Citations

Abstract Honoring a patient’s wishes for end-of-life care can be challenging if the patient loses decision-making capacity and the identified alternate decision-maker will not respect the patient’s wishes. This article discusses how to proceed ethically and legally when the alternate decision-maker and care team disagree about respecting a patient’s end-of-life preferences.

Abstract Genetic counseling assistants (GCAs) support genetic counselors (GCs) and genetics clinic workflows, but their potential roles in pretest genetic counseling for rare diseases have not been explored. A pilot within the Mayo Clinic Center for Individualized Medicine's Genetic Testing and Counseling Clinic (GTAC), which offers predefined tests for patients with rare disease, explored the impact of GCA pretest education on appointment time and patient questions. After training, GCAs met GTAC patients prior to the GC to provide scripted information on genetics concepts, the visit purpose, and the test including result types and disclosure plans. Data from the pilot and a control group were collected and analyzed using descriptive statistics and two‐sided t ‐tests. Patient cohort characteristics did not differ between the two models. When a GCA provided pretest education, the GC spent an average of 11.2 minutes less with the patient during their session compared to visits completed solely by the GC ( p < 0.0001). Total appointment time was not impacted by GCA education. Questions asked to GCAs were often not within GCA scope to answer (72.7%), and some patients asked repetitive questions to both the GC and GCA (63.2%). Reduction in GC time per patient could lead to increased accessibility by allowing additional patients to be seen in a day. Similar models may support GCA professional development while allowing GCs to remove repetitive education from their genetic counseling sessions, leading to less burnout and/or increased job satisfaction. Impact on administrative workflows, access, revenue, patient satisfaction and outcomes and GC/GCA satisfaction and benefits can continue to be explored when trialing models incorporating GCAs in pretest education roles.

Abstract Background: Immune checkpoint inhibitors (ICIs) have improved survival in non-small-cell lung cancer (NSCLC), yet only a subset of patients benefits, and biomarkers like PD-L1 remain limited. Here, we introduce a deep learning-based pathomics framework that utilizes routine H&E-stained slides to predict therapeutic response and survival outcomes in ICI-treated metastatic NSCLC. Methods: The study included 797 ICI-treated NSCLC patients from MD Anderson, with external validation in 280 patients from Mayo Clinic, Gustave Roussy, and the Phase III ICI-naïve LUSC Lung-MAP S1400I trial receiving nivolumab with or without ipilimumab. Path-IO (Pathology-Driven Immunotherapy Optimization) comprised of four major steps: (1) pathologist-verified tissue classifier segmented WSIs into eight compartments—Background, Bronchi, Immune, Lung, Necrosis, Stroma, Tumor, and Vessel—and was validated on TCGA and CPTAC dataset; (2) a survival prediction module generating patient-level risk scores in the MD Anderson cohort and validated across external datasets; (3) integration of Path-IO risk scores with radiomics and clinical features to improve prognostic accuracy; and (4) biological interpretability analyses correlating Path-IO risk immune contexture from multiplex immunofluorescence and transcriptomic signatures from NanoString profiling. Results: Path-IO effectively stratified patients into high and low-risk groups with significant survival differences. In the MD Anderson cohort, it achieved HR = 2.11 (p &lt; 0.001) and HR = 2.51 (p &lt; 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p &lt; 0.001) and HR = 1.87 (p &lt; 0.001) for PFS, respectively. In the multicenter Phase III Lung-MAP S1400I trial, Path-IO achieved HR = 1.78 (p = 0.016) for OS and HR = 2.76 (p = 0.006) for PFS. In external validation, it predicted outcomes in the Gustave Roussy (OS = 1.97, p = 0.003; PFS = 1.51, p = 0.046) and Mayo Clinic (OS = 2.46, p = 0.007; PFS = 2.45, p = 0.027) cohorts. Path-IO outperformed PD-L1 and remained an independent predictor in multivariate analysis (p &lt; 0.001). It enabled data-driven frontline selection between anti-PD-1 monotherapy and chemo-immunotherapy, offering guidance toward more personalized treatment decisions. Integration with radiomics and clinical features further improved predictive accuracy (OS 0.63→0.75; PFS C-index 0.58→0.70), while high Path-IO risk scores correlated with immune-cold phenotypes identified by multiplex immunofluorescence and NanoString transcriptomics. Conclusions: Path-IO demonstrates that deep learning models rooted in histopathologic architecture can generate interpretable and biologically informed survival predictions in NSCLC treated with ICIs. By integrating pathology, radiology, and clinical data, Path-IO provides complementary predictive value beyond established biomarkers such as PD-L1. Citation Format: Rukhmini Bandyopadhyay, Linghzi Hong, Mihaela Aldea, Shenduo Li, Lodovica Zullo, Frank R. Rojas, Maliazurina B. Saad, Maricel C. Marin, Muhammad Waqas, Jiexin Zhang, Eman Showkatian, Claudio A. Arrechedera, Xiaoyu Han, Yuliya Kitsel, Sherif Ismail, Muhammad Aminu, Bo Zhu, Carol C. Wu, Brett W. Carter, Joe Y Chang, Zhongxing Liao, Maria R. Ghigna, Davide Soldato, Hai T. Tran, Xiuning Le, Tina Cascone, Bingnan Zhang, Haniel A. Araujo, Mehmet Altan, Simon Heeke, David Jaffray, Don L. Gibbons, Ara Vaporciyan, J Jack Lee, Neda Kalhor, Cara Haymaker, Ignacio Wistuba, John V. Heymach, Yanyan Lou, Natalie Vokes, Luisa M. Solis Soto, Jianjun Zhang, Jia Wu. Path-IO: A deep learning pathomics framework for personalized immunotherapy selection and outcome prediction in metastatic non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4003.

ABSTRACT Objectives The increasing volume of medical education research necessitates efficient, reliable, and scalable methods for conducting quality appraisals. The Medical Education Research Study Quality Instrument (MERSQI) is a widely used tool, although its manual scoring process remains resource‐intensive. This study evaluated how well large language models (LLMs) appraise medical education research using the MERSQI tool in comparison with human judges. Methods Three LLMs (GPT‐5, Claude Sonnet 4, and Gemini 2.5 Pro) assigned MERSQI domain scores to 1423 medical education research articles. The authors compared AI‐generated scores with human‐generated scores using intraclass correlation coefficients (ICCs) across the six MERSQI domains. They evaluated the agreement between AI‐ and human‐generated MERSQI composite scores using Bland–Altman plots. Results Domain‐level ICC values ranged from fair (0.24) to near perfect (0.81), with the lowest agreement observed in the ‘sampling,’ ‘validity evidence,’ and ‘data analysis’ domains. No single LLM consistently outperformed the others across all domains. Composite score agreement with human ratings was substantial and similar across LLMs (ICC range: 0.65–0.69). GPT‐5 produced slightly lower composite scores than humans, while Claude Sonnet 4 and Gemini 2.5 Pro produced higher scores, with Gemini showing the largest deviation. The Bland–Altman plots for Gemini 2.5 Pro suggested proportional bias, indicating its agreement with human scores varied across the range of study quality. Conclusions These LLMs demonstrated substantial agreement with human raters for MERSQI composite scores, but domain‐level agreement varied. Systematic differences in scoring patterns highlight the need for human oversight and additional calibrations before integrating LLMs into systematic review appraisal workflows.

ABSTRACT Objective To identify and compare how patient perceptions contribute to Type 1 Delays, defined as delays in deciding to seek care, among individuals with head and neck cancer (HNC) in high‐income countries (HICs) and low‐ and middle‐income countries (LMICs). Methods We conducted a scoping review and systematic search of MEDLINE, Embase, Cochrane Library, and Scopus from inception through November 20, 2023. Eligible studies included patients with HNC in HICs or LMICs who reported patient perceptions contributing to Type 1 Delays. Data extraction was performed in duplicate, and barriers were categorized thematically. Fisher's exact test was used to compare the frequency of reported barriers between HIC and LMIC studies. Results Seventeen studies met inclusion criteria, of which 64.7% ( n = 11) were conducted in HICs and 35.3% ( n = 6) in LMICs, representing 9 countries and 1410 participants. The most frequently cited perceptions contributing to delay were symptom unawareness, misinterpretation, and minimization. Additional barriers included fear of diagnosis, stigma, alternative medicine use, financial challenges, and geographic barriers. Transport/geographic barriers showed a trend toward being more common in LMIC studies ( p = 0.05). Descriptive patterns suggested LMIC patients more often reported structural and sociocultural barriers, whereas HIC patients more commonly cited psychosocial or perceptual barriers. Conclusion Type 1 Delays in HNC are common across both HICs and LMICs, with overlapping and region‐specific contributors. Tailored interventions, such as patient education, stigma reduction, and improved access to diagnostic services, may promote earlier detection and improve global HNC outcomes. Level of Evidence 3.

Inverted sinonasal papilloma (ISP) and respiratory epithelial adenomatoid hamartoma (REAH) are sinonasal lesions with overlapping clinical, radiographic, and histologic features, making accurate diagnosis challenging. ISP is a benign but aggressive tumor with a propensity for local destruction, recurrence, and malignant transformation, whereas REAH is a hamartomatous lesion often arising in the olfactory cleft. We present four patients with lesions initially suspected to be ISP based on intraoperative frozen section but ultimately diagnosed as REAH on permanent pathology. All patients had polypoid sinonasal masses, frequently involving or adjacent to the olfactory cleft. Intraoperative frozen sections were often inconclusive or favored ISP, reflecting the difficulty of distinguishing these entities. Histologic analysis revealed that epithelial thickness served as a key morphologic feature differentiating ISP from REAH, with REAH exhibiting ciliated glandular proliferation and ISP characterized by hyperplastic epithelium with features of columnar and squamous differentiation. These patients underscore the diagnostic pitfalls of small biopsies and frozen sections, and highlight the importance of permanent specimens and collaboration with experienced pathologists. Accurate differentiation is critical, as management strategies differ substantially: ISP requires aggressive resection and long-term surveillance, while REAH may be treated with limited excision and minimal follow-up.

PURPOSE We sought to characterize outcome associations of poly ADP-ribose polymerase inhibitors (PARPi) in patients with ovarian cancer (OVCA) whose cancers contain homozygous loss (deletions) of homologous recombination repair (HRR) pathway genes, an alteration class not uniformly reported on clinical testing and not susceptible to acquired resistance via secondary reversion mutations. METHODS This study used US-based deidentified Flatiron Health-Foundation Medicine OVCA Clinico-Genomic Database. Patients with advanced OVCA and genomic profiling by Foundation Medicine were assembled into cohorts evaluating PARPi monotherapy use, and separately, PARPi maintenance therapy use, with outcomes associated with HRR gene alterations: BRCA1 / 2 , PALB2 , RAD51B/C/D , BARD1 , and BRIP1 . RESULTS 1.5% (82/5,404) had HRR gene homozygous loss, with BRCA1 and BRCA2 loss being most common. Among all OVCAs with BRCA1/2 alterations, 7.5% (63/836) were homozygous losses. Most BRCA1 (34/38, 89%) and BRCA2 (18/25, 72%) homozygous losses were intragenic, with breakpoints not spanning entire gene. Maintenance cohort: 22 patients had HRR gene homozygous loss, with strongly improved progression-free survival (PFS) for PARPi versus no PARPi use (HR, 0.06, 95% CI, 0.01 to 0.37). Monotherapy cohort: patients without HRR alterations (n = 198), HRR mutations or rearrangements (n = 93), and HRR homozygous loss (n = 8) had median PFS of 4.9, 10.6, and 25.4 months, and median overall survival of 18.5, 22.5, and 51.8 months, respectively. CONCLUSION We observe durable PARPi effectiveness for patients with OVCA and HRR gene homozygous loss in routine practice. We demonstrate the importance of detecting and reporting homozygous loss in HRR genes, an aspect not standard to all sequencing assays.

Abstract Objectives To examine the extent to which heat-related causes of death are recorded in fatal drug overdoses, how these patterns vary across states and over time, and how overdose characteristics differ between deaths with, versus without, heat involvement recorded. Methods Death certificate data for all drug overdose deaths in US residents from 2001 to 2024 (from the National Center for Health Statistics) were analyzed to identify whether a heat-related cause of death was also listed on the death certificate. Joinpoint regression, descriptive statistics, and nonparametric tests were used to examine temporal trends and compare overdose deaths with versus without recorded heat involvement. Results In 2001, fewer than 10 drug overdose deaths with recorded heat involvement were identified, but this number increased to 558 in 2024. From 2013 to 2024, mortality rates increased significantly, with an estimated annual percent change of 30.1 (95% Confidence Interval, 26.5–47.1). The highest mortality rates and numbers of deaths were observed in residents of Arizona and Nevada. American Indian/Alaska Native, Mexican-heritage, and foreign-born populations accounted for larger shares of overdose deaths with, compared to without, heat involvement recorded. A street or highway was more frequently identified as the place of injury in overdose deaths with (18.9%), versus without (2.2%) heat involvement reported. Psychostimulants such as methamphetamine were involved in 85.9% of overdose deaths with, compared to 28.9% without, recorded heat involvement. Conclusions Although representing only a fraction of all overdose deaths, fatal overdoses involving heat exposure have increased markedly over time and disproportionately impact certain states and demographic groups.

Background: Although donor and recipient characteristics are used to estimate graft failure risk, they remain limited in predicting early acute rejection. We developed and validated a next-generation sequencing (NGS) assay targeting the pre-transplant immunologic profile to predict early acute rejection following kidney transplantation. Methods: This prospective, international study enrolled 321 kidney transplant participants across 13 sites, forming discovery and validation cohorts. Peripheral blood was collected prior to transplant and at 1, 3, 6, 12, and 24 months post-transplant, with protocol biopsies performed at 3 and 12 months, as well as any indication biopsies. Biopsies were assessed by a blinded central pathologist according to the 2019 Banff criteria. The pre-transplant risk assessment (PTRA™) test to evaluate RNA expression of a 29-gene signature algorithm designed to predict early acute rejection risk was clinically validated utilizing 122 deceased donor kidney transplant recipients. Results: PTRA classified 31 of 122 participants (25%) as high-risk and 91 (75%) as low-risk. There were 9 early acute rejection events in the first 60 days post-transplant: 6/31 (19%) in the high-risk group and 3/91 (3%) in the low-risk group. PTRA discrimination for early acute rejection at 60 days post-transplant yielded an AUC of 0.78 (95% confidence interval [CI]: 64.4 to 91.5), p<0.001. A cut-off of ≤45 was defined as low-risk and >45 as high-risk for early acute rejection within 60 days post-transplant. Sensitivity 0.67 (95% CI: 0.36 to 0.97), specificity 0.78 (95% CI: 0.70 to 0.86), positive predictive value 0.19 (95% CI: 0.05 to 0.33), and negative predictive value 0.97 (95% CI: 0.93 to 1.00). The odds ratio comparing patients identified as high- vs low-risk by PTRA was 7.04 (95% CI: 1.64 to 30.2), p=0.009. Conclusions: This study validates the ability of PTRA to stratify kidney transplant recipients as high- or low-risk for early acute rejection using a pre-transplant transcriptomic profile, with implications for graft health and personalized treatment management.