Clinical and Translational Radiation Oncology

BACKGROUND AND PURPOSE: ROAM/EORTC-1308 is an international (Europe and Australasia) phase III randomised controlled trial (RCT) comparing radiotherapy (60Gy/30#) to observation after surgery for atypical meningioma. The treatment plans of all the patients randomised to radiotherapy were reviewed and approved prior to treatment delivery, either by the UK RTTQA group (European sites) or the Australian TROG (Australia and New Zealand). Pretrial credentialling included outlining and planning a benchmark case (DR). MATERIALS AND METHODS: Variations from the guidelines were noted in quality assurance (QA) reports both for DR and for the on-trial Individual Case Reviews (ICRs) and classed as major or minor. After recruitment had finished, a random 10% of the ICRs were independently reviewed for audit purposes. We used the QA reports to analyse the variations in the DR and in the ICRs RESULTS: 56 sites (20 UK, 25 EORTC, 11 TROG) undertook the DR of which 13 (23%) had major variations. During the trial 64 patients at 28 sites received radiotherapy, the median being 2 patients per site. Overall, 25 (39%) patient cases needed resubmitting: 22 (36%) sets of outlines (2 cases twice) and 12 (14%) treatment plans (1 plan twice). CONCLUSIONS: For complex radiotherapy of rare tumours, a DR is insufficient and prospective ICRs of all patients is required. The consistent high-quality radiotherapy in ROAM/EORTC1308 ensures the primary outcome (progression free survival) will be a robust assessment and any difference between treatment arms cannot be attributed to variation in radiotherapy treatment.

Background: Radiotherapy is a cornerstone of treatment for pediatric central nervous system (CNS) tumors, but dose selection remains largely uniform despite substantial interpatient variability in tumor radiosensitivity. This limitation is particularly consequential in children, in whom radiation-associated toxicity has lifelong impact. The genomic-adjusted radiation dose (GARD), which integrates tumor genomics with delivered radiation dose, quantifies the biological effect of radiotherapy and has been validated across multiple adult malignancies. Its relevance in pediatric CNS tumors remains unknown. Methods: We performed a retrospective cohort study using gene expression and clinical data from 246 pediatric patients with high-grade glioma, medulloblastoma, or ependymoma from the Childrens Brain Tumor Network. GARD was calculated using a sequencing-adapted radiosensitivity index integrated with radiation dose via the linear-quadratic model. Associations between GARD, physical radiation dose, and clinical outcomes (event-free survival and overall survival) were evaluated using Cox proportional hazards models stratified by tumor type and anatomic location. Patients who did not receive radiotherapy were analyzed as a negative control cohort (sham-GARD). Results: Among patients receiving radiotherapy, physical radiation dose was relatively uniform, yet GARD demonstrated substantial interpatient variability in predicted biological effect. Higher GARD was significantly associated with improved event-free survival (hazard ratio [HR] 0.90, 95% CI 0.83-0.97; p=0.004) and overall survival (HR 0.90, 0.83-0.99; p=0.018). By contrast, physical radiation dose was not associated with either endpoint. In patients who did not receive radiotherapy, sham-GARD was not associated with outcomes, supporting its role as a treatment-specific predictor rather than a general prognostic biomarker. Conclusions: In pediatric CNS tumors, the biological effect of radiotherapy as quantified by GARD is associated with clinical outcomes, whereas physical dose alone is not. These findings challenge the current paradigm of uniform radiotherapy dosing and support a genomically informed approach to dose individualization. Prospective evaluation of GARD-guided radiotherapy is warranted to optimize tumor control while minimizing long-term toxicity in children.

Background. Treatment of locally advanced rectal cancer (LARC) increasingly varies in radiotherapy use, sequence, and intensity. Pretreatment biomarkers for the mismatch-repair-proficient majority remain limited: the biopsy-adapted Immunoscore predicts neoadjuvant response and recurrence risk, but no available biomarker estimates intrinsic tumor radiosensitivity or guides radiotherapy dose, use, or sequence. Genomic Adjusted Radiation Dose (GARD) combines the biopsy-derived Radiosensitivity Index (RSI) with the prescribed dose-fractionation schedule through the linear-quadratic model to estimate tumor-specific modeled radiation effect. We sought to evaluate whether pretreatment GARD is prognostic for outcomes in radiotherapy-treated LARC. Patients and methods. We performed a retrospective pooled analysis of 497 patients with LARC drawn from four prospective clinical trial and institutional cohorts; 335 patients (67%) were prospectively enrolled in clinical trials. The cohort spanned induction chemotherapy followed by chemoradiotherapy (CRT) (n=142), CRT followed by consolidation chemotherapy (n=120), and CRT without sequential chemotherapy (n=235). Pretreatment gene expression was measured by microarray or RNA sequencing and harmonized across platforms before GARD calculation. The primary endpoint was disease-free survival (DFS). GARD was evaluated continuously using cohort-stratified Cox regression and dichotomized at the outcome-blind pooled-cohort median of 19.3. Multivariable models adjusted for age, sex, and clinical stage. Results. Median follow-up was 5.3 years. Among 456 patients evaluable for DFS, 99 experienced an event. Higher GARD was associated with longer DFS as a continuous variable (hazard ratio [HR] per 1-unit increase, 0.92; 95% CI, 0.86-0.99; p=0.027) and at the median threshold (GARD >19.3 versus <19.3: HR, 0.62; 95% CI, 0.41-0.92; p=0.021). Five-year DFS was 81% versus 73%, and 10-year DFS was 80% versus 66%, respectively. GARD remained independently associated with DFS after adjustment for age, sex, and clinical stage (HR, 0.92; p=0.023). Overall survival (OS) was directionally consistent but not statistically significant (HR per 1-unit increase, 0.94; p=0.18). Among 445 patients with evaluable Neoadjuvant Rectal (NAR) scores, higher-GARD patients had lower median NAR scores (8.4 versus 15.0; p=0.004), were more frequently classified as low risk (34% versus 21%), and were less frequently classified as high risk (23% versus 31%). Among 460 patients evaluable for pathologic complete response (pCR), the pCR rate was numerically higher with higher GARD (22% versus 15%; odds ratio per 1-unit increase, 1.07; p=0.09). Conclusions. Pretreatment GARD, a biology-based model of tumor-specific radiation effect, stratified DFS independently of clinical stage and was associated with NAR-defined pathologic response across contemporary treatment sequences. These findings provide multicohort evidence of prognostic validity but do not establish prediction of radiotherapy benefit. Prospective GARD-stratified trials should test whether incorporating tumor radiosensitivity into decisions about radiotherapy use, dose, and sequence improves tumor control and organ preservation while reducing treatment-related morbidity.

Background: Biochemical recurrence (BCR) occurs in up to 40% of men following radical prostatectomy (RP). Current risk models rely primarily on clinicopathologic variables and may not fully capture the biological heterogeneity associated with recurrence. The Decipher Genomic Classifier (DGC), prostate-specific membrane antigen positron emission tomography (PSMA-PET), and multiparametric magnetic resonance imaging (mpMRI) provide complementary prognostic information that may improve prediction. Objective: To develop and evaluate machine learning (ML) models integrating DGC, PSMA-PET, and mpMRI for preoperative prediction of BCR following RP. Methods: This retrospective study included patients with available preoperative DGC, PSMA-PET, mpMRI, and clinicopathologic data. Logistic regression (LR), random forest (RF), and XGBoost models were developed using single- and multimodality feature combinations. Early- and intermediate-fusion strategies were evaluated. Performance was assessed using an area under the receiver operating characteristic curve (AUC) and accuracy. Clinical utility was evaluated using decision curve analysis. Results: XGBoost consistently outperformed LR and RF. DGC achieved the highest single-modality performance (AUC 0.94, accuracy 86.7%). Among multimodal models, DGC combined with PSMA-PET using intermediate fusion achieved the best overall performance (AUC 0.93, accuracy 87.0%). Addition of mpMRI reduced performance (AUC 0.85, accuracy 83.0%). Decision curve analysis demonstrated positive net benefit across clinically relevant thresholds. Conclusion: XGBoost-based multimodal fusion improved preoperative BCR prediction following RP. DGC was the strongest individual predictor, while integration with PSMA-PET provided the best overall performance, supporting the potential of radiogenomic ML models for personalized risk stratification.

Abstract Purpose: To compare scalar circulating blood dose summaries, assess whether associations with grade 3 or higher lymphopenia and overall survival persisted after planning target volume (PTV) adjustment, and distinguish scalar from exploratory dynamic blood dose analyses. Methods and Materials: The assembled dataset included 133 patients from 2 retrospective thoracic radiation cohorts; 121 entered the dosimetric main analysis and 116 passed a post hoc dosimetric gate. Six scalar exposures were compared: an ICE3 (Immune Circulation radiation Exposure Estimator Engine) 10 compartment mean-dose metric, corrected effective dose to immune cells (EDIC), body remainder dose, a hematological dose (HEDOS) derived organ-mean approximation, mean lung dose, and mean heart dose. Logistic base models included baseline absolute lymphocyte count, concurrent chemotherapy, and cohort; PTV was then added. Overall survival used cohort-stratified Cox models on an endpoint-specific common set. Benjamini Hochberg correction was applied within prespecified 6exposure families and, separately, across 3 selected post hoc bootstrap contrasts. Results: The lymphopenia analysis included 94 patients and 69 events. After PTV adjustment, the HEDOS derived approximation remained nominally associated (odds ratio, 2.54; 95% confidence interval, 1.18-5.45; P=.017; q=.102), but no exposure survived false-discovery-rate control. The survival set contained 92 patients and 42 deaths; no PTV adjusted scalar exposure was associated with survival (all q>=.531). In post hoc analyses, the standardized association of mean heart dose with overall survival was more positive than that of ICE3 (difference in log hazard ratios, 0.44; 95% CI, 0.13 0.94; multiplicity-adjusted q=.024). The corresponding contrast with the HEDOS derived approximation did not meet the adjusted significance threshold (q=.053). These coefficient contrasts do not establish superior predictive performance or causality. Exported ICE3 kinetic summaries had no false-discovery-rate-significant residual associations. A 13 case HEDOS bDVH audit showed little change under one continuous versus 10 second gap perturbation. Conclusions: PTV adjustment attenuated scalar blood-dose associations with severe lymphopenia, and no scalar exposure retained a multiplicity-robust survival association. The selected mean heart dose coefficient contrast is hypothesis generating and does not establish superior prediction. The primary cohort comparison evaluated a HEDOS derived organ-mean approximation rather than the full dynamic HEDOS framework; therefore, these findings should not be interpreted as evidence against the potential value of particle level blood dose distributions or time-dependent blood-flow modeling.

Purpose/Objective There is paucity of data reporting outcomes of breast cancers with initial internal mammary nodal involvement and no visceral metastases, treated with curative hypofractionated radiotherapy . We report the outcomes from a tertiary centre alongside spatial patterns of recurrences in the above group Material/Methods For this retrospective cross-sectional study, consecutive patients contoured as per the ESTRO 2013 guidelines, treated between 2016-2022 were eligible if their diagnostic imaging demonstrated involvement of the internal mammary nodes. Radiotherapy (40 Gy/15#/3 weeks) was delivered to the residual breast / thoracic wall, SCF region corresponding to the ESTRO lymph node level 4 and internal mammary chain nodes. Residual IMN/ level 4 nodes received a boost of 10Gy/5#. Spatial mapping of sites of recurrence at the local site and three nodal sites (axilla, SCF and IMN) was performed using deformable image registration. Sites of recurrence at the local site and three nodal levels were contoured separately. Volumetric intersection of the recurrent gross tumour volume (GTV_recurrence) with treated clinical target volume (CTV) was calculated. Actuarial overall (OS), disease free survival (DFS) & cumulative incidence of local (LR), regional (RR) and loco-regional recurrence(LRR) were calculated using Kaplan Meier method. Univariate comparison of outcomes with or without residual disease was performed using the log rank test. Results The median age of the 61 eligible women was 49 years. 77% received neoadjuvant chemotherapy and the rest adjuvant chemotherapy. 82% patients had a mastectomy. Axillary lymph node dissection was done in 96.7%. Boosts to residual IMN and SCF nodes were delivered to 21(34.4%) and 2 (3.3%) respectively. Median follow up was 3.6 years. Out of the 61 patients, 42 patients were disease free with an estimated 3 year disease free survival of 75% (95% CI 64, 88%). Spatial mapping of locoregional recurrence was possible in all but 1 patient with local (only) recurrence who was lost to follow-up after mammogram only. Among the patients with loco regional recurrence 1 had recurrence in local site + SCF +axilla, 3 had recurrence in the SCF+axilla, 2 in the SCF+IMN and 1 in the axilla+SCF+IMN. Only one patient had isolated axillary recurrence or isolated SCF recurrence. There were no IMN only recurrences. Among the 8 patients with nodal recurrence, a total of 27 individual GTV_recurrence were identified in the axilla(n=11), SCF(n=11) and IMN (n=5). IMN recurrences showed complete or partial overlap with CTV. SCF recurrences were a mix with predominantly in-field recurrences while axillary recurrences occurred outside the treated volume.Four (6.6%) patients had Grade 2 lymphoedema as documented late side effect. Conclusion Aggressive treatment of IMN disease with adjuvant radiation is effective with good locoregional control. Systemic recurrences are common and may benefit from intensification strategies.

Background. Accurate delineation of the gross tumour volume (GTV) - primary tumour (GTVp) and nodal disease (GTVn) - on FDG-PET/CT is a critical step of head and neck radiotherapy planning. Comparisons between lightweight custom networks and the auto-configured nnU-Net v2 are usually reported as end-to-end pipelines, conflating the contribution of the network with that of the inference-time post-processing applied on top of it. We separated the two. Methods. MiniUNet3D (custom 3D U-Net, 18.3 M parameters) and nnU-Net v2 (3d_fullres, 88.2 M parameters) were trained on the same 578 FDG-PET/CT cases (85/15 author-defined split of the HECKTOR 2025 Task 1 set, 8 centres) and evaluated on the same internal cohort. Three arms were compared pairwise: MiniUNet3D raw output at a fixed 0.5 threshold, MiniUNet3D with a locked adaptive post-processing pipeline, and nnU-Net v2. Comparisons used paired Wilcoxon tests with bootstrap confidence intervals, Bonferroni and Benjamini-Hochberg correction, and Cohen's d; catastrophic failure (Dice < 0.01) was compared with an exact McNemar test. Cases with an empty reference for a given target were excluded from that target's analysis (n = 98 GTVp, n = 93 GTVn). Results. With post-processing matched off, nnU-Net v2 was superior: median GTVp Dice 0.799 versus 0.592 (mean difference -0.244, 95 % CI -0.300 to -0.191; d = -0.88) and GTVn 0.774 versus 0.598 (d = -0.82). Post-processing raised MiniUNet3D to 0.800 (GTVp) and 0.738 (GTVn), recovering 79 % of that difference. Post-processed, MiniUNet3D matched nnU-Net v2 on GTVp Dice (p = 0.113) but remained inferior on nodal disease after Bonferroni correction (Dice p = 0.041; surface Dice p = 0.049). Catastrophic GTVp failures were 25/98 raw, 8/98 post-processed and 1/98 for nnU-Net v2 (McNemar p = 0.016). Inference took 34 s versus 78 s per case on the same GPU. Conclusions. Post-processing recovered most, but not all, of the difference between the two models, and it did not confer robustness: an eight-fold higher rate of empty contours on small primaries persisted, which is the more consequential difference for planning safety. Pipeline comparisons reported without a post-processing ablation risk attributing to a network what post-processing supplied.

Purpose: Clinical assessment of vertebral lesion quality (osteolytic, osteoblastic, mixed) remains subjective, with limited interobserver reliability. This study evaluated a novel application of 3D convolutional neural networks (3D-CNNs) for classifying lesion quality from CT volumes in metastatic cancer patients. Materials and Methods: This retrospective study used CT data from 151 cancer patients planned for radiotherapy for metastatic spine disease (September 2020-July 2024). Leveraging vertebra-level expert annotations, we introduced an unconventional U-Net-based strategy converting coarse voxel-wise predictions into vertebra-level lesion classifications. The final dataset comprised 2,125 vertebrae across four classes (no lesion, osteolytic, osteoblastic, mixed), split into a 3-fold cross-validation set and an independent holdout test set. Model performance was benchmarked against a DenseNet121 baseline and a musculoskeletal radiologist, with Cohen's kappa assessing inter-rater agreement. Results: The 3D model achieved an ensemble accuracy of 84.7%, outperforming DenseNet121 (72.1%), with substantial gains in F1 score, precision, and balanced accuracy. It showed high concordance with the radiologist (Cohen's kappa = 0.76) and comparable sensitivity and specificity across all lesion subtypes. We found both models and the radiologist to struggle with osteolytic lesions, reflecting the difficulty of distinguishing this class from age-related changes in vertebral bone density and architecture caused by benign bone lesions, age-related systemic skeletal disorders and cancer treatments. Conclusions: 3D-CNNs trained with vertebra-level labels can accurately and reliably classify vertebral metastatic lesion quality from CT scans, offering a scalable path toward automated characterization of metastatic spine disease to support clinical decision-making and large-scale radiomics research.

BackgroundRadiotherapy can provide durable local control for optic pathway-hypothalamic glioma (OPHG), but its use is limited by concern regarding delayed vascular, endocrine, visual, oncological, and neurological toxicities. ObjectiveTo systematically characterize and quantify the safety of radiotherapy and radiosurgery for OPHG and explore clinically relevant modifiers of treatment-related toxicity. MethodsPubMed, Scopus, Web of Science, Embase, Cochrane, Google Scholar, and ClinicalTrials.gov were searched from inception through 1 June 2026. Eligible non-randomized studies reporting safety outcomes after radiotherapy or radiosurgery were included. Random-effects binomial-normal generalized linear mixed-effects models were used to pool proportions, with exact conditional models for sparse comparative analyses. ResultsThirty-five studies were included, of which 31 contributed event-level data to at least one quantitative safety outcome. The pooled incidence of any treatment-related toxicity was 8.46% (95% CI, 1.37-38.01%). Vasculopathy occurred in 9.44% (95% CI, 5.22-16.49%). Secondary neoplasms occurred in 5.41% (95% CI, 2.23-12.53%), decreasing to 2.83% under a strict malignant-event definition. Incident endocrinopathy had the highest pooled estimate at 21.19% (95% CI, 4.72-59.31%) and increased with longer follow-up. Treatment-related visual toxicity was 2.26%, whereas radiation-related mortality was 0.59%. Radiation necrosis, severe toxicity, and treatment-attributed neurocognitive toxicity were sparsely reported. ConclusionLate toxicity following radiotherapy for OPHG is heterogeneous, with endocrinopathy, vasculopathy, and secondary neoplasms representing the principal quantifiable safety concerns. Treatment decisions should therefore be individualized, with prolonged vascular, endocrine, visual, and oncological surveillance and further prospective evaluation of contemporary radiation techniques.

Background and purpose: Radiation-induced lymphopenia is associated with adverse outcomes in thoracic malignancies. FLASH radiotherapy delivers radiation over a timescale of hundreds of milliseconds, potentially reducing the fraction of irradiated circulating lymphocytes. In this study, we investigated whether FLASH mitigates lymphopenia after thoracic irradiation delivered with protons or photons. Materials and methods: C57BL/6 mice received three 13.5-Gy whole-heart fractions at 48-hour intervals using FLASH or standard dose-rate proton irradiation at the University of Pennsylvania (n=15), with photon validation at Queen's University Belfast (n=72). Leukocytes and CD4 T cells, CD8 T cells, B cells, and NK cells were quantified by hemocytometer and flow cytometry. A continuous-time Markov model simulated lymphocyte trafficking, dose accumulation, and post-irradiation recovery. Results: FLASH attenuated leukocyte depletion across both proton and photon irradiation modalities. In the proton cohort, white blood cell counts were significantly higher after FLASH at D1, D3, D7, and D14; CD4 T cells and NK cells were preserved through D14, while CD8 T cell sparing persisted through D21. Photon FLASH preserved CD45 leukocytes at D1, D3, D7, and D21, with sustained CD8 sparing at D21. Modeling showed that FLASH shifted the lymphocyte dose distribution toward lower exposures, increasing the proportion of lymphocytes receiving <1 Gy from 2.4% to 16.4%, and reduced the proportion of lymphocytes repeatedly irradiated across all three fractions from 36.3% at standard dose rate to 9.18%, despite similar median cumulative doses. The spleen contributed substantially to cumulative lymphocyte dose, and marrow-entering lymphocytes displayed a more high-dose-enriched distribution after FLASH irradiation. Conclusion: FLASH consistently mitigated radiation-induced lymphopenia for proton and photon modalities, with durable CD8 T cell preservation. These findings support a kinetic mechanism and provide a rationale for combining FLASH radiotherapy with immune-sparing planning and immunotherapy.

Introduction Anatomical endoscopic enucleation of the prostate (AEEP) techniques, including bipolar enucleation (B-TUEP), holmium laser enucleation (HoLEP), thulium laser enucleation (ThuLEP), and thulium fibre laser enucleation (ThuFLEP), demonstrate comparable clinical outcomes for benign prostatic hyperplasia. As clinical equivalence is increasingly established, cost becomes a key determinant of modality selection. We performed a cost minimisation analysis comparing index procedural costs across AEEP modalities from an institutional perspective. Methods A cost minimisation model was developed from the institutional perspective, incorporating amortised capital costs, maintenance, and consumables. In addition to the base-case scenario of 180 cases per year, we modelled two additional case volume scenarios: low (50 cases/year) and high (500 cases/year) volume. Thu:YAG laser fibres were modelled on two scenarios: disposable single-use, and reusable fibres (up to 10 cases per fibre). Breakeven analysis determined the threshold volume at which each laser modality achieves cost parity with B-TUEP, and one-way sensitivity analysis was performed on key cost parameters. Analysis was limited to index procedural costs calculated in Singapore dollars. Results At the base case of 180 cases per year, B-TUEP had the lowest index procedure cost (SGD 1,018), followed by ThuFLEP (SGD 1,584), ThuLEP (1,599), and HoLEP (SGD 1,655). Breakeven analysis demonstrated that HoLEP, ThuLEP, and ThuFLEP can never achieve cost parity with B-TUEP when laser fibres are single-use, as laser modalities carry higher costs on both capital and per-case dimensions. ThuLEP with reusable fibres (10 uses per fibre) was the only modality to cross below B-TUEP, at a breakeven volume of 198 cases per year. At 500 cases per year with reusable fibres, ThuLEP achieved the lowest cost (SGD 847), representing a 15.4% saving over B-TUEP. Sensitivity analysis identified annual case volume and B-TUEP loop cost as the most influential parameters. Conclusion Index procedural costs in AEEP are strongly influenced by case volume and consumable strategy. While B-TUEP remains cost-efficient at low volume, high-volume practice combined with reusable Thu:YAG fibre technology enables cost parity and potential cost advantage for laser enucleation. These findings highlight the importance of economies of scale and device utilisation in technology adoption.

Background. Roughly, half of new type 1 diabetes (T1D) diagnoses occur in individuals under 18 years old and represent a more aggressive destruction of beta cell mass (BCM). [11C]-(+)-PHNO positron emission tomography (PET) imaging is used to assess BCM, but current pancreas PET imaging protocols are limited to adults. Previously published full count data from six healthy controls and five T1Ds (6M/5F; 22 to 53 years old) were used for retrospective analysis. Dynamic [11C]-(+)-PHNO PET/CT scans were acquired and reconstructed using full-count list-mode data. For the current comparison to full count data, 50%, 25% and 10% down-sampled count data were re-reconstructed. Pancreas and spleen (reference region) time-activity-curves (TACs) were assessed, and volume of distribution (VT, mL/cm3) was estimated using the reversible 1-tissue compartment model (1TC) with tmax of 30 min for all count levels. Pancreas and Spleen VT estimates (1TC; tmax= 30 min) were used to calculate non-displaceable binding potential (BPND) and were then correlated to semi-quantitative methods of standardized uptake value ratio (SUVR-1) (20-30 min; ref: spleen) to examine simplified methods using simulated low dose protocols. Finally, we performed dosimetry in adult, adolescent and pediatric phantoms to assess radiation dose for simulated low-dose protocols. Results. Qualitatively, increasing noise can be visualized at successive reduced-count levels images, compared to full-count images. Despite progressively increasing noise in reduced-count images, TACs at each reduced-count level remained similar to full-count TACs in both HC and individuals with T1D. Quantitatively, 1TC VT estimates were similar for all reduced count levels and range of tmax values, compared to full-count (all R2[&ge;]0.99). Pancreas SUVR-1 (20-30 min) and pancreas BPND (tmax = 30; ref: spleen) were highly correlated for all count levels (all R2[&ge;]0.80). All age groups were under both the yearly occupational and research scan radiation dose limits when examining mean effective dose equivalent with reduced (1/10th) injected dose protocols. Conclusion. Low-count reconstructed data and simplified reference region approaches provide accurate quantification compared to full-count reconstructions. These results provide evidence that it is possible to perform accurate quantification using simulated low dose protocols to quantify BCM for use in individuals with T1D under 18 years old.

Background: A 2019 report from our institution described a multilayer artificial neural network (ANN) for predicting prostate cancer at biopsy in 334 patients, trained with TensorFlow 1.x and evaluated at three fixed step counts without separating hyperparameter selection from test evaluation. We re-analyzed an expanded cohort from the same institution using contemporary machine-learning practice. Methods: We pooled all available biopsy episodes from the same institutional database (n = 526; 524 after excluding one non-binary outcome code and one record with missing digital rectal examination [DRE] data), retaining the same seven predictors used in the original report (age, prior biopsy history, PSA, prostate volume, DRE, and MRI diffusion-weighted imaging findings in the peripheral and transition zones). Because 27 patients contributed more than one biopsy episode, we used patient-ID-grouped, stratified k-fold cross-validation (StratifiedGroupKFold; scikit-learn 1.8.0) with 3 and 5 folds, repeated over 10 random partitions, to avoid leakage between folds. Four classifiers were compared: L2-regularized logistic regression, gradient boosting, random forest, and a shallow (single hidden layer) multilayer perceptron. Two outcomes were modeled: detection of any prostate cancer, and detection of clinically significant prostate cancer (Gleason score [&ge;] 7). Results: Any-cancer prevalence was 55.7% (292/524) and Gleason score [&ge;] 7 prevalence was 39.7% (208/524). With repeated 5-fold cross-validation, gradient boosting gave the highest discrimination for any prostate cancer (mean AUC 0.826, 95% CI 0.823-0.830) and for Gleason score [&ge;] 7 (mean AUC 0.855, 95% CI 0.852-0.859), closely followed by random forest and logistic regression (AUC 0.81-0.85). The shallow multilayer perceptron performed worse and less consistently than the other three models (any-cancer AUC 0.671; Gleason score [&ge;] 7 AUC 0.742) and than the deeper five-hidden-layer ANN reported in 2019. Results with 3-fold cross-validation were essentially unchanged. Conclusions: In an expanded cohort, regularized logistic regression, gradient boosting, and random forest all discriminated prostate cancer at biopsy at least as well as the previously reported multilayer ANN, using far simpler models and a methodology that separates hyperparameter tuning from performance estimation. A shallow neural network offered no advantage over these simpler alternatives in this sample size. This is a preprint; the study has not undergone external peer review.

Background: Primary malignant cardiac tumors (PMCTs) are rare and histologically heterogeneous. Objective: To compare demographics, specific ICD-O-3 morphologies, first-course treatment patterns, annual registered case counts, and unadjusted overall survival between soft-tissue and hematologic PMCTs. Methods: We identified 730 PMCT cases diagnosed from 2000 to 2021 in SEER 18 (ICD-O-3 topography C38.0). Histologic lineage was assigned from ICD-O-3 morphology. Comparative analyses included soft-tissue (n=458) and hematologic (n=212) tumors. First-course variables were primary-site surgery, chemotherapy (yes versus no/unknown), and radiotherapy (radiation versus none/unknown). Groups were compared with chi-square tests. Overall survival was estimated with Kaplan-Meier methods; follow-up was truncated at 120 months. Results: Soft-tissue PMCTs occurred predominantly at ages 45-64 years (67.9%), whereas hematologic PMCTs occurred predominantly at age [&ge;]65 years (63.2%; p<0.001). Men comprised 59.9% of hematologic and 49.3% of soft-tissue cases (p=0.014). The leading soft-tissue morphology was hemangiosarcoma/angiosarcoma (ICD-O-3 9120/3; 201/458, 43.9%); synovial sarcoma accounted for 20/458 cases (4.4%). Diffuse large B-cell lymphoma, NOS, accounted for 131/212 hematologic tumors (61.8%). Any primary-site surgery was recorded in 66.6% of soft-tissue versus 15.6% of hematologic cases (p<0.001). Chemotherapy was recorded in 67.5% versus 51.1% (p<0.001), and radiotherapy in 9.0% versus 20.5% (p<0.001). In exploratory Kaplan-Meier analyses, hematologic patients with recorded chemotherapy had higher unadjusted 120-month overall survival than those without recorded chemotherapy (42.0% versus 12.2%; log-rank p=7.5x10-). Radiation-associated survival differences were not statistically significant in either lineage. Conclusions: Soft-tissue and hematologic PMCTs have distinct age distributions, named histologies, and first-course treatment patterns in SEER. These findings describe registry coding and do not establish treatment effectiveness or population incidence.

Automated lesion segmentation is increasingly central to PSMA PET/CT interpretation, supporting staging, treatment planning, and response assessment at a scale that outpaces available nuclear-medicine expertise. However, automated PSMA-PET/CT whole-body lesion segmentation models are trained on images alone, with no knowledge of where in the body prostate metastases actually tend to occur. Radiologists use clinical domain knowledge of metastatic spread, but its absence in machine learning models produces false positives in anatomically implausible locations and missed lesions in high-risk sites such as the liver. In this work, we explore whether population-level spatial knowledge of metastatic spread can be used to augment deep learning segmentation predictions, and how such a prior should be fused with a network's output, without additional training. We build a data-driven metastasis atlas from 375 expert-annotated whole-body PSMA PET/CT scans and investigate its fusion with a trained segmentation network under a Bayesian framework, in which prediction probabilities from an nnU-Net-based lesion segmentation model serve as the likelihood and the data-driven atlas as the prior. Because metastases occupy only a small fraction of whole-body voxels, the atlas's peak probability is too low, and standard power-scaled or naive Bayesian pooling references lack the tools to deal with this shortcoming. This causes these standard fusion strategies to fail and, in the naive Bayesian case, to sharply degrade performance. We instead derive a calibrated, background-referenced log-odds fusion, one of many possible approaches to combine a population atlas with a deep learning model's predictions, distinct from classical multi-atlas label fusion in that it fuses a single population prior with a trained network's softmax rather than combining several registered atlases. Furthermore, this approach is neutral outside atlas support by construction, reduces exactly to the baseline network when unweighted, and requires no retraining. This atlas fusion significantly improved mean Dice over the baseline nnU-Net on a disjoint internal test set ($+0.011$, Holm-adjusted $p=0.021$) and on an independent external cohort ($+0.0129$, Holm-adjusted $p=3.8\times10^{-16}$), with lesion sensitivity improving from 0.849 to 0.861 internally and Dice improving over baseline in every stratified anatomic region, including the rare, high-risk sites motivating this work, while naive Bayesian pooling degrades performance sharply and power-scaled pooling underperforms it throughout. Our findings suggest that population-level spatial priors can meaningfully augment deep learning predictions in whole-body oncologic segmentation, provided the fusion rule is calibrated to where the prior actually carries signal.

Background. Extracellular volume fraction (ECV) derived from contrast-enhanced CT is a validated marker of hepatic fibrosis and has been reported to differ between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma. In published work it is obtained from a small number of hand-placed two-dimensional regions of interest, and the software that computes it is either tied to one manufacturer's workstation or based on spectral or dual-energy acquisition. We are not aware of an accessible tool that produces voxelwise liver ECV maps from conventional single-energy multiphase CT. Methods. We developed CT ECV Mapper, a scripted 3D Slicer extension with a three-layer architecture whose numerical core imports neither slicer nor vtk and is unit-tested outside 3D Slicer. The interactive application provides two-stage registration that the operator inspects and accepts before any ECV is computed, operator-placed three-dimensional regions of interest, user-adjustable calculation parameters, a voxelwise ECV color map and ROI statistics; the same logic layer can be driven unattended across a cohort. The tool was applied to the 164 patients of the public WAW-TACE multiphase HCC/TACE dataset that have both unenhanced and delayed-phase series. Results. 156 of 164 cases (95.1%) completed unattended. Whole-liver ECV had a median of 36.2% (interquartile range 31.9-41.5), consistent with published CT-ECV values for fibrotic and cirrhotic liver. Registering the arterial and portal phases on demand extended tumor ECV from the 38 lesions a conventional two-phase pipeline can reach to 248 lesions in 156 patients. Every failure was attributable to an identifiable mechanism: craniocaudal field-of-view mismatch between phases in six cases, aortic calcification within the blood-pool region in one, and in one case a labeling error in the source dataset, in which the series declared as unenhanced proved to be a second reconstruction of the portal venous phase; this was detected by the blood-pool validity check rather than by visual review. Conclusions. Voxelwise CT ECV mapping of the liver and of hepatic tumors is feasible from conventional multiphase CT on an open platform, both interactively and as an unattended batch, with quality-control instrumentation that fails explicitly and diagnosably. This is a technical development and feasibility report; the application has not been evaluated against a reference standard and no claim of clinical validity is made.

Purpose: Respiratory motion remains a major source of quantitative bias in PET and becomes increasingly relevant for high-sensitivity long axial field-of-view (LAFOV) PET/CT. Although numerous respiratory motion correction (MoCo) methods have been proposed, their quantitative accuracy cannot be established clinically because a patient-specific motion-free reference is fundamentally unavailable in vivo. This study combined clinical PET imaging with a digital twin, a realistic representation of both the PET/CT system and the patient, to objectively validate respiratory MoCo against a corresponding motion-free reference. Methods: Twenty patients (10 [18F]FDG with predominantly pulmonary lesions and 10 [18F]SiFAlin-TATE with predominantly hepatic lesions; total 135 lesions) were analyzed. The digital twin combined a validated LAFOV PET/CT simulation model with an anatomically realistic phantom containing 14 lung and liver lesions, two patient-derived respiratory patterns, and respiratory motion amplitudes of 2 and 3 cm, generating patient-like datasets with corresponding motion-free references. Data-driven and image-based MoCo were evaluated using lesion morphology, SUVmean, SUVmax, and metabolic tumor volume (MTV). Results: In patients, data-driven MoCo produced larger SUVmean increases than image-based MoCo for liver (48.1{+/-}18.9% vs. 17.0 {+/-} 12.0%; p<0.01), lower-lung (32.5{+/-}21.2% vs. 16.3{+/-}15.6%, p=0.06), and upper-lung lesions (28.4{+/-}32.0% vs. 10.4 {+/-} 17.2%; p<0.01), with similar findings for SUVmax and larger MTV reductions. Simulation revealed marked motion-induced SUVmean underestimation before correction, particularly in liver (-31.2{+/-}6.8%) and lower lung (-15.5{+/-}13.9%). Relative to the motion-free reference, data-driven MoCo most accurately recovered hepatic uptake (4.3{+/-}11.7% vs. -10.0 {+/-} 9.2%; p=0.01) but overestimated pulmonary uptake (lower lung: 19.8{+/-}16.3% vs. -1.6 {+/-} 10.2%; p=0.02). SUVmax showed the same regional behavior, whereas image-based MoCo yielded MTV estimates closer to the reference. Quantitative recovery was largely independent of respiratory pattern, while larger motion amplitudes mainly affected image-based MoCo. Conclusion: Combining clinical PET with a realistic digital twin and corresponding motion-free ground truth enabled objective validation of respiratory MoCo beyond conventional clinical evaluation. Larger correction-induced quantitative changes should not be equated with greater quantitative accuracy. Instead, MoCo performance was region- and metric-dependent, highlighting the value of ground-truth-based validation for developing and benchmarking respiratory motion correction and quantitative PET on LAFOV PET/CT systems.

Radiation oncology has a long history of developing in-house health information technology (HIT) tools such as quality assurance (QA) checklists, yet there is little guidance from professional bodies on how to implement these tools in complex clinical environments. Building on our previous work that used human-centered participatory co-design, the Task-User-Representation-Function (TURF) framework, and multi-method usability evaluations to design and develop an enhanced dosimetry QA checklist (DQC), this study investigated the barriers and facilitators (determinants) to implementing the enhanced DQC in a radiation oncology clinic, examined implementation strategies, proposed an implementation framework for QA checklists in radiation oncology, and assessed four implementation outcomes: acceptability, appropriateness, feasibility, and adoption. We conducted a qualitative implementation study using an abductive research approach at an academic medical center. All key stakeholders (dosimetrists, physicists, trainees, and software developers) participated in semi-structured interviews, field observations, and surveys across pre-implementation, implementation, and post-implementation phases. Data were analyzed using a hybrid inductive-deductive approach, with deductive coding guided by an adapted Consolidated Framework for Implementation Research (CFIR) mapped to the Unified Theory of Acceptance and Use of Technology and by the Expert Recommendations for Implementing Change (ERIC) compilation. We identified 4 CFIR constructs and 12 sub-constructs as barriers, with structural characteristics and planning showing the highest negative valence, and 5 CFIR constructs and 19 sub-constructs as facilitators, with relative advantage, culture, and leadership engagement showing the highest positive valence. Participants' suggestions mapped to 19 ERIC strategies in 7 clusters, and the CFIR-ERIC matching tool identified 14 evidence-based strategies in 4 clusters that informed a proposed phased implementation framework. Acceptability, appropriateness, and feasibility scores improved significantly from pre-implementation to implementation for all professional roles (p<0.05), yet adoption reached 100% only in the sixth week of implementation. These findings highlight the value of combining subjective and objective implementation outcomes and provide a practical, evidence-based framework for implementing in-house QA checklists in radiation oncology that warrants validation in diverse settings.

Herein, we assess the potential of low-dose stereotactic radiosurgery (SRS) to modulate neuronal network states without apparent damage to cellular integrity. Using a small animal radiation research platform (SARRP), a 1 mm3 focal target in the mouse visual cortex was irradiated with doses of 5, 20, and 40 Gy. One-month later a significant dose-dependent increase in excitatory synapse numbers was observed, notably limited to the treated visual cortex and not the adjacent somatosensory cortex. Six months post-irradiation, cortical neuronal microcircuit activity was monitored in awake mice using high sensitivity two-photon calcium imaging. A single 5 Gy dose resulted in a significant microcircuit-wide increase of spontaneous neuronal activity, consistent with a lasting shift in the functional architecture of the irradiated nodal network. At higher SRS doses (40 Gy) this neuromodulatory window appears to close. In aggregate, these data suggest that low-dose radiation could, in some circumstances, be exploited by selected high precision SRS technologies to durably modulate neuronal circuit disorders. Some, or even all the clinical benefits reported in the companion article by Zhao et al. are likely attributable to the biological properties we sought to characterize in our research. One Sentence SummaryLow-dose stereotactic radiosurgery effectively and durably modulates neuronal excitability via synaptic re-organization and could open new clinical possibilities for neuromodulation.

PurposeHigh dietary fibre intake has been linked to lower cancer risk, yet its role in prostate cancer treatment responses and radiotherapy tolerance remains unclear. We evaluated the effects of dietary fibres (inulin, pectin, {beta}-glucan) on prostate tumour growth, gut microbiota and intestinal response to ionising radiation (IR) in murine models. MethodsMale FVB and C57BL/6J mice were injected with murine Myc-CaP (FVB), RM-1 or DVL3 (C57BL/6J) prostate tumour cells and fed a low-fibre (0.2% cellulose) or high-fibre diet (10% inulin, pectin or {beta}-glucan). Some mice had tumour irradiation (6 Gy). Tumour volume, caecal weight and faecal microbiota relative abundance (by 16S rRNA gene sequencing) were analysed. Caecal contents fermentation acids were quantified by gas chromatography. The effects of dietary fibre on intestinal acute normal tissue toxicity post-irradiation (10-14 Gy) were assessed by intestinal crypt assay. ResultsInulin delayed average tumour growth in all models. Inulin and {beta}-glucan prolonged post-IR tumour control versus 0.2% cellulose (all p <0.05), in some but not all mice. Inulin, pectin and {beta}-glucan increased faecal acetate concentrations post-IR and mice demonstrated responder (R) vs non-responder (NR) phenotypes to diet/IR, associated with Bifidobacterium (inulin-R), Lactobacillus and Parasutterella (pectin-R) and Muribaculacaeae and Muribaculum ({beta}-glucan-R). High fibre-fed mice had enhanced intestinal crypt regeneration following 12 Gy compared to 0.2% cellulose-fed mice. ConclusionsHigh fibre diets slowed prostate tumour growth both alone and following 6 Gy IR, while protecting small intestines from radiation-induced injury. Effects may have been mediated via increased microbiota-driven metabolite production and enhanced epithelial regeneration, but more mechanistic work is required to explore causality. The differences in individual responses to various fibres should be investigated further, as this may have relevance to adopting dietary fibre supplementation strategies in human radiotherapy patients, and may reflect the recognised importance of an individuals baseline microbiota on dietary effects.