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PURPOSE Older patients with ALL receiving conventional chemotherapy have poor survival due to toxic death and relapse. We hypothesized that a chemotherapy-free, targeted regimen using the anti-CD22 antibody-calicheamicin conjugate inotuzumab ozogamicin followed by the bispecific anti-CD19/CD3 T-cell engager blinatumomab would reduce toxic death and yield high rates of prolonged remission and survival. METHODS Eligible patients were age 60 years and older with untreated, Philadelphia chromosome (Ph)–negative, CD22-positive, B-cell ALL. Patients received up to two cycles of inotuzumab ozogamicin followed by four or five cycles of blinatumomab with intrathecal methotrexate CNS prophylaxis. The primary end point was 1-year event-free survival (EFS). RESULTS The 33 eligible patients had a median age of 71 years (range, 60-84) and a median CD22 expression of 92% (range, 21%-100%). Eight (24%) had previous chemotherapy or radiation for other cancers, six for multiple myeloma. The composite complete remission rate was 85% after two cycles of inotuzumab ozogamicin and 97% by the end of two cycles of blinatumomab. At a median follow-up of 30 months, the 1-year EFS and overall survival were 75% (95% CI, 61 to 92) and 85% (95% CI, 73 to 98), respectively. EFS was shorter with lower CD22 expression or detectable measurable residual disease at any time point. CONCLUSION Inotuzumab ozogamicin then blinatumomab without maintenance chemotherapy in older patients with untreated, Ph-negative, CD22-positive, B-cell ALL yields a high remission rate and excellent EFS. Given the lack of standard, safe, and effective therapies in this population, the regimen should be considered a standard treatment option.

Background: It is crucial to evaluate liver fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). Digital pathology, an automated method for quantitative fibrosis measurement, provides valuable support to pathologists by providing refined continuous metrics and addressing inter-observer variability. Although non-invasive tests (NITs) have been validated as consistent with manual pathology, the relationship between digital pathology and NITs remains unexplored. Methods: This study included 99 biopsy-proven MASLD patients. Quantitative-fibrosis (Q-Fibrosis) used second-harmonic generation/two-photon excitation fluorescence microscopy (SHG/TPEF) to quantify fibrosis parameters (q-FPs). Correlations between eight NITs and q-FPs were analyzed. Results: Using manual pathology as standard, Q-Fibrosis exhibited excellent diagnostic performance in fibrosis stages assessment with area under the receiver operating characteristic curves (AUCs) ranging from 0.924 to 0.967. In addition, magnetic resonance elastography (MRE) achieved the highest diagnostic accuracy (AUC: 0.781–0.977) among the eight NITs. Furthermore, MRE-assessed liver stiffness measurement (MRE-LSM) showed the strongest correlation with q-FPs, particularly adjusted by string length, string width, and the number of short and thick strings within the portal region. Conclusions: Both MRE and digital pathology demonstrated excellent diagnostic accuracy. MRE-LSM was primarily determined by collagen extent, location and pattern, which provide a new perspective for understanding the relationship between the change in MRE and histological fibrosis reverse.

Abstract Background Physical activity is associated with lower colorectal cancer (CRC) risk, but its association with molecular subtypes defined by genetic and epigenetic alterations of the disease is unclear. Such information may enhance the understanding of the mechanisms related to the benefits of physical activity. Methods Pooled observational (cases: n = 5386; controls: n = 6798; studies n = 5) and genome-wide association data (cases: n = 8178; controls: n = 10 472; studies n = 5) were used. We used multivariable logistic regression models and Mendelian randomization to assess the association between physical activity and the risk of CRC subtypes defined by individual tumor markers (and marker combinations), namely microsatellite instability status, CpG island methylator phenotype status, and BRAF and KRAS mutations. We used case-only analysis to test for differences between molecular subtypes. We applied Bonferroni correction to account for multiple tests. Results In the pooled observational analysis, higher levels of physical activity were associated with lower CRC risk (Obs-per 1SD, odds ratio [OR] = 0.94, 95% confidence interval [CI] = 0.90 to 0.97), with an association that was stronger in males (Obs-per 1SD, OR = 0.91, 95% CI = 0.87 to 0.96) than in females (Obs-per 1SD, OR = 0.97, 95% CI = 0.91 to 1.03; Pinteraction = .04). Higher physical activity was associated with a lower risk of CRC across all molecular subtypes, especially in males. There was no difference in the associations by subtypes by pooled observational or Mendelian randomization analyses. The findings did not differ by study design, anatomical site, and early or late age onset of CRC. Conclusions Our findings suggest that physical activity is not differentially associated with the 4 major molecular subtypes involved in colorectal carcinogenesis, indicating that its benefits extend broadly across colorectal cancer pathogenesis.

ABSTRACT Background and Aims Clinically significant portal hypertension (CSPH; hepatic venous pressure gradient (HVPG) ≥ 10 mmHg) is a severe complication of chronic liver disease, with increased risk of variceal bleeding and liver decompensation (at HVPG ≥ 12 mmHg). This study evaluated the performance of multiparametric (mp)MRI for diagnosing CSPH and predicting liver decompensation, compared to ultrasound‐based shear wave elastography (SWE) and blood tests. Methods In this prospective single‐centre study (2018–2022), 59 patients (M 30, mean age 52.7 years) underwent mpMRI and HVPG measurement, with an average interval of 32 ± 31 days. The mpMRI protocol included 2D/3D MR elastography (MRE), T 1 /proprietary iron‐compensated T 1 (cT 1 )/T 1ρ mapping, gadoxetate‐enhanced dynamic contrast‐enhanced MRI (DCE‐MRI) of the liver and spleen, SWE and blood tests. Statistical analyses included Mann–Whitney U tests, ROC analysis, logistic regression and Cox proportional hazards models. Results CSPH was present in 13 patients (22%). Several MRI parameters showed high diagnostic performance for CSPH (AUC ≥ 0.8), with spleen stiffness‐2D MRE (AUC 0.88, 95% confidence interval (CI) 0.78–0.98) and liver uptake on DCE‐MRI (AUC 0.83, 95% CI 0.70–0.96) performing best, while SWE had AUCs of 0.63 (95% CI 0.45–0.81) for liver and 0.67 (95% CI 0.44–0.89) for spleen. Combining spleen stiffness‐2D MRE and liver uptake achieved an AUC of 0.93 (95% CI 0.86–1.00) for diagnosing CSPH. For predicting decompensation, spleen stiffness‐3D MRE and liver uptake rate had AUCs of 0.83 (95% CI 0.68–0.99) and 0.83 (95% CI 0.70–0.95), respectively, outperforming SWE. Conclusions Spleen stiffness measured with MRE combined with gadoxetate liver uptake (measured with DCE‐MRI) can diagnose CSPH and predict liver decompensation, with superior performance compared to SWE. Trial Registration ClinicalTrials.gov identifier: NCT03436550

Abstract Background Lung adenocarcinoma in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve lung adenocarcinoma risk prediction. Methods PRSs were developed using genome-wide association study summary statistics from East Asian (8002 cases; 20 782 controls) and European (2058 cases; 5575 controls) populations. Single-ancestry models included a PRS with 25 single nucleotide variants (PRS-25), PRS clumping and thresholding (CT), and LDpred2; multi-ancestry models included LDpred2 plus European PRS-128, PRS-CSx, and clumping and thresholding, super learning, empirical Bayes (CT-SLEB). Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium and externally validated in the Nanjing Lung Cancer Cohort. We assessed predictive accuracy via area under the curve operating procedure (AUC), with 10-year and (ages 30-80 years) absolute risks estimates. Results The best multi-ancestry PRS, using East Asian and European data via CT-SLEB, outperformed the best East Asian-only (LDpred2; AUC = 0.629, 95% confidence interval [CI] = 0.618 to 0.641), achieving an AUC of 0.640 (95% CI = 0.629 to 0.653) and odds ratio of 1.71 (95% CI = 1.61 to 1.82) per standard deviation increase. Nanjing Lung Cancer Cohort validation confirmed robust performance (AUC = 0.649, 95% CI = 0.623 to 0.676). The top 20% PRS group had a 3.92-fold higher lung adenocarcinoma risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year lung adenocarcinoma risk at age 50 years (0.42%) by age 41 years, 9 years earlier. Conclusions Multi-ancestry PRS approaches enhance lung adenocarcinoma risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

Abstract Error analysis is a critical step in evaluating and improving clinical concept extraction models, the most common clinical natural language processing (NLP) task. Unlike corpus annotation, which follows standardized protocols for creating gold-standard datasets, error analysis requires nuanced judgment grounded in both clinical expertise and NLP knowledge. This is especially important given the heterogeneity of clinical text, where variations in documentation style, note structure, and terminology can substantially influence model behavior. Despite its importance, there is currently no standardized, user-level framework to support systematic error analysis in clinical concept extraction task. In this study, we developed and validated MedError, a machine-assisted, human-in-the-loop framework designed to standardize and enhance error analysis for clinical concept extraction tasks. We collected and manually curated a corpus of 1,187 unique errors from a total of 4,237 notes across three different distinct hospitals. The error categories were defined using our previously validated error taxonomy and included 480 false negatives and 707 false positives across 25 error types and 48 clinical concept categories. We evaluated the performance of three proprietary and three open-source large language models (LLMs) in automatically classifying these errors into 26 and 15 predefined categories. We further developed a machine-assisted framework, MedError, which integrates best practices in error analysis, LLM-assisted classification and reasoning, and a user-friendly interface to enable more efficient, reproducible, and context-aware error analysis. The framework supports both single-site and federated multisite error analysis, facilitating the effective translation of clinical NLP systems into real-world settings.

Objective: To develop, implement, and evaluate a novel process used for residency application review that deemphasizes metrics known to create bias with the goal of interviewing and matching a more diverse resident cohort. Methods: Between 2020 and 2023, a novel standardized rubric and application review process were developed and implemented at the authors’ academic training program. The rubric deemphasized USMLE scores, honor society membership, and number of publications while utilizing an AI-driven pre-sort of applications, facilitating holistic review. This weighted applicant attributes identified in personal statements, life experiences, achievements in community, leadership roles, otolaryngology-specific impressions highlighted in letters of recommendation, and otolaryngology-specific research and publications similarly to academic metrics (clerkship grades and standardized test scores). Demographics of applicants interviewed and matched into our program were compared pre- and post-implementation using descriptive statistics. Results: Using a standardized rubric to review residency applications that deemphasizes traditional metrics, the authors’ otolaryngology residency program has interviewed, ranked, and matched more diverse candidates. As a result, from 2019 to 2022, the overall resident cohort has diversified from 4% historically marginalized residents to 24%, from 28% female residents to 44%, and from 33% of residents outside our geographic region to 66%. Conclusion: There is significant underrepresentation among otolaryngology residents. Selection based upon USMLE Step 1 scores, honor society membership, and number of publications have been shown to restrict diversity. Purposeful holistic review increases diversity in interviewed and matched resident cohort.

Abstract Aims Our aim was to assess the clinical and prognostic significance of the left ventricular (LV) hypercontractile phenotype (HP) in hypertrophic cardiomyopathy (HCM), which until now remains unclear. Methods and results We enrolled 1533 HCM patients (age 51 ± 15 years, 965 males, 63%) with ejection fraction (EF) ≥ 50%, referred for rest transthoracic echocardiography (TTE) in 27 laboratories from 13 countries. Two-dimensional volumetric TTE assessment included LV outflow tract gradient (LVOTG), LV EF, and LV force (systolic blood pressure + LVOTG/ESV, mmHg/mL). HP was defined as the highest quartile of the force (> 7.32 mmHg/mL). Survival analysis was performed in a subset of 1200 patients with follow-up information. Compared with non-HP patients, HP showed higher heart rate (72 ± 14 vs. 67 ± 14 beats per minute, P < 0.001), lower stroke volume (50 ± 22 vs. 67 ± 22 mL, P < 0.001), larger left atrial volume index (44 ± 16 vs. 40 ± 16 mL/m2, P = 0.011) and higher coronary flow velocity in the mid-distal left anterior descending artery (n = 325, 41 ± 12 vs. 37 ± 14 cm/s, P = 0.021). Force was moderately related to LV EF (r = 0.48, P < 0.001) and weakly to LVOTG (r = 0.36, P < 0.001). During a median follow-up of 87 months (interquartile range 45–143 months), 131 all-cause deaths occurred. At multivariable Cox analysis, a force > 7.32 mmHg (the fourth quartile for analyzed patients) was associated with a hazard ratio of 1.44 (95% Confidence intervals 1.00–2.07) for all-cause death, independently of LVOTG and LV EF. Conclusion HP in HCM is associated with a disadvantageous systemic, cardiac and coronary hemodynamic profile as well as lower survival in the long-term.

ABSTRACT Background Prompt detection and intervention are crucial for improving outcomes in acute invasive fungal rhinosinusitis (AIFS). Diagnostic prediction models assist in risk‐stratification, but their accuracy requires testing through external validation. This study aims to validate a previously published diagnostic prediction model for AIFS in an independent cohort. Methods A retrospective chart review was conducted at a tertiary care center (2008–2023) to identify patients with an otolaryngology consult for suspected AIFS. Of 65 patients identified, 11 (16.9%) were diagnosed with AIFS based on histopathology. Risk was calculated using Yin et al.’s predictive model. Predictive performance was assessed by calibration and discrimination. Results Patients had significantly higher rates of diabetes (46.2% vs. 26.1%, p = 0.002), long‐term steroid use (60% vs. 28.2%, p < 0.0001), and solid organ transplantation (38.5% vs. 8.5%, p < 0.001), compared with the development cohort, with conversely lower rates of hematologic malignancy (29.2% vs. 58.7, p < 0.001) and neutropenia (19.4% vs. 41%, p = 0.001). Despite these differences, both the three‐variable (C‐index: 0.844; 95% CI, 0.736–0.952) and four‐variable models (C‐index: 0.963; 95% CI, 0.919–1) showed adequate discrimination. Both models exhibited slight overprediction of risk, with a calibration‐in‐the‐large predicted risk of 24.1% (95% CI, 13.68–34.46) for the three‐variable model and 24.2% (95% CI, 13.76–34.57) for the four‐variable model. Calibration plots confirmed overprediction. Conclusion The AIFS diagnostic model demonstrates acceptable discrimination and calibration on external validation, with generalizability to patients with different comorbidities. Larger studies are recommended to further test the model's predictive performance and clinical applicability.