.75, IMRT: 0.50). The Dosiomics EQD2 model, trained on the full training cohort, attained the second-best performance in the nCV, demonstrating the same technique-dependence (ROC-AUC of 0.75 vs. 0.39). Using a Dosiomics EQD2 ensemble model trained separately on 3D-CRT and IMRT subgroups increased overall performance to a testing ROC-AUC of 0.61, outperforming other modeling strategies for IMRT, while being outperformed by clinical models for 3D-CRT. CONCLUSIONS:This prospective trial-based study reveals an overall limited predictive capacity of radiomics and dosiomics models and a large influence of radiation technique. IMRT-specific models should be investigated further.

Background:Evidence on the influence of eloquent brain areas on the effectiveness and side effects of radio-therapy (RT) remains limited. This study evaluated the relationship between eloquent brain regions, radiation dose, and tumor recurrence patterns in glioma patients. Methods:Preoperative navigated transcranial magnetic stimulation (nTMS) mapping of language and motor function, complemented by nTMS-based tractography, was performed. Magnetic resonance imaging of tumor recurrence was co-registered with RT treatment plans and functional nTMS data. Tumor growth direction, radiation dose to eloquent structures, and clinical outcomes were analyzed. Results:Seventy-two patients with glioblastoma, aged 57.7 ± 14.8 years, were included. Tumor recurrence toward eloquent brain areas, assessed either by volumetric or linear measurements, indicated growth affecting motor function in 68.1% and language function in 79.3% of patients. Following RT, new motor deficits occurred in 3/48 patients (6.3%) and language deterioration in 3/20 (15.0%). The mean dose to the corticospinal tract was 10.1 Gy in patients with motor decline versus 3.7 Gy in those without (P = .137). For language fiber tracts, corresponding doses were 34.1 Gy and 15.1 Gy (P = .073). Conclusions:Tumor recurrence toward eloquent brain areas was observed, with high radiation doses to eloquent brain areas being associated with higher rates of neurological deterioration. These findings create an ambiguous situation regarding the application of high radiation doses to the resection cavity facing eloquent brain areas while simultaneously ensuring optimal dose gradients to spare those.

BACKGROUND:Synchronous oligometastatic non-small cell lung cancer (sOMD) is a heterogeneous subgroup of stage IV disease in which multimodal treatment may improve outcomes. METHODS:Patients with sOMD (≤5 metastases in ≤ 3 organs) diagnosed between July 2015 and May 2020 at five lung cancer centers were retrospectively analyzed. Patients receiving systemic therapy alone or multimodal therapy consisting of systemic therapy plus local treatment (surgery and/or radiotherapy) were included; those treated with local therapy alone were excluded. Progression-free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier method according to treatment modality and treatment sequence. Multivariable Cox regression identified independent prognostic factors. RESULTS:Among 293 patients (median follow-up, 33.5 months), 74% received multimodal therapy. Median PFS was 9.9 months overall and was significantly longer with multimodal therapy than with systemic therapy alone (10.2 vs. 8.4 months; p = 0.02). Median OS was 26.4 months and numerically favored multimodal therapy (31.1 vs. 20.0 months). Among patients receiving local treatment, surgery was associated with longer PFS and OS than radiotherapy. Complete local treatment of all metastatic lesions was associated with improved OS compared with incomplete treatment (31.6 vs. 16.3 months). In multivariable analysis, omission of local therapy independently predicted worse PFS (HR 1.55) and OS (HR 1.52). Older age and poorer ECOG performance status were also independently associated with inferior outcomes. CONCLUSIONS:Multimodal therapy combining systemic and local ablative treatment was associated with improved outcomes compared with systemic therapy alone. A sequential approach, with systemic therapy followed by local ablative treatment, appeared to provide the greatest benefit. Prospective studies are warranted to define the optimal treatment sequence and refine patient selection.