Radiotherapy and Oncology

Optimizing radiotherapy dose distributions remain a resource-intensive bottleneck. Existing AI-based dose prediction methods often have limited generalizability because they rely on small, heterogeneous datasets. We present nnDoseNetv2, an auto-configured, end-to-end framework for dose prediction across diverse disease sites (head and neck, prostate, breast, and lung), prescription levels (1.5-84 Gy), and treatment modalities (IMRT, VMAT, and 3D-CRT). By integrating machine-specific beam geometry with 3D structural information, the framework is designed to generalize across varied clinical scenarios. A single multi-site model was trained on 1,000 clinical plans. On sites seen during training, performance was comparable to specialized site-specific models. On unseen sites (liver and whole brain), the model outperformed site-specific models, with mean absolute errors of 2.46% and 6.97% of prescription, respectively. These results suggest that geometric awareness can bridge disparate anatomical domains while eliminating the need for site-specific model maintenance, providing a scalable and high-fidelity approach for personalized radiotherapy planning.

Humanized mice (hu-mice), which recapitulate the human immune system, have become increasingly important for preclinical immunotherapy studies. Among these models, the human peripheral blood mononuclear cells (PBMC)-engrafted hu-mice model is the simplest and fastest. However, its utility is hindered by the development of lethal graft-versus-host disease (GvHD) and the insufficient reconstitution of human leukocytes. To address these limitations, we developed PBMC hu-mice models using a novel strain, NOD-CD47nullRag2nullIL-2r{gamma}null (RTKO) focusing on the immunological defects of the NOD strain and the immunotolerance provided by CD47 deficiency. Six-week-old female NOD-Rag2nullIL-2r{gamma}null (RID) and RTKO mice were intravenously injected with three different PBMC doses (3x106, 5x106, and 1x107 cells). At standard doses (5x106 and 1x107 cells), RTKO mice exhibited enhanced engraftment of human leukocytes, though GvHD was more severe compared to the RID strain, resulting in a limited experimental window. However, in a subsequent trial using a lower dose of PBMCs (3 x 106 cells), RTKO mice demonstrated notable advantages, including stable reconstitution of human leukocytes, milder GvHD symptoms without life-threatening lesions, and a markedly prolonged experimental window. Considering the difficulties in generating hematopoietic stem cell (HSC)-engrafted hu-mice, the extended experimental window provided by this model, which is comparable to HSC hu-mice, is a significant improvement. Moreover, the radiation tolerance conferred by the Rag gene mutation in this model offers another advantage for radiotherapy research. Consequently, the low-dose PBMC RTKO model serves as a versatile and valuable platform for a broad spectrum of immunotherapy studies, especially in the field of immuno-oncology.

Abstract: Purpose: Recurrence in high grade glioma (HGG) predominantly occurs within the high dose radiation field, raising the question of whether treatment failure reflects limitations in radiation target delineation or is driven by intrinsic tumor biology. This study evaluated recurrence patterns following standard chemoradiotherapy and their treatment implications. Material and Methods: This retrospective single center study included 41 patients with histologically confirmed HGG treated with surgery followed by radiotherapy with concurrent and adjuvant temozolomide (TMZ). Patients were followed through August 2018; those with recurrence were included in the analysis. Recurrence patterns were classified based on their spatial relationship to the 60 Gy isodose line as central, infield, marginal, or distant. Survival outcomes were estimated using the Kaplan-Meier method and compared using the log rank test. Results: The most common pattern of recurrence was central (15 patients, 36.5%), followed by infield (11, 26.8%), distant (6, 14.6%), marginal (5, 12.1%), and multicentric (4, 9.8%). Central and in field recurrences (local failures) accounted for 26 patients (63%). Median overall survival (OS) was 27 months, and median progression-free survival (PFS) was 12 months. Survival differed significantly by recurrence pattern (log-rank p = 0.018), with marginal recurrence associated with more favorable outcomes. Conclusion: The predominance of central and infield recurrences within the high-dose region suggests that treatment failure in HGG is not solely explained by inadequate target delineation and may also be driven, in part, by intrinsic tumor biology, including radioresistant subpopulations and tumor heterogeneity. Future strategies may benefit from incorporating biologically guided approaches alongside optimization of radiation treatment parameters.

BACKGROUNDSubcutaneous white adipose tissue (scWAT), a key metabolic and endocrine organ, is inevitably exposed during radiotherapy (RT). While RT is a cornerstone of cancer treatment, its efficacy is limited by toxicity to surrounding healthy tissues. Ultra-high dose rate (FLASH) RT has emerged as a promising modality capable of preserving tumor control while reducing normal tissue damage - the so-called FLASH effect. Clinical evidence indicates that childhood exposure to conventional (CONV) RT is associated with long-term dysmetabolism and WAT dysfunction. However, the impact of FLASH-RT on WAT has not been investigated. AIMTo compare the effects of FLASH- and CONV-RT on adipocyte function and scWAT homeostasis, and to identify molecular and structural changes associated with each modality. METHODSWe evaluated the effects of FLASH- and CONV-RT on adipocytes and scWAT using a dedicated linear accelerator capable of delivering both modalities. Experiments were performed in the human SGBS preadipocyte/adipocyte cell line and in a mouse model subjected to proximal hind limb irradiation, with analyses conducted 70 days post-exposure. RESULTSRT impaired adipogenic differentiation in a dose-dependent manner, with a relative sparing effect of FLASH at 4-8 Gy. Mature adipocytes exhibited radioresistance, with protection by FLASH at 8 Gy. In vivo, both regimens reduced fat mass without affecting body weight, with greater loss following CONV-RT. Transcriptomic profiling of scWAT revealed inflammatory and neurodegenerative signatures after CONV-RT, whereas FLASH-RT induced minimal transcriptional changes. Histological and ultrastructural analyses confirmed increased cellular damage, vacuolization, lipid spill-over, and reduced PLIN1 expression, predominantly in CONV-treated mice. CONCLUSIONSWAT homeostasis is sensitive to conventional RT, whereas FLASH-RT better preserves tissue structure and function, with implications for long-term metabolic health in cancer survivors.

Purpose: Manual verification of AI-based auto-contouring is labor-intensive and prone to fatigue-related errors. This study developed the large language model (LLM)-based automated Quality Assurance (QA) for auto-contouring (LAQUA) system using a multimodal LLM, Gemini 2.5 Pro, and evaluated its feasibility as a clinical primary screening tool to streamline the QA workflow. Methods: Twenty male pelvic CT scans from an open dataset were utilized. Three distinct auto-contouring software packages (OncoStudio, RatoGuide prototype and syngo.via) were evaluated. Auto-contouring results for each slice were exported as PDF images with overlaid contours and input into Gemini 2.5 Pro. The LLM was instructed to rate the contour quality on a 5-point clinical scale (5: Optimal; 4: Acceptable; 3: Suboptimal; 2: Unacceptable; redraw from scratch; 1: Unacceptable; organ not detected). Using evaluations by two board-certified radiation oncologists as ground truth, Spearman's rank correlation coefficients ({rho}) and weighted kappa coefficients ({kappa}) were calculated. Additionally, to assess screening performance, sensitivity and specificity were calculated by dichotomizing the scores into "Pass" and "Fail" using two different cutoffs (scores [≥] 3 and [≥] 4 as "Pass"). Finally, the alignment of the rationales provided by the LLM with the auto-contouring quality was evaluated by two board-certified radiation oncologists. This was conducted using a Likert scale assessing four domains (error detection, hallucination, clinical relevance, and anatomical understanding), each scored out of 2 points. Results: The LAQUA system demonstrated moderate to strong agreement with expert judgments across all evaluated organs ({rho}: 0.567 - 0.835; quadratic weighted {kappa} : 0.639 - 0.804), with the rectum showing the highest correlation. Regarding screening performance, a cutoff of [≥]3 as "Pass" achieved the highest sensitivity and specificity in specific subgroups, but with wide 95% confidence intervals (CIs). A cutoff of [≥]4 as "Pass" narrowed the CIs, yielding the highest sensitivity in the rectum (0.976) and the highest specificity in the left femoral head (0.933). Qualitatively, the LLM's rationales achieved an overall mean score of 1.70 {+/-} 0.48 (out of 2), with 155 of 291 outputs receiving perfect scores across all criteria. Conclusions: The LAQUA system demonstrated substantial agreement with expert evaluations in AI-based auto-contouring quality assessment. While potential overestimation bias (risk of missing "Fail" cases) warrants caution, the observed sensitivity suggests its feasibility as a primary screening QA tool to efficiently filter acceptable contours, thereby reducing the clinical workload.

Radiation-induced intestinal injury is a widely used model for studying mechanisms regulating tissue injury and regeneration. Traditionally, Cesium (137Cs) radiation has been used in research applications, but over the past decade, X-ray irradiation has become increasingly favored due to its improved safety and non-radioactive profile. Since each type of radiation has distinct physical characteristics that drive its performance, we sought to systematically compare the effects of the X-ray and 137Cs irradiators on intestinal epithelial injury and regeneration. Using established in vitro models, including colorectal cancer cell lines such as HCT116, RKO, and DLD-1, and mouse intestinal organoids, alongside an in vivo model, Bmi1-CreER;Rosa26eYFP, we evaluated differences in transcriptional, protein, and histopathological responses to irradiation. Our results demonstrate that X-ray produced intestinal injury and regenerative responses comparable to those induced by 137Cs, supporting its reliability as an alternative modality for studying intestinal radiation.

Definitive radiotherapy (RT) for prostate cancer (PC) with dose intensification and/or focal boosting has excellent oncologic outcomes, but many patients experience adverse events. Dose escalation to the whole prostate improves outcomes at the expense of increased late adverse events. Intraprostatic recurrence after definitive RT typically occurs at the site of the primary tumor, suggesting that dose to the site of the dominant lesion is an important predictor of future failure. The efficacy and safety of tumor-focused RT compared to that of standard RT for definitive treatment of localized PC has not been assessed. RadTARGET (RAdiation Dose TAiloRing Guided by Enhanced Targeting) is a phase II randomized trial that aims to demonstrate superior safety of image-guided, tumor-focused RT compared to standard RT for acute genitourinary (GU) or gastrointestinal (GI) in the setting of definitive RT for intermediate- and high-risk PC. The study intervention is image-guided, tumor-focused RT with dose intensification of cancer visible on imaging and dose de-intensification to remaining prostate. Patients will be randomized to two arms: those who receive standard RT dose and those that receive tumor-focused RT. The study population will be patients with intermediate- or high-risk PC planning to undergo definitive RT with or without systemic therapy. The primary endpoint to compare between randomized arms is acute GU or GI grade [≥]2 adverse events. Participant and study duration are 5 years and 8 years, respectively. RadTARGET will compare the efficacy and safety of tumor-focused RT to that of standard RT for definitive treatment of localized PC. We hypothesize that the tumor-focused approach will substantially reduce adverse events after prostate RT while retaining high efficacy. If this hypothesis is confirmed, we will conclude that a phase III randomized control trial is warranted to formally establish oncologic non-inferiority compared to the current standard of whole-gland dose escalation.

Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of smoking and pre-existing cardiovascular diseases (CVD). The objectives of this study were to characterize the incidence of CVAEs in this population and evaluate machine learning (ML) models for CVAEs risk stratification and mortality prediction. Methods A retrospective study was conducted among Appalachian patients with lung cancer treated with definitive CRT at a single institution between 2013 and 2025. Baseline clinical variables, including demographics, smoking status, pre-existing CVD, and post-CRT CVAEs were collected. Heart dosimetric parameters were also obtained. ML models [Random Forest (RF), Gradient Boosting (GBM), Support Vector Machine (SVM), Logistic Regression (LR)] were trained using 5 fold cross validation and evaluated using AUC, sensitivity, specificity, and F1 score. Feature importance was assessed using permutation analysis. Wilcoxon and Chi-squared tests were used for descriptive comparisons. Results Eighty-six patients (mean age 66 years, 47% male) were included. At diagnosis, 80% (n=69) had NSCLC and 20% (n=17) had LS-SCLC. CVAEs occurred in 51 patients (59%). The most frequent events were NSTEMI (n=15, 29.4%), pericardial disease (n=15, 29.4%), and arrhythmia (n=8, 15.7%). Mean heart dose was higher in the CVAE group (13.4 vs 9.4 Gy, p=0.27). For CVAE prediction, GBM achieved the highest AUC (0.55, 95% CI 0.44-0.69) and sensitivity (75%), while RF showed the highest sensitivity (80%, 95% CI 69-90%). Key predictors included age and cardiac dosimetrists (Heart V20, V40, V50, and mean heart dose). For mortality prediction, RF achieved the highest discrimination (AUC = 0.63, 95% CI 0.496-0.750). Age, cardiac dosimetry, disease stage, and cardiovascular comorbidity were the most influential predictors. Conclusion High incidence of CVAEs occurred among patients with lung cancer treated with CRT in this Appalachian cohort. While ML models demonstrated modest predictive performance, tree-based approaches demonstrated high sensitivity for identifying patients at risk for CVAEs and mortality. Age and cardiac radiation dose metrics consistently emerged as key predictors, highlighting the importance of cardiac dose optimization and ML-based risk stratification for cardio-oncology surveillance.

International guidelines for low-frequency electromagnetic field exposure (LF EMF) are primarily intended to prevent substantiated adverse effects. In the frameworks, limits on internal electric fields are linked to external exposure levels through computational dosimetry. However, the relationship between internal electric fields and these adverse effects remains incompletely understood. In particular, current approaches often overlook the morphological complexity and diversity of cortical neurons, which may limit the realism of neuronal activation estimates used to support these assessments. This study evaluates LF EMF-induced neural activation using 25 morphologically realistic neuron models spanning all cortical layers, embedded within 11 detailed human head models. The internal electric fields were simulated for uniform magnetic field exposures (100 Hz-100 kHz) along the three anatomical directions, and excitation thresholds were computed using a multi-scale framework combining voxel-based dosimetry with biophysical neuron simulations. A real-world exposure scenario involving a child near an acousto-magnetic article-surveillance deactivator was also analyzed. Thresholds varied across cell type, morphology, cortical location, subject anatomy, frequency, and exposure direction, with L2/3 pyramidal, L4 basket, and L5 thick-tufted pyramidal cells showing the lowest thresholds. Despite this variability, all simulated thresholds were conservative with respect to the basic restrictions and dosimetric reference limits set by IEEE ICES and ICNIRP. The smallest margin occurred at 100 kHz, where the threshold remained a factor of 2.8 above the corresponding limit. These findings indicate that current LF EMF exposure limits remain conservative when evaluated using highly detailed, morphology-based CNS activation models.

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Radioresistance, partly mediated by glioma stem-like cells, represents a major clinical challenge which could be overcome by the identification of the modulators of radioresistance. Existing CRISPR screens in human GBM models have largely used two-dimensional cultures with short-term viability readouts, failing to capture the long-term clonogenic behaviour underlying tumour recurrence after radiotherapy. Method: We developed ClonoScreen3D-CRISPRi, combining CRISPRi-mediated gene knockdown with three-dimensional clonogenic survival assays. Two GBM cell lines (G7 and GBML20), differing in MGMT promoter methylation status, were engineered to express the KRAB-dCas9 editor. Nine candidate radiosensitivity modifiers, selected through transcriptomic analysis, pharmacological studies, and literature review, were examined in both lines. Target validation was performed using full radiation dose-response assays and a pharmacological inhibitor. Results: The majority of candidate genes significantly altered survival fraction following irradiation in both cell lines. Knockdown of NFKB2, RELB, and CDK9 produced the most potent radiosensitization, with sensitizer enhancement ratios of 1.39-1.70 in validation studies, exceeding those of established radiosensitizers including PARP and ATM inhibitors. Notably, knockdown of these genes induced no significant cytotoxicity in the absence of radiation. Pharmacological validation using an IKK inhibitor confirmed these findings, implicating non-canonical NF-{kappa}{beta} signalling and CDK9-dependent transcriptional elongation as critical adaptive mechanisms in GBM radioresistance. Conclusions: ClonoScreen3D-CRISPRi is a scalable, physiologically relevant platform for identifying genetic modifiers of radioresistance. The non-canonical NF-{kappa}{beta} pathway and CDK9 represent promising radiosensitizing targets, and larger screens could enable systematic prioritisation of candidates for clinical translation.

Purpose: To develop a deep neural network-based, AIF-free, perfusion estimation method (QTMnet) for improved performance on glioma classification. Methods: A globally defined arterial input function (AIF) is needed to recover perfusion parameters in the two-compartment exchange model (2CXM). We have developed Quantitative Transport Mapping (QTM) to create an AIF-independent estimation method. QTM estimation can be formulated using deep neural networks trained on synthetic DCE-MRI data (QTMnet). Here, we provide a fluid mechanics-based DCE-MRI simulation with exchange between the capillaries and extravascular extracellular space. We implemented tumor ROI generation to morphologically characterize tissue perfusion. We compared our QTMnet implementation with 2CXM on 30 glioma human subjects, 15 of which had low-grade gliomas, and 15 with high-grade glioblastomas. Results: QTMnet outperforms (best AUC: 0.973) traditional 2CXM (best AUC: 0.911) in a glioma grading task. Conclusion: The AIF-independent QTMnet estimation provides a quantitative delineation between low-grade and high-grade gliomas.

BackgroundGallium (Ga) is a promising anti-tumor agent; however, its precise molecular targets in osteosarcoma remain debated. While current paradigms largely attribute its toxicity to reactive oxygen species (ROS) and ferroptosis, understanding its true mechanism is essential for overcoming therapeutic resistance. This highlights the need for interdisciplinary approaches, such as metabolomics, to unveil novel vulnerabilities in cancer metabolism. MethodsWe employed an interdisciplinary strategy utilizing high-resolution liquid chromatography-mass spectrometry (LC-MS) metabolomics and 13C2-glutamine stable isotope tracing in osteosarcoma cells to elucidate the cytotoxic mechanisms of gallium nitrate. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) was utilized for elemental mapping, and in silico modeling was applied to evaluated metal binding dynamics. Furthermore, synergistic effects were tested by combining gallium with the DNA-damaging agent cisplatin. ResultsOur metabolic profiling revealed a profound bifurcation characterized by the systemic depletion of glycolysis and pentose phosphate pathway intermediates, coupled with a novel ribonucleotide accumulation bottleneck. The observed distinct signature strongly implicated ribonucleotide reductase (RNR) as the primary enzymatic target. In silico modeling and SEM-EDS visually and thermodynamically confirmedthat gallium acts as a structural decoy for iron within the RNR active site. The co-localization induces functional iron starvation rather than canonical ferroptosis. Furthermore, isotope tracing confirmed that elevated ROS is a consequence of overall metabolic failure, not the primary driver of cell death. Crucially, gallium functioned as a metabolic DNA repair inhibitor, synergizing potently with cisplatin to prevent the repair of platinum-induced DNA lesions. ConclusionsGallium selectively sensitizes highly proliferative sarcoma cells by disrupting RNR-mediated DNA precursor synthesis, while sparing normal osteoblasts. Leveraging metabolomics to uncover this state of functional iron starvation provides a rational, interdisciplinary framework for developing gallium-based combination therapies designed to break platinum resistance in clinical oncology.

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non overlapped configurations specifically for Tc 99m MPI parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal to noise ratio (SNR), contrast to noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSC's superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc 99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

The Bayesian Ordered Lattice Design (BOLD) method for Phase I clinical trials is extended to address an important challenge. It is widely understood that conventional Phase I trial designs are not consistently effective in determining safe and active dose levels. The US FDA launched the Project Optimus, aimed at reforming the paradigms of dose optimization and selection. We propose a backfill BOLD design (BF-BOLD) that centers on BOLD for dose-finding but also adds an activity evaluation for each patient. Our method for determining the optimal biological dose (OBD) first involves identifying the maximum tolerated dose (MTD) and then assessing activity rates among dose levels below the identified MTD. This approach is straightforward and does not require complex statistical modeling. The results of the simulation indicate that performing dose-finding trials with backfilling can both enhance safety and activity assessment, thereby improving treatment sustainability while also preserving the potential for efficacy of the Recommended Phase II Dose (RP2D). We also demonstrate the applicability of the backfill design for reducing overdose rates, and as a more attractive alternative to small-scale dose expansion trials that follow dose escalation. Backfill designs are an important design approach for early phase trials.

BackgroundTreatment optimization in HPV-associated oropharyngeal cancer (OPSCC) remains challenging, as recent de-escalation trials have shown limited success. Current patient selection strategies based on smoking history and TNM classification are insufficient, highlighting the need for robust, standardized prognostic biomarkers. We report the first validation of the Immunoscore (IS) for prognostic stratification in HPV-associated OPSCC. Patients and methodsWe analyzed 191 HPV-associated (p16+ and HPV DNA/RNA+) OPSCC patients from an international multicenter cohort (2015-2024), comprising a French monocentric retrospective training cohort (N = 48) and three validation cohorts: French monocentric retrospective (N = 48), French multicenter prospective (N = 50), and US multicenter retrospective (N = 45). IS is a standardized digital pathology assay quantifying CD3lJ and CD8lJ densities in tumor cores and invasive margins, with cut-offs defined in the training cohort and validated across cohorts. Associations with disease-free survival (DFS), time to recurrence (TTR) and overall survival (OS) were assessed, alongside 3RNA-seq and sequential immunofluorescence profiling of immune composition. ResultsMedian age 65; 80% male; 74% smokers; 66% T1-2; 82% N0-1 (AJCC8th). IS-High patients demonstrated superior 3-year DFS in the training and validation cohorts 1-3 (all log-rank P < 0.05). Multivariable analysis identified IS-Low as the strongest independent risk factor for DFS (HR 9.03; 95% CI: 4.02-20.31; P < 0.001). The model combining IS with clinical factors showed higher predictive accuracy for DFS (C-index 0.82) than clinical variables alone (0.7; P < 0.0001). Similar findings were observed for TTR and OS. IS-High tumors showed markedly higher enrichment of lymphoid and myeloid immune cell populations, contrasting with immune-poor signatures in IS-Low tumors. ConclusionsIS is a robust biomarker that outperforms standard clinical variables in both prognostic and predictive accuracy. The enriched cytotoxic immune infiltrate in IS-High tumors explains favorable outcomes and supports their suitability for treatment de-escalation. Prospective validation is warranted.

Background: Early-phase oncology development increasingly depends on integrated interpretation of clinical outcomes, translational biomarkers, and pharmacokinetic exposure rather than toxicity alone. This shift has created a need for reproducible analytical workflows that can combine heterogeneous trial data into traceable, analysis-ready outputs suitable for exploratory review and early decision support. Objective: To develop a reproducible Python-based workflow that simulates a plausible early-phase oncology study, integrates clinical, biomarker, and pharmacokinetic data, and generates analysis-ready datasets, visual summaries, and exploratory predictive models relevant to early development analytics. Methods: A workflow was constructed to simulate an early-phase oncology cohort of 120 patients distributed across multiple dose levels. Three synthetic raw data sources were generated, including patient-level clinical data, baseline biomarker data, and longitudinal pharmacokinetic profiles. These sources were merged into a single analysis-ready dataset containing derived variables such as tumor percent change from baseline, clinical-benefit status, exposure summaries, adverse-event indicators, and survival outcomes. The workflow produced structured tables, patient listings, waterfall plots, Kaplan-Meier-style survival curves, biomarker-response visualizations, pharmacokinetic profile plots, and exploratory machine-learning outputs. Results: The final integrated dataset contained 120 patients and 30 variables. Median survival across the simulated cohort was 243.8 days, and higher dose groups showed improved median survival and greater clinical benefit relative to the low-dose group. Clinical benefit increased from 8.6% in the low-dose group to 29.0% in the medium-dose group and 45.2% in the high-dose group. Higher baseline LDH, CRP, and ctDNA fraction tracked with less favorable tumor-response trajectories, whereas higher exposure, reflected by AUC and Cmax, associated with improved disease control. Pharmacokinetic profiles showed clear dose-dependent separation. Grade 3 or higher adverse-event rates remained within a plausible exploratory range across dose groups. A random-forest model for clinical benefit achieved an exploratory ROC AUC of 0.845, while a logistic-regression model for strict responder status could not be fit because no simulated patient met the prespecified objective response threshold. Conclusions: This proof-of-concept demonstrates that a transparent Python workflow can generate a coherent early-phase oncology analytical ecosystem from synthetic inputs. The workflow supports integration of heterogeneous data streams, derivation of analysis-ready variables, production of interpretable outputs, and exploratory modeling in a reproducible framework. Although the simulated responder prevalence was too low to support objective response modeling, this limitation itself highlights the importance of simulation calibration for downstream analytical validity. The framework provides a practical Health Informatics demonstration of how early oncology trial data can be structured and analyzed for exploratory translational decision support.

Prehabilitation (PRH) is a preoperative process aimed at optimizing patients functional capacity to improve surgical outcomes and overall well-being. While its physical and cognitive benefits are increasingly documented, its emotional impact, particularly in neuro-oncology patients, remains less explored. This study assessed the psychological effects of a PRH program on 29 brain tumor patients. The primary outcome, emotional well-being, was measured using quality of life and emotional distress metrices. Secondary outcomes included perceived stress levels and control attitudes. Additionally, qualitative data from structured interviews provided further insights into the psychological effects of the intervention. The results indicated significant improvements in quality of life and reductions in emotional distress, particularly among women. While perceived stress levels remained stable, control attitudes showed an increase. Qualitative analysis further highlighted the positive changes in the control sense and identified additional factors, such as the importance of social support sources during the PRH process. Overall, these findings suggest that PRH interventions play a significant role in enhancing emotional well-being among neuro-oncological patients in the preoperative phase. These results underscore the importance of implementing comprehensive and personalized PRH approaches to optimize clinical status both before and after surgery, thereby promoting sustained psychological benefits in this population. This study is based on data collected at Institut Guttmann in Barcelona in the context of the Prehabilita project (ClinicalTrials.gov identifier: NCT05844605; registration date: 06/05/2023).

Predicting response to induction chemotherapy (IC) and overall survival (OS) is critical for optimizing treatment in patients with locally advanced nasopharyngeal carcinoma (LANPC). This study aimed to develop and validate a multi-task deep learning model integrating pretreatment MRI and whole slide images (WSIs) to predict IC response and OS in LANPC. Pretreatment MRI and WSIs from 404 patients with LANPC were retrospectively collected to construct a multi-task model (MoEMIL) for the simultaneous prediction of early IC response and OS. MoEMIL employed multi-instance learning to process WSIs, PyRadiomics and a convolutional neural network (ResNet50) to extract MRI features, and fused multimodal features through a multi-gate mixture-of-experts architecture. Clustering-constrained attention multiple instance learning and gradient-weighted class activation mapping were applied for visualization and interpretation. MoEMIL effectively stratified patients into good and poor IC response groups, achieving areas under the curve of 0.917, 0.869, and 0.801 in the train, validation, and test sets, respectively, and outperformed the deep learning radiomics model, the pathomics model and TNM staging. The model also stratified patients into high- and low-risk OS groups (P < 0.05). MoEMIL shows promise as a decision-support tool for early IC response prediction and prognostication in LANPC. Author SummaryWe have developed a deep learning model that integrates two types of medical images, including magnetic resonance imaging (MRI) and digital pathological slices, to simultaneously predict response to induction chemotherapy and prognosis in patients with locally advanced nasopharyngeal carcinoma. Current treatment decisions primarily rely on traditional tumor staging (TNM), which often fails to comprehensively reflect the complexity of the disease. Our model, named MoEMIL, was trained and tested on data from 404 patients across two hospitals and consistently outperformed both single-model approaches and TNM staging methods. By identifying patients who exhibit poor response to induction chemotherapy or higher prognostic risk, our tool can assist clinicians in achieving personalized treatment, enabling intensified management for high-risk patients and avoiding unnecessary side effects for low-risk patients. Additionally, we visualize the models reasoning process through heat map generation, which highlights the image regions exerting the greatest influence on prediction outcomes. This work represents a step toward more precise treatment for nasopharyngeal carcinoma; however, larger-scale prospective studies are required before the model can be integrated into routine clinical practice.

Background. Adult diffuse glioma is a representative class of primary brain tumors for which accurate MRI-based tumor segmentation is indispensable for treatment planning. Conventional automated segmentation methods have relied primarily on image information and spatial prompts, and auxiliary clinical information that is routinely acquired in clinical practice has not been sufficiently exploited as an input. Objective. Building on a dual-prompt-driven Segment Anything Model (SAM) extension framework that fuses visual and language reference prompts, we propose a method that integrates patient demographics, unsupervised molecular cluster variables derived from TCGA high-throughput profiling, and histopathological parameters as learnable prompt embeddings, and we evaluate its effect on the accuracy of lower-grade glioma (LGG) MRI segmentation. Methods. An auxiliary prompt encoder converts clinical metadata into high-dimensional embeddings that are fused with the prompt representations of Segment Anything Model (SAM) ViT-B through a cross-attention fusion mechanism. The TCGA-LGG MRI Segmentation dataset (Kaggle release by Buda et al.; n = 110 patients; WHO grade II-III) was split at the patient level (train/val/test = 71/17/22) using three different random seeds, and the three slices with the largest tumor area were extracted from each patient. To avoid pseudo-replication arising from multiple slices per patient and repeated measurements across seeds, our primary analysis aggregated Dice and 95th-percentile Hausdorff distance (HD95) to the patient x seed unit (n = 66); secondary analyses at the unique-patient level (n = 22) and at the per-slice level (n = 198) are also reported. Pairwise comparisons used paired t-tests with Bonferroni correction (k = 3) and Wilcoxon signed-rank tests, and a permutation test (K = 30) served as an auxiliary check of effective use of the auxiliary information. Results. At the patient x seed level (n = 66), Proposed (full clinical) achieved a Dice gain of +0.287 over the zero-shot SAM ViT-B baseline (paired-t p = 4.2 x 10^-15, Cohen's d_z = +1.25, Bonferroni-corrected p << 0.001; Wilcoxon p = 2.0 x 10^-10), and HD95 improved from 218.2 to 64.6. Because zero-shot SAM is not designed for domain-specific medical segmentation, the large absolute HD95 gap largely reflects the expected domain gap rather than a competitive baseline. The additional contribution of the full clinical configuration over the demographics-only configuration was Dice = +0.023 (paired-t p = 0.057, Bonferroni-corrected p = 0.172), which did not reach statistical significance at the patient level and is reported as a directional trend. The permutation test (K = 30, seed 2025) yielded real-metadata Dice = 0.819 versus a shuffled-metadata mean of 0.773, giving an empirical p = 0.032 = 1/(K + 1), which is at the resolution limit of this test and should therefore be interpreted as preliminary evidence. Conclusions. Integrating auxiliary clinical information as multimodal prompts produced a large improvement over the zero-shot SAM baseline on this LGG cohort. More importantly, a robustness analysis showed that Proposed (full clinical) outperformed the trained Base (no auxiliary information) under all tested spatial-prompt conditions, including perfect centroid (+0.014), and that the advantage was most pronounced in the prompt-free regime (+0.231, p = 0.039), where the base model collapsed but the proposed model maintained meaningful segmentation by leveraging clinical metadata alone. The additional contribution of molecular and histopathological information beyond demographics was not statistically resolved at the patient level (+0.023, n.s.). Establishing clinical utility will require external validation on larger multi-center cohorts and direct comparisons with established segmentation methods. Keywords: brain tumor segmentation; Segment Anything Model (SAM); vision-language prompt-driven segmentation; auxiliary clinical prompts; multimodal learning; TCGA-LGG; deep learning

Ionizing radiation can be an effective therapy for prostate cancer. Unfortunately, however, more aggressive prostate cancers such as neuroendocrine prostate cancer (NEPC) are often radiation resistant, which contributes to their high degree of morbidity and mortality. In this study, we used an unbiased approach to identify novel mechanisms that contribute to resistance to radiation and that are associated with neuroendocrine differentiation. Specifically, we compared the expression of cell surface proteins by mass spectrometry in prostate cancer cell lines that had been either untreated or treated with radiation to induce resistance, a process that also promotes neuroendocrine differentiation. Among the proteins identified by this screen, we focused on folate receptor (FR) because of its known biological functions and the fact that it is a validated therapeutic target. Our data reveal that FR has a causal role in enabling prostate cancer cells to resist radiation. Importantly, we also demonstrate that the expression of FR is regulated by HIF-1, which also has a causal role in radiation resistance and neuroendocrine differentiation. Given that the ability of cells to resist damage and death in response to ionizing radiation depends largely on their ability to buffer the substantial increase in reactive oxygen species (ROS) that is generated by radiation, we also demonstrate that the folate-FR axis promotes radiation resistance by sustaining intracellular glutathione levels that buffer this increase in ROS. In summary, the data reported here highlight a novel role for FR in resistance to ionizing radiation that is intimately associated with the hypoxic microenvironment of NEPC and the ability of the folate-FRa axis to maintain redox homeostasis.