Citations

Abstract Objective. Four-dimensional CT (4DCT) enables visualization of dynamic wrist joint pathology but may be compromised by motion artifacts. Deep learning (DL)-assisted motion correction has the potential to enhance wrist 4DCT quality, but model development is hindered by scarce training data. To address this, we developed a model-based motion simulation framework that generated realistic 4DCT data with paired static ground truth images to demonstrate wrist motion correction using a physics-informed DL model trained with simulated data. Approach. Wrist motion was simulated by synchronizing rotational and translational movements of carpal bones and connective tissues. Elastic deformation guided by analytically-derived motion vector fields was applied to static wrist scans at neutral positions to create artifact-free images at multiple angular positions. Virtual motion-corrupted 4DCT images were produced via forward projection and reconstruction with synchronized gantry and wrist motion. Experimental data from 4DCT cadaver scans were collected to validate simulation quality. Low-intensity region score (LIRS), positivity, and entropy were used to assess simulated motion artifacts. A physics-informed motion correction network was adapted exclusively using simulated training data and subsequently deployed on simulated, real cadaveric, and in vivo 4DCT scans. LIRS and structural similarity index (SSIM) were used to evaluate motion correction quality. Wilcoxon signed-rank tests were conducted ( α = 0.05). Results. The simulation method created realistic motion artifacts compared to real scan reference ( p > 0.05). Motion artifacts were effectively mitigated across testing cases demonstrating generalizability. In simulated scans, LIRS improved from 0.55 ± 0.12–0.75 ± 0.04 and SSIM from 0.87 ± 0.08–0.95 ± 0.03 at bone regions (each p < 0.01). In experimental cadaveric scans, LIRS improved from 0.05 ± 0.01–0.31 ± 0.17 in marker regions and from 0.58 ± 0.19–0.77 ± 0.14 in bone regions (each p < 0.01). Significance. Our simulation method facilitates training DL-assisted motion correction algorithms by providing high-quality training data, effectively supporting 4DCT image quality enhancement.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Background Tumor-infiltrating lymphocytes (TILs) are established determinants of clinical outcomes in breast cancer. 1–6 While stromal TILs (sTILs) are commonly evaluated due to easier scoring and higher interobserver reproducibility, intraepithelial TILs may provide additional insights into anti-tumor immunity. 7 We aimed to comprehensively characterize immunotype-associated cellular landscapes in triple-negative breast cancer (TNBC) and identify features associated with clinical outcomes. Methods 212 TNBC tumors were classified into three immunotypes based on immune infiltrate location and density: immune desert (ID), immune excluded (IE), and immune inflamed (IN). 8 Single-cell spatial transcriptomics was performed on 75 tumors to define the cellular composition and functional states of the tumor microenvironment across immunotypes. Findings were validated and integrated with bulk RNA sequencing data and clinical outcome datasets from FinXX, CALGB-40603, I-SPY 2, and Real-World Clinico-Genomic Data (RWCGD) cohorts. Results Patients with IN tumors had significantly improved outcomes compared with ID tumors. Despite high sTILs, IE tumors showed poor outcomes similar to ID tumors. Single-cell spatial analysis revealed that ID and IE tumors exhibited reduced major histocompatibility complex I/II expression and fewer tumor-resident plasmacytoid dendritic cells (pDCs) compared with IN tumors, which were enriched for interferon-alpha (IFNα) and interferon-gamma (IFNγ) responses. High IFN response scores were associated with favorable outcomes across multiple therapy types in independent datasets. Deconvolution of RWCGD bulk RNA-seq data confirmed that pDC abundance correlated with improved outcomes specifically in hormone receptor–negative subtypes. Conclusions Our study highlights pDCs and IFN signaling as hallmarks of effective anti-tumor immunity in TNBC. Immunotype-based profiling underscores the limited prognostic value of sTILs alone in immune-excluded tumors and supports pDCs and IFN pathways as potential biomarkers for prognosis and therapeutic development in TNBC.

TPS650 Background: The TAILORx and RxPONDER trials showed that recurrence score (RS) identifies many postmenopausal pts who do not benefit from addition of ACT to endocrine therapy (ET); however, RS also identifies subsets who do benefit (node-neg/high clinical risk/RS 16-20, node-neg/RS 21-25, and node-pos/RS ≤25). Most premenopausal pts in these trials did not receive ovarian function suppression (OFS) as part of their ET regimen. Given the observed benefit from OFS in high-risk premenopausal pts with HR+/HER2– BC in SOFT/TEXT, we question whether the ACT benefit in TAILORx/RxPONDER was due to chemotherapy-induced OFS. To address this, we developed OFSET, a phase III clinical trial comparing OFS+ET to ACT+OFS+ET. Methods: We hypothesize that addition of ACT to OFS+ET is superior to OFS+ET in improving invasive breast cancer-free survival (IBCFS) among premenopausal pts with early-stage HR+/HER2– tumors, and a 21-gene RS between 16-25 (for pN0 pts) and 0-25 (for pN1 pts). Secondary objectives include invasive disease-free survival, overall survival, distant recurrence-free interval, breast cancer-free interval, and health-related quality of life (HRQOL). Pts must be node-neg with RS 16-20 (plus high clinical risk), RS 21-25, or have 1-3 positive nodes with RS ≤25. Stratification is by nodal status/RS status (pN0 RS 16-25; pN1 RS 0-15; pN1 RS 16-25), intent to receive CDK4/6 inhibitor (yes; no), and age (18-39; ≥40). Pts are randomized after surgery to either OFS+ET or ACT+OFS+ET. ET is an aromatase inhibitor (AI) per physician discretion; or tamoxifen if AI is not tolerated or if OFS is incomplete. Radiotherapy will be administered per protocol guidelines. An HRQOL sub-study will assess severe menopausal symptoms (measured by FACT ESS-19) and pain severity (PROMIS) between arms. Blood and tumor specimens will be collected for future research. We anticipate accrual of 3,960 pts in 7 yrs, 7 mos. Per NSABP B-28/RxPONDER data, 5-yr IBCFS of pN1 pts on the ACT+OFS+ET arm is estimated at 92.3%. Per TAILORx data, 5-yr IBCFS of pN0 pts on the ACT arm is ~95%. Assuming 56% of pts to be pN0 and 44% pN1, and a 0.5% annual loss-to-follow-up rate, the definitive analyses to detect a hazard ratio: 0.75 with ACT+OFS+ET v OFS+ET, with 1-sided α of 0.025 and 80% power, will require 380 IBCFS events, expected to occur ~11 yrs after study initiation. Accrual is 496/3,960 (from 8/2023 to 1/2026). NCT #: NCT05879926. Clinical trial information: NCT05879926 .

Abstract Background Radiomics extracts quantitative imaging features from computed tomography (CT) data for clinical decision‐making. However, variations in acquisition parameters—particularly x‐ray tube voltage (kV)—introduce non‐biological variability in attenuation values, limiting the reproducibility of radiomic features across scanners, protocols, and institutions. Purpose To develop and evaluate a CT dAta harmoNiZAtion framework based on deep learNed vIrTual monoEnergetic imaging (TANZANITE), which leverages the keV flexibility of virtual monoenergetic images (VMIs) to enable cross‐kV scan translation and radiomics harmonization. Methods TANZANITE is a model hub consisting of multiple pre‐trained convolutional neural networks (CNNs), each designed to translate VMIs from 1 keV level to another. Phantom‐based calibration was first used to determine energy‐equivalent (Eff_E) keV levels corresponding to each tube potential (e.g., Eff_E(A) keV for source kV and Eff_E(B) keV for target kV). A CNN trained using 69 120 patches from seven patient cases to map VMIs from Eff_E(A) to Eff_E(B) was selected from the TANZANITE hub and applied directly to clinical CT images acquired at the source kV. This harmonized the images to match the attenuation characteristics of the target kV setting. Evaluation was conducted on independent dual‐energy CT datasets acquired at 100/Sn150 kV. Regions of interest (ROIs) were placed in the kidney, liver, and spine to assess CT number consistency and radiomic feature reproducibility. The concordance correlation coefficient (CCC) was calculated across 93 non‐shape radiomic features. Results After TANZANITE processing with 100 kV images, CT numbers in evaluated organs closely matched the Sn150 kV reference values in four testing patient cases. For example, mean kidney CT numbers changed from 320 HU (100 kV) to 156 HU (TANZANITE), approximating the Sn150 kV value of 160 HU. Similar changes were observed in the liver (157–105 HU vs. 104 HU reference) and spine (45–22 HU vs. 19 HU reference). Radiomic reproducibility improved substantially across organs: mean CCC increased from 0.590 to 0.995 in the liver, 0.300 to 0.970 in the kidney, and 0.630 to 0.968 in the spine. Post‐TANZANITE, over 98% of features exceeded the stability threshold (CCC ≥ 0.900) in all three representative organs. Conclusion TANZANITE provides a flexible, image‐domain harmonization framework by learning the keV‐to‐keV translation in the VMI domain and applying pre‐trained CNNs to clinical kV images. It improves CT number consistency and organ‐specific radiomic reproducibility without requiring raw projection data or scanner‐specific training. This approach supports consistent quantitative imaging across multiple‐kV acquisition protocols, enhancing radiomics reliability in clinical settings.

OBJECTIVE: A definition of refractory septic shock is necessary to guide diagnosis, management, prognostication, research, and future guidelines for this most severe form of the disease. We sought to achieve consensus on clinical criteria that would be used to define refractory septic shock. DESIGN: Review of literature, expert panel position statements, and Delphi rounds with an international expert group. SETTING: Consensus was defined as having at least 75% of panellists in agreement or disagreement on the three highest or lowest levels of a 7-point Likert scale or based on responses to single- or multiple-choice questions, respectively. SUBJECTS: A panel of multinational, multiprofessional and multidisciplinary critical care experts assembled by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine (57 invitations and 56 participants). MEASUREMENTS AND MAIN RESULTS: A five-round Delphi process was conducted for consensus and stability. The steering committee proposed 34 statements, and five of them were rejected by panel experts after round 2. Among 29 statements selected from eight domains, consensus was reached for 13. The panel agreed on the need for a comprehensive consensus set of clinical criteria for refractory septic shock. Markers of organ dysfunction (75%, 2 rounds), tissue perfusion (91.1%, 2 rounds) including lactate (94.6%, 2 rounds) and capillary refill time (76.8%, 2 rounds), assessment of fluid-responsiveness after initial resuscitation (92.9%, 5 rounds), and use of vasoactive drugs at norepinephrine equivalents greater than 0.5 µg/kg/min (75.0%, 3 rounds), were selected as clinical criteria of refractory septic shock. The use of critical care ultrasound (CCUS) (92.9%, 3 rounds) was the single diagnostic modality that reached a consensus-based agreement. CONCLUSIONS: A consensus for 13 criteria to frame the definition of refractory septic shock was reached. Refractory septic shock is characterised by persistently elevated lactate concentrations and or prolonged capillary refill time in patients with septic shock who are fluid unresponsive, require a norepinephrine base equivalent dose greater than 0.5 micrograms per kilogram per minute, and undergo CCUS assessment when mixed shock is suspected.

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.