Citations

Background The Woven EndoBridge (WEB) device represents an innovative solution for cerebral aneurysm occlusion, particularly for challenging wide-neck bifurcation aneurysms. However, factors affecting sustained occlusion remain poorly understood. We utilized machine learning to attempt to identify predictors of favorable long-term outcomes following WEB treatment. Methods In this multicenter retrospective study, we collected patient demographics, aneurysm characteristics, procedural details, and clinical outcomes. The primary endpoint was improvement in occlusion status, defined as maintained Raymond-Roy Occlusion Classification (RROC) grade 1, or improvement from grade 2 to 1, or from grade 3 to either 2 or 1 on final angiographic follow up. The dataset was split into training (75%) and validation (25%) sets. The CatBoost algorithm was selected based on performance metrics, with Shapley Additive exPlanations (SHAP) values calculated to determine feature importance. Furthermore, a multivariable binomial logistic regression model was performed to validate machine learning findings. Results Among 720 aneurysms from 36 hospitals, 84% showed improvement in occlusion at follow up. Both machine learning and multivariable logistic regression identified aneurysm height as the most consistent correlate of nonimprovement (odds ratio (OR) 0.90 per mm, p = 0.022). In the CatBoost model, the highest-ranking features by SHAP included aneurysm height, patient age, treatment acuity, ACom location, WEB-SLS device, bifurcation anatomy, aneurysm multiplicity, baseline modified Rankin Scale, access route, and partial thrombosis. Conclusions Machine-learning and regression analyses identified consistent predictors of occlusion improvement after WEB treatment, with aneurysm height most strongly linked to nonimprovement. These insights may guide patient selection and follow up. Findings require cautious interpretation and external validation in larger cohorts.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

Background Immune checkpoint therapies targeting programmed cell death protein 1 (PD-1) and programmed death ligand 1 (PD-L1) have been met with limited clinical responses in the treatment of patients of varying cancer types. Current US Food and Drug Administration (FDA)-approved therapies function to prevent PD-1 binding to PD-L1, thereby preventing T-cell suppression. However, these therapies fail to prevent PD-L1 intrinsic signaling and recycling, which can render these treatments ineffective. Moreover, most research primarily focuses on how these antibodies affect tumor cells and their interactions with T cells. Myeloid cells, which also express PD-L1 and influence T-cell responses, are integral to the efficacy of these therapies. However, strategies that can target these cells to boost antitumor responses have not been effective. Methods Our group has developed a new anti-PD-L1 antibody (H1A), which disrupts PD-L1 recycling and redirects it towards degradation. Due to the loss of available PD-L1 protein, H1A effectively prevents PD-L1 intrinsic signaling. Using in vivo humanized PD-1/PD-L1 mouse tumor models, we evaluated the therapeutic efficacy of H1A against current FDA-approved PD-L1 blocking antibodies. Using human-derived immune cell in vitro assays, we pinpoint the biological consequences of H1A-induced PD-L1 degradation in myeloid cells. Results H1A demonstrated improved tumor control and established immunological memory responses in vivo in the treatment of tumors with moderate immunogenicity that are less responsive to current immunotherapies. This was supported by enhanced activation of myeloid cells (major histocompatibility complex (MHC)-II and CD80) and frequencies of effector T-cell populations found intratumorally. Human myeloid cells treated with H1A were also observed to have increased activation (MHC-II and CD80) and cytokine secretion. Similar to our in vivo findings, human peripheral blood lymphocyte cultures demonstrated increased frequencies of effector T-cell populations and greater tumor cell killing. Conclusions This work highlights the importance of targeting PD-L1 recycling and intrinsic signaling, specifically in myeloid cells, to boost antitumor responses. Our studies suggest that H1A can provide a solution to the lack of responses seen with current therapeutics, while also revealing previously unknown intrinsic functions of PD-L1 in myeloid cells.

Prostate cancer (PCa) progression is driven by intricate molecular mechanisms involving dysregulated signaling networks and posttranslational modifications of key regulatory proteins. In this study, we identify a novel oncogenic pathway wherein cyclin-dependent kinase 12 (CDK12) physically interacts with and phosphorylates forkhead box A1 (FOXA1) at serine 234 (S234). Phosphorylation at this residue markedly enhances FOXA1 transcriptional activity, leading to up-regulation of downstream targets including murine double minute 2 (MDM2), a critical negative regulator of the p53 tumor suppressor. Mechanistically, this CDK12–FOXA1–MDM2 axis destabilizes p53, attenuates apoptotic signaling, and promotes PCa cell survival and proliferation. Therapeutic targeting of CDK12 using the small-molecule inhibitor THZ531 or RNA interference effectively abrogates FOXA1 phosphorylation, restores p53 stability, reactivates apoptotic pathways, and suppresses tumor growth. Notably, the identification of S234 as a functional phosphorylation site in FOXA1 reveals a previously uncharacterized posttranslational regulatory mechanism in PCa biology. These findings establish the CDK12–FOXA1–MDM2 axis as a pivotal driver of PCa progression and underscore the therapeutic potential of targeting FOXA1 phosphorylation to restore tumor suppressor function and induce apoptosis in PCa. Our work provides a mechanistic framework for developing precision therapies aimed at disrupting this oncogenic cascade in PCa.

Abstract INTRODUCTION Preclinical Alzheimer's disease (AD) can be described relative to biomarker positivity onset time. METHODS We estimated time from amyloid positivity (A+) using sampled iterative local approximation (SILA) in a longitudinal autosomal dominant AD (ADAD) sample ( N = 379) with amyloid positron emission tomography. We compared (1) predicted age at A+ to imputed age, (2) estimated age at A+ to estimated age at symptom onset, and (3) variance in cognitive performance explained. RESULTS Mean error between imputed and SILA‐estimated age at A+ ( N = 26) was 1.15 years. Age at A+ explained 39% of estimated years to symptom onset (EYO) variance. Time from A+ explained 19% of cognitive composite variance and 14% of Clinical Dementia Rating Sum of Boxes CDR‐SB variance; EYO explained 43% and 57%, respectively. DISCUSSION SILA estimates A+ age in ADAD with reasonably good accuracy. SILA‐estimated time from A+ describes the start of pathology, but the time from A+ onset to symptoms is variable in ADAD and better described by EYO. Highlights Amyloid chronicity predicts a 14‐year preclinical AD phase in ADAD. SILA accurately estimates age at A+ (MAE < 2 years). EYO outperforms chronicity in predicting symptom onset. APP mutation carriers show atypical amyloid accumulation. Chronicity models help reveal AD heterogeneity in preclinical stages.

BACKGROUND: Intracranial atherosclerosis (ICAS) is associated with an increased risk of early recurrent ischemic stroke. We evaluated whether biomarkers of impaired distal perfusion—specifically, anterior circulation borderzone infarct, and hypoperfusion mismatch volume—were associated with recurrent ischemic stroke within 90 days. METHODS: The BIORISK ICAS (Biomarkers and Recurrence Risk in Symptomatic Intracranial Atherosclerosis) is a multicenter retrospective international study (35 sites) that included hospitalized patients with symptomatic ICAS (50%–99% luminal stenosis of the intracranial vertebral, basilar, distal internal carotid, or proximal middle cerebral artery) from January 2019 to June 2024. The primary outcome was recurrent ischemic stroke in the territory of the symptomatic artery within 90 days. In the primary analysis, the exposure was an acute anterior circulation borderzone infarct. In secondary analysis of the subset with presentation within 72 hours of last known normal and perfusion imaging completed, the exposure of interest was prespecified as hypoperfusion mismatch volume at T max (time to maximum) threshold of 6 seconds, dichotomized at the Youden index. We performed multivariable Cox regression to test associations between exposure variables and the outcome, adjusting for clinically relevant variables and those associated with the outcome ( P <0.1). RESULTS: Of 2050 patients with symptomatic ICAS, 1737 (84.7%) presented within 72 hours of symptom onset, among whom 509 (29.3%) underwent perfusion imaging. The primary analysis included 1891 patients; 174 (9.2%) patients had recurrent ischemic stroke in the symptomatic arterial territory. In adjusted Cox regression models, there was an association between anterior circulation borderzone infarct and recurrent ischemic stroke at 90 days (adjusted hazard ratio, 1.40 [95% CI, 1.02–1.93]). In the perfusion imaging analysis, hypoperfusion mismatch of ≥10 mL was associated with recurrent ischemic stroke (adjusted hazard ratio, 1.83 [95% CI, 1.03–3.28]). CONCLUSIONS: Biomarkers of impaired distal perfusion, anterior circulation borderzone infarct, and hypoperfusion mismatch were associated with increased risk of recurrent ischemic stroke. These findings support the use of perfusion imaging in ICAS as well as future trials investigating early reperfusion in high-risk patients with ICAS.

Abstract Increased demand to make medical linear accelerator acceptance testing procedures (ATP) more efficient exists while the technology being evaluated for clinical use has become increasingly complex. The most recent American Association of Physicists in Medicine (AAPM) task group giving guidelines to medical physicists on ATP was Task Group report 45, published in 1994. Several new technologies, enhanced machine performance, and possible future changes to ATP since that time require updated guidance from the AAPM. The goal of this task group is to empower the medical physicist to be meaningfully involved with the design and execution of comprehensive and effective linear accelerator ATP beginning with the purchasing contract negotiation to the contractual handover of the linear accelerator to the department for clinical use. Additionally, we discussed aspects related to beam matching, ATP at the time of replacing major linear accelerator's components as well as what we perceive as the future of the ATP process given the trends of standardizing linear accelerators.

Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.