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Background: Sinonasal malignancies (SNM) are rare cancers associated with poor prognosis and impaired quality of life (QOL). The 22-question Sino-Nasal Outcome Test (SNOT-22) has been used to assess QOL in patients with SNM. Objective: To determine the minimal clinically important difference (MCID) for the SNOT-22 in patients with SNM. Methods: About 264 patients with SNM were drawn from a prospective, longitudinal, multi-institutional study. SNOT-22 scores were collected at pre-treatment baseline and follow-up through 5 years post-treatment. Cronbach’s alpha was used to assess the internal consistency of the SNOT-22. Distribution-based approaches were used to estimate the MCID. Multivariable regression identified factors associated with SNOT-22 improvement exceeding the determined MCID. Results: Cronbach’s alpha was >.85. The estimated MCIDs were: 1.44 (Cohen’s effect size), 1.32 (standard error of the mean), 10.45 (1/2 of baseline standard deviation), and 3.67 (minimum detectable change). The mean MCID across methods was 4.22. Multivariable regression demonstrated higher odds of clinically meaningful improvement from baseline in patients with early-stage disease (odds ratio (OR) 3.510, P = .035), no neck irradiation (OR 11.050, P = .014), and neoadjuvant therapy (OR 16.667, P = .047). Conclusions: The SNOT-22 MCID was estimated as 4.22 in a large multi-center cohort of patients with SNM, suggesting that relatively small QOL changes are clinically relevant. When applying the MCID to the current cohort, advanced stage, treatment of neck disease, and not requiring neoadjuvant therapy were predictive of worse QOL.

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.

PURPOSE Cancer recurrence is a critical outcome for patients and physicians. Retrospective cancer recurrence data can evaluate recurrence-directed treatment and generate novel interventions targeting recurrent cancers. However, large cancer databases do not provide recurrence-related information, stymying study at scale, and consequently require significant manual record review. Automated evaluation of records may allow for the rapid generation of easily analyzed data sets, accelerating the evaluation of recurrence altogether. METHODS Patients treated with radiation therapy at one tertiary referral center from 2010 to 2018 with a verified cancer status (cancer recurrence v no cancer recurrence) were identified. Patients with recurrent disease were initially identified through manual record review, and the associated pathology report was collected. Google Automated Machine Learning with Natural Language Processing (AutoNLP) and Google Gemini 1.5 Pro were used to generate a model for binary classification, with comparison to the gold-standard manually developed data set. RESULTS A total of 7,054 patients were identified. 3,431 (48.6%) were female, with a median age of 64 years. Head and neck (1,482, 21%), breast (1,480, 21%), upper GI (1,307, 18.5%), and lung/thorax (1,107, 15.7%) were the most common disease sites. Recurrence was verified for 1,546 patients (21.9%) using pathology reports, of which 1,249 positive cases were paired with 651 negative pathology reports for model development. Google Gemini 1.5 Pro consistently outperformed AutoNLP across all measurements of accuracy, generating a greater absolute difference in precision, recall, negative predictive value, and specificity, and a higher likelihood of correct classification at the individual level, rendering Gemini superior in recurrence status extraction. CONCLUSION AutoNLP and Google Gemini 1.5 Pro are promising tools for identifying recurrence from pathology reports, with the latter demonstrating superior overall performance, making it particularly suitable for clinical translation.

PURPOSE Gastroesophageal carcinoma (GEC) is increasingly managed using biomarker-guided therapies. Although both tissue DNA (tDNA) and circulating tumor DNA (ctDNA) testing are used clinically, the concordance of genomic alterations between primary tumors, metastatic sites, and liquid biopsy remains incompletely understood. METHODS We retrospectively analyzed 345 patients with GEC who underwent ctDNA testing at Mayo Clinic. tDNA next-generation sequencing and immunohistochemistry (IHC) for human epidermal growth factor receptor 2 (HER2)/erb-b2 receptor tyrosine kinase 2 ( ERBB2 ) and mismatch repair (MMR) were collected. Concordance was evaluated between ctDNA and tDNA, ctDNA and IHC, and across all three modalities, using 60-day and 30-day intervals. RESULTS Among 345 patients with ctDNA testing, 186 had tDNA testing, and 310 underwent IHC. ctDNA testing showed a shorter turnaround time than tDNA (median 6 v 13 days). The most frequent alteration in ctDNA, primary tDNA, and metastatic tDNA was TP53. Concordance between ctDNA and primary tDNA was ≥78.8% for key genes; between ctDNA and metastatic tDNA, it was ≥84.8%. IHC-ctDNA concordance was higher in metastatic tumors, with 91.1% for HER2/ ERBB2 and 97.7% for mismatch repair (MMR)/microsatellite instability (MSI) compared with 81.1% and 95.8%, respectively, in primary tumors. Triplet concordance for MSI/MMR was 97.6% in primary tumors and 100% in metastatic tumors, while ERBB2 /HER2 showed 84.4% and 93.9% concordance, respectively. ctDNA temporal heterogeneity analysis revealed 14 patients with fluctuating ERBB2 status and 3 with fluctuating MSI-H status. CONCLUSION ctDNA testing demonstrates high concordance with tDNA and IHC and offers faster turnaround. Given its minimally invasive nature and strong concordance with tissue-based testing, ctDNA is a reliable tool for genomic profiling and longitudinal monitoring in GEC.

PURPOSE The primary objective of this randomized, double-blind, placebo-controlled, multicenter phase II study (Alliance A221805) was to screen two doses of duloxetine for preventing sensory oxaliplatin-induced peripheral neuropathy (OIPN). METHODS Participants were randomly assigned 1:1:1 to receive once daily 30 mg duloxetine, 60 mg duloxetine, or placebo. Eligible participants had stage II to III colorectal cancer and no baseline neuropathy, had Eastern Cooperative Oncology Group performance status 0-2, were age 25 years and older, and received oxaliplatin via one of the following doses and schedules: 85 mg/m 2 every 2 weeks (6 or 12 doses) or 130 mg/m 2 every 3 weeks (4 doses). Duloxetine/placebo was taken once daily beginning day 1 of cycle 1 and continued for 17 weeks. The primary end point, a composite response reflecting sensory OIPN symptom severity and onset, was measured in weeks 19-21 using a validated participant-reported outcome survey assessing extremity numbness, tingling, and pain. Response was defined as a participant-reported highest score of ≤2 (ie, 1 = not at all; 2 = a little) on survey items. To be evaluable for the response end point, eligible participants must have initiated oxaliplatin and submitted ≥1 postbaseline OIPN survey. RESULTS Of the 199 participants (n = 66, 30 mg duloxetine; n = 66, 60 mg duloxetine; n = 67, placebo), 46, 47, and 50 (N = 143, 71.8%), respectively, were evaluable for primary end point analysis based on modified intention-to-treat criteria. Participant mean age was 55.1 years (standard deviation = 10.4). Most were White (n = 113, 80.7%) and male (n = 82, 58.6%). The proportion of responders among those receiving placebo (68.0%) was similar to those receiving duloxetine 30 mg (65.2%) or 60 mg (66.0%). Duloxetine adherence rates, measured via pill counts—30 mg (54%), 60 mg (57%), and placebo (59%) groups—were low (<75%). CONCLUSION Duloxetine is not more promising than placebo for preventing sensory OIPN.

Abstract Purpose: This multicenter, single-arm, phase II trial evaluated the safety and efficacy of cabazitaxel and carboplatin followed by abiraterone, plus androgen deprivation therapy (ADT), in patients with high-volume metastatic castration-sensitive prostate cancer (mCSPC). Patients and Methods: Eligible patients had high-volume mCSPC and ≤3 months of prior ADT. Patients received six 21-day cycles of cabazitaxel (20 mg/m2) and carboplatin (AUC 4) with continuous ADT, followed by maintenance abiraterone (1,000 mg daily) and prednisone (5 mg daily). The primary endpoint was the proportion of patients free of prostate-specific antigen (PSA) or radiographic progression at 12 months. Secondary endpoints included complete PSA response (≤0.2 ng/mL), objective response, progression-free survival (PFS), and safety. Overall survival (OS) was also reported. Results: Sixty-one participants were enrolled at eight sites. The median age was 64 years, median baseline PSA was 6.6 ng/mL (0.07–745.7 ng/mL), 73.3% had Gleason grade group 5, 69.5% had 10 or more metastases, and 19.6% had a homologous recombination repair (HRR) mutation. PSA PFS at 12 months was 84.6% [95% confidence interval (CI), 72.5%–91.7%], and OS at 12 months was 94.8% (84.7%–98.3%). Complete PSA response was 66.7%, and complete objective response was 30.7%. Contrary to our hypothesis, relative to HRR wild-type patients, HRR-mutated patients (19.6%) had numerically fewer complete PSA responses (33.3% vs. 70.3%) and inferior PFS (HR, 2.43; 95% CI, 0.93–6.39). Common side effects of this quadruplet regimen included fatigue, nausea, and diarrhea. Conclusions: Cabazitaxel and carboplatin followed by abiraterone, together with ADT, were feasible, safe, and efficacious in patients with high-volume mCSPC and warrant further study in larger randomized trials.

Abstract Purpose: Standard care for high-grade gliomas (HGG) involves maximal surgical resection followed by radiation and temozolomide. Postoperative adjuvant therapy frequently causes lymphopenia, which is associated with poor prognosis. Interleukin 7 (IL7) is essential for lymphocyte development, homeostasis, and survival. NT-I7 (efineptakin alfa), a long-acting recombinant IL7, reverses lymphopenia and improves survival in murine glioma models. However, the safety, maximum tolerated dose (MTD), and impact of NT-I7 on immune cells in patients with HGG remain unknown. Patients and Methods: We conducted a phase I trial (NCT03687957) examining the MTD and effect of NT-I7 on lymphocytes in patients with newly diagnosed HGG. The primary endpoint was dose-limiting toxicity; secondary endpoints included absolute lymphocyte count (ALC) changes over time, overall response, progression-free survival, and overall survival. Exploratory endpoints included immune profiling at different time points using single-cell RNA sequencing (scRNA-seq) in a subset of patients. Results: NT-I7 was well tolerated with a MTD of 720 µg/kg. Moreover, NT-I7 significantly increased ALCs for more than 12 weeks in duration. Early elevations in CD4+, CD8+ T cells and NK cells further coincided with increased TNF and CXCL9 cytokine levels. Comprehensive immune profiling of peripheral blood T cells revealed selective clonotype expansion within CD8+, but not CD4+, T cells following NT-I7 administration. Finally, a subset of our patients with MGMT promoter–unmethylated glioblastoma, which are typically associated with a poorer prognosis, demonstrated promising clinical responses. Conclusions: NT-I7 has the potential to maintain and increase lymphocyte counts in patients with HGG and warrants further investigation, particularly in combination with immune-based therapies.

Abstract Background Current risk assessment tools for guiding direct oral anticoagulant (DOAC) therapy for patients with atrial fibrillation (AF) based on clinical risk factors demonstrate modest predictive performance limiting clinical impact. Additionally, while guidelines recommend periodic reassessment of risk over time, there remains an absence of modelling solutions for capturing evolving risk in AF patients. Methods Using UK electronic health records, we developed and validated the Transformer-based Risk assessment survival model (TRisk), an artificial intelligence model that predicts 12-month thromboembolic and bleeding events in AF patients by leveraging temporal patient journeys up to baseline. A cohort of 411,850 prevalent non-valvular AF patients aged ≥18 years between 2010 and 2020 was identified from 1,442 English general practices. Practices were randomly allocated to derivation (n=1,079) and external validation (n=363) cohorts. TRisk was compared with CHA 2 DS 2 -VASc and CHA 2 DS 2 -VA for thromboembolic event prediction, and HAS-BLED and ORBIT for bleeding prediction, with subgroup analyses by sex, age, and baseline characteristics. A second validation of TRisk was also performed on 16,218 US AF patients between 2010 and 2023. A decision model compared outcomes and healthcare costs for TRisk versus standard care. Findings TRisk achieved higher discrimination for thromboembolic event prediction (C-index: 0.82; 95% confidence interval [CI]: [0.81, 0.83]) as compared to CHA 2 DS 2 -VASc (0.71 [0.70, 0.73]) in UK validation. Application of TRisk to US data yielded similar C-index: 0.82 (0.80, 0.84). For bleeding prediction, TRisk (C-index: 0.70 [0.69–0.71]) outperformed both HAS-BLED (0.63; [0.61, 0.64]) and ORBIT (0.64; [0.63, 0.65]), with comparable US results (0.71; [0.69, 0.74]). The model remained well-calibrated across both populations and performed equitably across subgroups, including by race and during the COVID-19 pandemic. Impact analyses showed TRisk could reduce DOAC prescriptions by 8% in the UK and 7% in the US relative to guideline-recommended approaches, while preventing at least as many thromboembolic events. This refined approach would generate annual healthcare savings of £5.5 million and $456.2 million in the UK and US respectively among patients initiating DOACs, rising to £48.6 million and $1.8 billion when extended to all AF patients on DOACs. Interpretation TRisk enabled more precise prediction for both thromboembolic and bleeding events across AF populations in UK and US compared to established clinical scoring systems. Incorporating TRisk into routine AF care would result in substantial cost savings without compromising the identification of true high-risk patients. Funding None

Importance Left atrial appendage occlusion (LAAO) is used in patients with contraindications to oral anticoagulation, who are at increased risk for major bleeding (MB). Objective To evaluate the incidence, predictors, management, and clinical outcomes of MB after LAAO in the Amulet IDE trial. Design, Setting, and Participants The randomized clinical Amulet IDE trial enrolled patients from 2016 through 2019 with 5-year follow-up. Procedures were performed at specialized LAAO centers. Of 1878 randomized patients (1:1 Amulet vs Watchman), 1833 underwent attempted LAAO implantation. These data were analyzed from May 2025 through November 2025. Main Outcomes and Measures Pooled (Amulet and Watchman) patients with vs without MB (Bleeding Academic Research Consortium [BARC] score ≥3: overt bleeding with transfusion and ≥3 g/dL hemoglobin drop) were compared. Results This study included a total of 1833 patients (57.7% male and 42.3% female; mean [SD] age, 75.8 [7.5] years). MB occurred in 331 patients (18.1%) over 5 years (annualized rate 5.9% per year). Patients with MB were older (76.8 vs 74.7 years) and had higher CHA 2 DS 2 -VASc (4.8 vs 4.6) and HAS-BLED (3.4 vs 3.2) scores, as well as greater prevalence of diabetes (38.7% vs 34.1%), prior MB (45.3% vs 28.0%), and kidney disease (9.4% vs 4.5%). MB risk was highest in the first 6 months after LAAO (20.5% per year), then decreased to 3.9% per year through 5 years. Most MB events were nonprocedural (88.8%) with gastrointestinal bleeding accounting for 252 of 438 events. Independent predictors of MB included increasing age (hazard ratio [HR], 1.04; 95% CI, 1.02-1.06), female sex (HR, 1.25; 95% CI, 1.01-1.56), diabetes (HR, 1.26; 95% CI, 1.01-1.58), prior MB (HR, 1.93; 95% CI, 1.55-2.40), and kidney disease (HR, 2.15; 95% CI, 1.48, 3.12). At first MB event, 47.1% of patients were not receiving antithrombotic therapy and 92 patients had recurrent events. Patients with MB had significantly higher rates of the composite of stroke, systemic embolism, or cardiovascular death (32.5% vs 18.0%; P &amp;lt; .001), driven by higher stroke (14.7% vs 6.6%) and cardiovascular death (24.4% vs 12.9%) rates. All-cause mortality was also higher (49.2% vs 25.4%; P &amp;lt; .001). MB was fatal (BARC score of 5) in 25 patients (1.4%). Conclusions and Relevance In this study, MB was common in this high-risk population and largely related to underlying patient factors. Early MB risk was highest 6 months post-LAAO, then dropped considerably. Most events were gastrointestinal and occurred even in patients on minimal or no antithrombotic therapy. MB after LAAO was strongly associated with increased stroke and mortality. Trial Registration ClinicalTrials.gov Identifier: NCT02879448