International Journal of Radiation Oncology*Biology*Physics

Background and purposeStereotactic body radiotherapy (SBRT) has become a standard treatment option for localized prostate cancer, with low rates of clinically relevant late toxicity. However, the identification of robust dosimetric predictors of toxicity remains challenging due to the high dimensionality and collinearity of dose-volume histogram (DVH) metrics. This study aimed to explore whether principal component analysis (PCA) of DVHs can identify dose regions associated with late gastrointestinal and genitourinary toxicity after prostate SBRT. Materials and methodsWe analysed a single-institution cohort of patients treated with prostate SBRT. Rectum, rectal wall, bladder and bladder wall DVHs were extracted with a dose bin resolution of 0.5 Gy. PCA was applied separately to each structure to identify dominant patterns of dose-volume variability. PCA-derived dose metrics were subsequently evaluated using Spearman correlation analyses, receiver operating characteristic (ROC) curves, and exploratory logistic regression models. Late toxicity was scored according to CTCAE version 5.0, with grade [≥] 2 events at 12 months as the primary endpoint. ResultsPCA demonstrated that a limited number of components accounted for most DVH variability, with the largest contributions arising from intermediate-dose regions. For the whole rectum, intermediate-dose metrics showed the strongest association with late rectal toxicity. Rectal V18.1 Gy yielded the highest discriminative performance (AUC = 0.87), followed by V29 Gy (AUC = 0.83), whereas low-dose (V1.5 Gy) and high-dose (V42.5 Gy) metrics showed limited or no discrimination. Rectal wall metrics demonstrated weaker and less robust associations, and no clinically meaningful discriminative performance was observed for bladder or bladder wall DVH metrics. Exploratory regression analyses supported the association between intermediate rectal dose exposure and late rectal toxicity. ConclusionIn prostate SBRT, PCA of DVHs highlights intermediate rectal dose exposure as the primary dosimetric determinant of late rectal toxicity. Whole-rectum intermediate-dose metrics outperform both low- and high-dose parameters, as well as rectal wall and bladder-derived metrics. These findings support a parsimonious, data-driven focus on intermediate-dose rectal volumes for toxicity risk assessment and hypothesis generation in prostate SBRT planning.

PurposeThis study evaluated the safety and effectiveness of an intraoral light-emitting diode (LED)-based photobiomodulation (PBM) device to reduce the incidence and severity of oral mucositis (OM) from intensity modulated radiation therapy (IMRT) for head and neck cancer (HNC). MethodsThis randomized, double-blind, sham-controlled trial enrolled patients with HNC undergoing high-dose IMRT over 6-8 weeks, with or without concurrent chemotherapy. Participants received daily 10-minute PBM or sham treatments immediately before IMRT sessions. Assessments were conducted at baseline, daily and weekly during IMRT, and two weeks post-IMRT. ResultsEighty-five participants (42 PBM; 43 sham) were enrolled across 12 US sites. No device-related adverse events were observed, and 99.5% of initiated sessions were completed. In the intent-to-treat population, severe OM (WHO Grade [≥]3) incidence was significantly lower with PBM across six weeks of IMRT (36.8% vs 57.1%; p = 0.046) and at two weeks post-treatment (10.8% vs 36.4%; p = 0.042). In the per-protocol population, the PBM arm reported significantly greater taste preservation (p = 0.034), lower increases in mouth/throat soreness (p = 0.029) and throat pain (p = 0.028) and needed fewer feeding tube placements (p = 0.073) than the control arm. ConclusionDaily intraoral PBM therapy using an LED-based device was safe, well tolerated, and significantly reduced the incidence of severe OM and associated complications in HNC patients undergoing IMRT with or without concurrent chemotherapy. These findings align with guidelines recommending daily intraoral PBM therapy for preventing cancer therapy-related OM, a dose-limiting toxicity for which effective preventive interventions are needed. Trial RegistrationClinicalTrials.gov Registration Number NCT03972527. Registered on June 3, 2019. Concise SummaryDaily intraoral PBM therapy using an LED-based device was safe, well tolerated, and significantly reduced the incidence of severe OM and associated complications in HNC patients undergoing IMRT with or without concurrent chemotherapy. These findings align with guidelines recommending daily intraoral PBM therapy for preventing cancer therapy-related OM, a dose-limiting toxicity for which effective preventive interventions are needed.

Glioblastoma is increasingly treated with Tumor Treating Fields (TTFields), but how post-craniotomy anatomy and fixation hardware alter delivered fields is unclear. We used finite-element modeling in a realistic head model to simulate TTFields after a standard bone flap with either a non-penetrating fixation plate or a penetrating skull clamp, and compared results to an intact-skull baseline across a range of clinically used array layouts. Bone gaps increased mean brain electric-field magnitude by [~]10-20%. Non-penetrating plates caused only minimal, localized changes relative to the bone-gap condition. In contrast, penetrating clamps produced strong but spatially confined increases: local mean fields were [~]6-8x higher within 5-10 mm of the device, with [≥] 50% enhancement extending [~]50-60 mm depending on whether the gap was modeled as healed scalp (soft-tissue-like) or healed bone; this enhancement decayed with distance. These simulations, performed in a single head model with literature-based tissue conductivities, suggest that penetrating hardware can substantially modulate local TTFields delivery, whereas non-penetrating plates have minimal impact. Accounting for post-surgical anatomy and hardware in TTFields planning may improve dose targeting.

BackgroundLattice radiotherapy (LRT) delivers heterogeneous dose distribution through a three-dimensional array of vertices within the tumor. It is typically applied in 1[~]5 fractions for patients with large tumor volumes. However, conventional LRT generally employs only a single vertex set, which may limit the biological advantages of this technique in multi-fraction treatments. PurposeThis study proposes a novel vertex arrangement strategy in LRT aimed at improving intratumoral dose homogeneity and enhancing coverage of high-dose regions through alternating irradiation of different vertex sets. Materials and methodsPatients with the gross tumor volume (GTV) between 300 cm3 to 2000 cm3 who received radiotherapy treatment at our institution were considered for inclusion. An "NaCl"-type structure was employed. Two sets of vertices ("Na"-type and "Cl"-type) were distributed within the tumor volume following a face-centered cubic (FCC) close-packed pattern analogous to the NaCl crystal structure. For each of the 10 patients with large tumor volumes (range: 319.23-1649.47 cc), two plans were generated: Plan A (optimized for "Na" vertices) and Plan B (optimized for "Cl" vertices). Each plan delivered 15 Gy per fraction to the vertices. Physical doses from Plans A and B were converted to EQD2 (/{beta} = 10 for GTV, /{beta} = 3 for normal tissues) and summed into three composite plans: A+A, A+B, and B+B. Plan quality was assessed using generalized equivalent uniform dose (EUD), homogeneity index (HI), D2, D98, and mean normal tissue dose (Dmean of NT). ResultsThe alternating composite plan (A+B) achieved significantly greater dose homogeneity compared to non-alternating plans (A+A and B+B), with a lower HI (1.23 {+/-} 0.08 vs. 1.70 {+/-} 0.08 and 1.70 {+/-} 0.09, p < 0.05) and higher EUD (3.76 {+/-} 0.38 Gy vs. 3.48 {+/-} 0.40 Gy and 3.42 {+/-} 0.25 Gy, p < 0.05). The low-dose metric D98 was also higher in A+B (4.23 {+/-} 0.27 Gy) than in A+A (3.92 {+/-} 0.25 Gy) and B+B (3.94 {+/-} 0.25 Gy). No significant difference was observed in NT Dmean among the three composite plans. ConclusionAlternating irradiation of two geometrically complementary vertex sets significantly improves dose coverage in high-dose regions and overall dose homogeneity without increasing normal tissue toxicity and potentially enhances therapeutic efficacy in spatially fractionated radiotherapy for large tumors.

BackgroundCurrently, no comprehensive tool exists to systematically assess patient safety in radiation oncology (RO). To address this gap, we developed the Patient Safety in Radiation Oncology questionnaire (PaSaRO), a German instrument enabling RO professionals to evaluate patient safety within their departments. MethodsBuilding on a literature review identifying 145 patient safety indicators (PSIs), we determined further PSIs via two focus groups with RO professionals, patient interviews, and expert consultations. RO professionals were recruited through professional networks and societies, while patients were recruited as a convenience sample at a university hospital centre. Content validity was ensured by assessing relevance and comprehensiveness through a Delphi study and comprehensibility through cognitive interviews with RO professionals. FindingsTwo focus groups generated 48 new PSIs, while nine patient interviews contributed 15 PSIs, and three experts suggested 12 more. In combination with the PSI from the review, a total of 213 PSIs were compiled and subsequently rated in the Delphi study by 84 professionals in the first round and 72 in the second. During this process, seven additional PSIs were suggested, and 158 were ultimately deemed relevant. In cognitive interviews, 43 PSIs were linguistically refined to improve clarity. InterpretationThis study produced a pilot version of the PaSaRO, comprising 158 consensus-based PSIs--the first comprehensive questionnaire specifically developed for RO. The rigorous multi-method development process ensures strong content validity. Future research will conduct psychometric evaluation, after which PaSaRO may serve as a standardized tool for assessing, monitoring, and improving patient safety in RO.

BackgroundAccurate contouring of target volumes and organs at risk is critical for radiotherapy. While deep learning (DL) models offer efficient automation, their generalizability to real-world clinical cases containing anatomical variations and artifacts requires rigorous validation. PurposeTo evaluate the clinical accuracy and robustness of RatoGuide, a novel DL-based auto-segmentation software, using a dataset derived from routine clinical practice including atypical cases. MethodsThis single-center retrospective study included 36 patients treated for head and neck, thoracic, abdominal, and pelvic cancers. The cohort was intentionally selected to encompass diverse anatomies and artifacts (e.g., pacemakers, artificial femoral head replacement). Auto-contours generated by RatoGuide were compared with expert-approved manual contours. Performance was evaluated quantitatively using the Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95), and qualitatively via a 5-point visual assessment scale (higher is better) by four independent reviewers. A score of [&le;]2 by multiple reviewers was defined as failure. ResultsOverall, the mean DSC, HD95, and visual assessment score were 0.79 {+/-} 0.19, 6.35 {+/-} 12.2 mm, and 3.65 {+/-} 0.88, respectively. The mean DSC exceeded 0.8 in 62% (23/37 organ structures) of the evaluated structure types, and a total of 93.5% (315/337) of all contours were considered clinically acceptable based on visual evaluation . However, lower performance was observed in small structures (e.g., optic chiasm) and low-contrast organs (e.g., esophagus). ConclusionsRatoGuide demonstrated favorable performance for major organs across various anatomical regions, consistent with benchmarks reported in the literature. However, performance variability in atypical cases underscores the necessity of rigorous visual verification by experts for clinical implementation.

BackgroundProstate cancer (PCa) is the most prevalent male cancer in the U.S., accounting for 29% of new cancer diagnoses. Multiparametric MRI (MP-MRI), including T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps, is an effective tool for detecting PCa; however, accuracy varies, and false-positives may lead to unnecessary biopsies or overtreatment. Radio-pathomic maps (RPMs), derived from MP-MRI and machine learning, have been advantageous in differentiating clinically significant PCa. This study tested whether RPMs of tissue density and histo-morphometric features could better predict cancer presence than conventional MR imaging. Materials and MethodsMP-MRI from 236 patients prospectively recruited between 2014 and 2023 with confirmed PCa were analyzed. Whole-mount prostate sections sliced to match the MRI were processed, digitized, and Gleason-pattern annotated by a GU pathologist. Automated algorithms identified glands and calculated quantitative histo-morphometric features, which were mapped across whole slide images. Slides were nonlinearly aligned to each patients T2WI using in-house software, enabling direct comparison of slides, features, and annotations in MR-space. A multi-step prediction model was trained using a 2/3 - 1/3 train/test split to predict histo-morphometric features using 5x5 voxel tiles from T2WI and ADC. These feature maps were then used generate tumor probability maps. ResultsHistological feature models produced RMSE values approximately within one standard deviation of the ground truths variability, indicating acceptable performance. The best RPM, using histological density features, achieved an accuracy of [~]80%. Visual inspection of RPMs showed good concordance to high-grade cancer annotations. ConclusionThis study demonstrates that the use of MRI intensities can predict complex histo-morphometric features and delineate regions of PCa non-invasively. Future research is warranted to determine the clinical benefit of using RPMs in treatment guidance.

DNA repair capacity (DRC), particularly at the pathway level, varies among individuals. While previous studies explored DRC in relation to environmental exposures and cancer risk, few measured DRC in patient-focused cohorts and were focused on one or two repair pathways only. We comprehensively profiled DRC for all the major repair pathways and DNA lesions in 100 lung cancer patients undergoing radiotherapy (RT) using advanced Fluorescence Multiplex based Host Cell Reactivation assays in blood cells before and after RT and investigated how DRC responded to RT and was influenced by clinical variables. Variation between individuals was significant in all pathways and smaller than variation within-person. DNA glycosylase activity decreased immediately following RT and subsequently returned to baseline in patients receiving high-intensity RT during the follow-up months. Lower DRC against oxidative lesions was found in cancer patients compared to healthy controls. These results highlight oxidative DNA damage repair as a sensitive marker of RT response and cancer burden upon profiling the DNA repair landscape. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/26343971v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1ddcd5forg.highwire.dtl.DTLVardef@d642fdorg.highwire.dtl.DTLVardef@c7f38corg.highwire.dtl.DTLVardef@1469bea_HPS_FORMAT_FIGEXP M_FIG C_FIG

Medical image segmentation remains a critical bottleneck in clinical workflows, from diagnostic radiology to radiation oncology treatment planning. We present Onco-Seg, a medical imaging adaptation of Metas Segment Anything Model 3 (SAM3) that leverages promptable concept segmentation for automated tumor and organ delineation across multiple imaging modalities. Unlike previous SAM adaptations limited to single modalities, Onco-Seg introduces a unified framework supporting CT, MRI, ultrasound, dermoscopy, and endoscopy through modality-specific preprocessing and parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). We train on 35 datasets comprising over 98,000 cases across 8 imaging modalities using sequential checkpoint chaining on a 4-GPU distributed training infrastructure. We evaluate Onco-Seg on 12 benchmark datasets spanning breast, liver, prostate, lung, skin, and gastrointestinal pathologies, achieving strong performance on breast ultrasound (Dice: 0.752{+/-}0.24), polyp segmentation (Dice: 0.714{+/-}0.32), and liver CT (Dice: 0.641{+/-}0.12). We further propose two clinical deployment patterns: an interactive "sidecar" for diagnostic radiology and a "silent assistant" for automated radiation oncology contouring. We release an open-source napari plugin enabling interactive segmentation with DICOM-RT export for radiation oncology workflows. Code and models are available at https://github.com/inventcures/onco-segment.

ObjectivesHead and neck cancer (HNC) has a high recurrence rate. Safety and effectiveness of PepCan in reducing recurrence for HNC patients were assessed. Methods and analysisPepCan consists of four human papillomavirus 16 (HPV 16) E6 peptides and a Candida skin testing reagent (Candin(R), Nielsen Biosciences) as a vaccine adjuvant. Since Candida was known to have a general immune stimulating effects, patients were recruited regardless of their HPV status. Men and women with HNC who had no evidence of disease after standard surgery, chemotherapy, and/or radiation treatments were enrolled. They were randomized at 3:1 to PepCan versus placebo. Seven intradermal injections of PepCan or placebo (saline) were given every 3 weeks (first 4 injections) or 3 months (last 3 injections). They were followed with two visits 6 months apart. Safety was assessed using Common Terminology Criteria for Adverse Events version 5, and efficacy was assessed based on not having recurrence within 2 years. In addition, immune responses were examined using enzyme-linked immunospot assay for HPV 16 E6 response, fluorescent-activated cell sorter analysis for peripheral immune cells, and T cell repertoire analysis. Peripheral cytokines and gut and oral microbiome were also analyzed. ResultsSeventeen patients were enrolled. The most common adverse events were grades 1 and 2 injection site reactions, and they occurred more frequently in the PepCan group (p<0.0001). Two patients had allergic reactions (grade 2 and grade 3), at the 6th vaccination, which were considered to be a dose-limiting toxicity (DLT). No serious adverse events were reported. In the intention-to-treat analysis (ITT), 45% (5/11) had non-recurrence in the PepCan group while 80% (4/5) had non-recurrence in the placebo group. For the per-protocol (PP) analysis, non-recurrence was 56% (5/9) for PepCan and 80% (4/5) for placebo. These differences were not statistically significant. Those who received PepCan and experienced non-recurrence had higher new T cell immune responses to HPV 16 E6 (p=0.05 for ITT and p=0.02 for PP). Pre-vaccination T helper type 1 cells were higher in the PepCan non-recurrence group compared to the PepCan recurrence group (p=0.01 for ITT and PP). ConclusionsPepCan is safe although DLT can occur after multiple injections of PepCan. PepCan does not seem to be effective in reducing recurrence; however, the results are inconclusive given the small patient numbers. What is already known on this topic O_LIHead and neck cancer (HNC) has a high recurrence rate after reaching no evidence of disease status after standard therapies including chemotherapy, radiation, immunotherapy and survey. However, no intervention is available to reduce recurrence. C_LI What this study adds O_LIA therapeutic human papillomavirus vaccine called PepCan was tested in a clinical trial and has been shown to be safe. C_LI How this study might affect research, practice or policy O_LIIf a sufficiently powered study demonstrates efficacy in reducing recurrence rate, then how HNC patients are treated after achieving the no evidence of disease status will change. C_LI

BackgroundPrimary central nervous system (CNS) tumors affect patients psychological well-being and quality of life. Individualized approaches, such as Managing Cancer and Living Meaningfully (CALM), have shown potential in advanced cancers for improving these outcomes. AimsThis study assessed the effects and feasibility of CALM delivered remotely to a diverse cohort of patients with a primary CNS tumor. MethodsPatients completed 3-6 remote CALM sessions focusing on 4 interrelated domains. Depression, death anxiety, attachment style, and quality of life were assessed at study enrollment, 3-months, and 6-months into the intervention. ResultsOf the 19 patients enrolled, 15 (79% retention rate) completed the study. Most patients had a high-grade (47%) tumor, mainly diagnosed in the brain (60%). The median age was 44 years (range, 24-70). Feasibility was demonstrated through adherence to completing outcome questionnaires and a high level of patient satisfaction (100% found it worthwhile). Although no statistically significant changes were seen in depression, death anxiety, attachment anxiety, or quality of life (p > 0.05; g = -0.09 to 0.78) at any measured time, a clinically meaningful decrease in depression was observed at the 6-month point (mean difference = -3.36, p = 0.13) among spine tumor patients. ConclusionsThis study demonstrated that delivering CALM via telehealth is feasible, as evidenced by high compliance, low attrition, and acceptability among patients diagnosed with CNS tumors. The findings indicated meaningful reductions in depressive symptoms among patients with spinal cord tumors. These preliminary positive findings justify further evaluation of the feasibility and effectiveness of CALM in a larger sample. Trial registrationClinicalTrials.gov ID NCT04852302

The relationship between red blood cell (RBC) storage duration and cancer recurrence remains controversial, with the "storage lesion" potentially amplifying transfusion-related immunomodulation effects. This retrospective cohort study examined whether RBC storage duration is independently associated with biochemical recurrence following radical prostatectomy for prostate cancer. We analyzed 316 men who underwent radical prostatectomy with perioperative allogeneic RBC transfusion at Cleveland Clinic (1998-2007). Patients were stratified by RBC storage duration: younger ([&le;]13 days, n=106), middle (13-18 days, n=103), and older ([&ge;]18 days, n=107). Primary outcome was biochemical recurrence (PSA [&ge;]0.4 ng/mL). We employed Kaplan-Meier survival analysis, Cox proportional hazards regression, and multivariable logistic regression with comprehensive diagnostics. Missing data (10.8%) were handled using multiple imputation by chained equations (MICE). Over median follow-up of 36.2 months, 54 patients (17.1%) experienced biochemical recurrence. RBC storage groups demonstrated exceptional baseline balance (all p>0.05). Kaplan-Meier analysis showed virtually identical survival curves (log-rank p=0.98). In fully adjusted Cox regression, neither middle (HR=1.01, 95% CI: 0.47-2.18, p=0.987) nor older (HR=0.82, 95% CI: 0.38-1.76, p=0.569) RBC storage was associated with recurrence. Logistic regression yielded consistent results (middle: OR=0.85, p=0.692; older: OR=0.93, p=0.854). Effect estimates remained stable across progressive covariate adjustment. In contrast, tumor biology factors demonstrated powerful associations: Gleason 8-10 (OR=5.72, p=0.001), log(PSA) (OR=2.24, p=0.007), and organ confinement (OR=0.48, p=0.047). Model discrimination was excellent (C-index=0.798, AUC=0.805), driven entirely by tumor characteristics. RBC storage alone provided zero discriminative ability (AUC=0.511). RBC storage duration (10-25 days) is not associated with biochemical recurrence after radical prostatectomy. This null finding is consistent across three complementary analytical methods. Tumor biology dominates prognosis with effect sizes far exceeding any potential transfusion effects. Current blood banking practices appear oncologically safe within typical storage windows.

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

Importance: Lung cancer mortality in the United States has fallen substantially in recent decades, yet the relative influence of behavioral, environmental, socioeconomic, and therapeutic factors and their sex specific contributions remains unclear. Understanding these drivers is essential to sustain progress and reduce persistent disparities. Objective: To quantify how behavioral, environmental, socioeconomic, and therapeutic determinants collectively shaped US lung cancer mortality from 1994 to 2020, assess sex specific differences, and forecast mortality trajectories through 2030 using an integrated machine learning framework. Design, Setting, and Participants: Ecological time series study using publicly available national data from 1994 to 2020. Sex stratified analyses were conducted integrating lung cancer mortality, smoking prevalence, fine particulate matter PM2.5 exposure, Human Development Index HDI, per capita healthcare expenditure, healthcare inflation, insurance coverage, income inequality, and annual drug approvals. Exposures: Behavioral smoking, environmental PM2.5, socioeconomic HDI health expenditure inflation, uninsurance inequality, and therapeutic drug approval indicators. Main Outcomes and Measures: Age-standardized lung cancer mortality per 100000 population. Temporal changes were modeled using Joinpoint regression. Concurrent associations were assessed using multivariable and elastic net regression, and forecasts were estimated with AutoRegressive Integrated Moving Average models with exogenous variables ARIMAX. Results: From 1994 to 2020, mortality declined by 59 percent in men, from 52.9 to 21.7 per 100000, and by 40 percent in women, from 26.7 to 15.9 per 100000, with faster declines after 2015. Smoking and PM2.5 decreased by more than 45 percent but remained strongly correlated with mortality. In elastic net models, PM2.5 was the strongest predictor for men, while smoking was the strongest predictor for women. Per capita expenditure and HDI ranked higher for men, while uninsurance and income inequality were strong predictors for women. Mortality declines occurred during periods of major approvals of lung cancer drugs. Forecasts suggest continued but slower declines through 2030, with projected rates of 20.2 and 14.9 deaths per 100000 in men and women, respectively. Conclusions and Relevance: Sex specific declines in lung cancer mortality reflect different dominant correlates, with air pollution more important in men and smoking more important in women, while socioeconomic conditions and therapeutic advances also influence trends. Continued tobacco control, improved air quality, and equitable access to screening and modern treatment are essential to sustain further reductions in mortality. Keywords: Lung Neoplasms, Sex Factors, Air Pollution, Smoking, Socioeconomic Factors, Machine Learning

PurposePatient-reported outcomes (PROs) provide a quantitative measure of a patients quality of life, directly from the patient without external influence or interpretation. Prior studies have demonstrated correlations between individual PROs and cancer treatment response. However, this area of research is still highly understudied, and patient data often goes ignored. Our previous work has shown how changes in insomnia can be used to make binary decisions about a patients future volume response. Here, we expand upon that work to determine precisely when treatment progression will occur, providing an opportunity for clinicians to intervene sooner. Experimental DesignThis study analyzed PROs and tumor volume data collected from 80 NSCLC patients undergoing immunotherapy to determine how PRO dynamics could inform when volumetric treatment progression would occur. We calibrated the tumor growth inhibition (TGI) model to patient-specific tumor volume dynamics for all volume measurements using a leave-one-out cross-validation approach. Growth parameters were divided based on progression status and sampled depending on changes in patient-reported insomnia. A cutoff analysis was performed to determine the optimal cutoff for distinguishing between responders and non-responders. Predictions were made for the Nth patient and categorized using the cutoff. ResultsThis study demonstrated that incorporating patient-specific changes in insomnia with a mathematical model of volume changes can predict patient response with a 72.2% true positive rate and 71.3% overall accuracy, on average 6-8 weeks sooner. ConclusionUsing this innovative framework, we can predict precisely when progression occurs, giving clinicians the opportunity to intervene beforehand.

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

BackgroundOnce the treatment starts, early prediction of treatment benefit and its correlation with overall survival (OS) remains challenging in metastatic castration-resistant prostate cancer (mCRPC). Existing prognostic models require long-term follow-up, limiting their ability to inform timely treatment decisions. To address this gap, we evaluated tumor growth rate (g-rate)-based survival models across multiple treatment lines to assess their ability to predict OS and support early clinical decision-making. MethodsWe developed GxSurv, a Random Survival Forest (RSF)-based framework that incorporates baseline clinical variables and g-rate calculated from serial on-treatment PSA, to construct line-specific prediction models of OS, a direct measure of treatment outcome. Three variants were developed: G3Surv, using the 3-month g-rate; G6Surv, using the 6-month g-rate; and GfSurv, using the final observed g-rate. Model performance was evaluated using Harrells C-index, Unos C-index, Integrated Brier Score (IBS), time-dependent area under the curve (tAUC). Model interpretability was assessed using permutation importance to quantify predictor contributions within the GxSurv framework. FindingsThe study included 15912 treatment records from 11014 patients with mCPRC across four lines of therapy. We found that incorporation of g-rate consistently improved model performance across all treatment lines, with all GxSurv models outperforming Cox proportional hazards (CoxPH). As the earliest prognostic model, our G3Surv demonstrated strong early predictive performance, with Harrells C-index values ranging from 0{middle dot}700 to 0{middle dot}746 and tAUC values of 0{middle dot}766 to 0{middle dot}822 across all lines, representing 5-8% and 4-5% improvements over CoxPH, respectively. These results indicate that G3Surv accurately predicts individual treatment outcomes at 3 months after treatment initiation. Feature importance analyses consistently identified g-rate as a top predictor, followed by baseline PSA and hemoglobin, with relative variation across treatment lines. InterpretationIntegrating g-rate calculated from on-treatment PSA values enables accurate, line-specific prediction of treatment outcomes in mCRPC, with the 3-month g-rate providing robust early prognostic information to support timely, personalized clinical decision-making. FundingU.S. National Science Foundation, National Institutes of Health, American Cancer Society.

BackgroundMeningioma-induced hyperostosis (MIH) is a frequent radiographic finding, yet its underlying mechanisms remain poorly understoodWhile hyperostosis has traditionally been treated as a binary phenomenon, the aim of this study was to determine whether MIH represents a heterogenous process with distinct radiological subtypes associated with genetic associations. MethodsWe retrospectively reviewed the records and imaging of patients with meningiomas resected between 2021-2024 at a single institution. Somatic mutations identified through next-generation sequencing were analyzed. CT images were analyzed for bone involvement and hyperostosis subtype. Type I hyperostosis was defined by destruction of cortical architecture while Type II hyperostosis was defined by the preservation of cortical structure. Associations with TRAF7 mutations were assessed using univariate testing, multivariable logistic regression, and supervised machine-learning models. Quantitative bone density analysis was performed using region-of-interest grayscale histogram analysis. ResultsAmong 384 tumors, 54 (14.1%) exhibited hyperostosis--23 Type I and 31 Type II. TRAF7 mutations were significantly enriched in Type I hyperostosis compared with Type II and non-hyperostotic tumors (78.3% vs 25.8% vs 17.0%, p<0.001). Type I hyperostosis independently predicted TRAF7 mutations (OR:18.73, p=0.001), along with skull base location, smaller tumor size, homogeneous contrast enhancement, and extensive T2 hyperintensity. Gradient boosting achieved the highest predictive accuracy (AUC=0.854). Quantitative bone density analysis demonstrated preserved cortical-cancellous architecture in Type II hyperostosis, whereas Type I showed architectural disruption. ConclusionsMIH is a radiographically heterogenous phenomenon. Hyperostosis with disrupted cortical architecture is strongly associated with TRAF7 mutations and may represent a key feature of this mutations radiographic phenotype.

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

Structured AbstractO_ST_ABSPurpose/ObjectivesC_ST_ABSThe role of postmastectomy radiotherapy (PMRT) in patients with pathologic N1 (pN1) breast cancer, including triple-negative breast cancer (TNBC), remains controversial in the era of modern systemic therapy. We evaluated the association between PMRT and recurrence-free survival (RFS) and overall survival (OS) and identified prognostic factors in a contemporary single-institution pN1 cohort. Materials/MethodsWe retrospectively reviewed female patients with pT1-2N1M0 breast cancer treated with mastectomy between 2016 and 2022. RFS and OS were estimated using Kaplan-Meier methods and compared by PMRT status with log-rank testing. Univariable Cox proportional hazards models assessed associations between clinical factors--including tumor laterality, receptor subtype (TNBC vs non-TNBC), nodal burden, and adjuvant therapies--and survival outcomes, with subgroup analyses by PMRT status and receptor subtype. ResultsFifty-seven patients were included; 22 (38.6%) received PMRT. With a median follow-up of 85 months, PMRT was not associated with improved RFS (median 133 vs 120 months; p=0.256) or OS (not reached vs 195 months; p=0.154). Hormone therapy was significantly associated with improved RFS (HR 0.43; p=0.026) and OS (HR 0.13; p=0.003), while having 2-3 positive lymph nodes predicted worse RFS (HR 2.86; p=0.007). No significant differential benefit from PMRT was observed in patients with TNBC or non-TNBC disease. ConclusionsPMRT was not associated with a survival benefit in this pN1 cohort, including patients with TNBC. Interpretation is limited by modest sample size and statistical power. Outcomes appeared driven by tumor biology, nodal burden, and systemic therapy, supporting individualized PMRT decision-making.