Guannan Gong

Introduction: MET (c‐MET) is a receptor tyrosine kinase implicated in numerous cancers, including pancreatic neuroendocrine tumours (pNETs), by promoting cell proliferation, survival, invasion and angiogenesis. Recognizing its oncogenic potential, there is significant interest in MET‐targeted therapies for malignancies like pNETs, which often develop treatment resistance. Immunohistochemistry (IHC) has become a practical method for detecting MET overexpression in cancers. This study evaluates MET expression in pNETs by IHC and assesses its correlation with prognostic variables and survival outcomes. Methods and results: Tissue microarrays containing well‐differentiated neuroendocrine tumours from the gastrointestinal tract were analysed. The study included 125 pNET cores from 112 patients after application of inclusion criteria. MET expression was determined using the H‐score system. Different variables were assessed for H‐score distribution and cross‐tables. Survival analyses were conducted based on progression‐free survival and overall survival. Positive MET expression was found in 83.5% of cases. Higher MET H‐scores were seen in patients with lymphovascular invasion (LVI), distant metastases and higher tumour grade ( P < 0.05). When assessing different variables for higher MET H‐scores, a significant association emerged at the 150‐cut‐off‐point for LVI, perineural invasion, radiological evidence of progression and overall survival. For survival analysis, at a MET H‐score threshold of 200, high MET expression was significantly associated with shorter progression‐free survival (mean 8.7 versus 13.4 years, P < 0.05) and overall survival (mean 3.6 versus 7.7 years, P < 0.05). Conclusion: Elevated MET expression is linked to adverse histopathological features and worse clinical outcomes in pNET. Standardizing MET IHC evaluation is critical as anti‐MET therapies develop, and identifying patients likely to benefit from these treatments remains essential.

Background and Scope: Clinical trial design in oncology relies heavily on evidence synthesized from prior studies. However, relevant information, including Kaplan-Meier curves, baseline characteristics, and eligibility criteria, is typically embedded in figures, tables, and free text, making systematic extraction and quantitative synthesis challenging. We developed LEAD-ONC (Literature to Evidence for Analytics and Design in Oncology), a platform designed to transform published clinical trial reports into structured, analyzable data to support evidence-informed trial design. Solution: LEAD-ONC integrates large language models and computer vision techniques to extract multimodal information from published oncology trials, including survival curves, risk tables, and baseline characteristics. Individual patient data are reconstructed from digitized Kaplan-Meier curves, whereas baseline covariates are extracted and harmonized at the trial level across studies. The platform incorporates cross-trial similarity assessment to identify clinically comparable studies and applies Bayesian hierarchical survival modeling to generate predictive survival distributions for future trials. Evaluation: We demonstrate the utility of LEAD-ONC using a case study in metastatic non–small cell lung cancer. The platform successfully extracted and harmonized data from multiple phase III trials and generated predictive survival distributions for a target population defined by mixed histology. Model-based projections of median overall survival and treatment effect illustrate how the platform can support quantitative assumptions for trial design. Relevance: LEAD-ONC provides a scalable framework for systematic evidence synthesis from published literature. By enabling target population–specific survival projections, the platform supports more transparent and data-driven clinical trial design. How to Access/Use: LEAD-ONC is available at: https://lead-onc.org/step1 .

Background: Eligibility criteria are essential to clinical trial design, guiding recruitment, and ensuring patient safety and scientific rigor. However, criteria are often lengthy, heterogeneous, and inconsistently formatted, which hinders large-scale interpretation and slows patient-trial matching. Manual review is time-consuming and error-prone. Advances in natural language processing and large language models (LLMs) offer opportunities to standardize and analyze eligibility text at scale. Objective: This study aims to develop and evaluate a scalable system that uses LLM-enabled natural language processing and unsupervised learning to identify, normalize, categorize, and visualize clinical trial eligibility criteria, with the goal of improving patient-trial matching and revealing domain-level trends. Methods: We designed a three-part pipeline: (1) representation of eligibility text using embeddings, followed by clustering to group semantically similar criteria; (2) dual-layer zero-shot LLM summarization for concept normalization, refinement, and deduplication of cluster exemplars; and (3) an interactive, web-based visualization interface to explore criteria distributions and trends by disease domain and over time. The pipeline was applied to 53,872 oncology trials (breast, lung, and gastrointestinal cancer) indexed on ClinicalTrials.gov. Outputs include cluster labels, normalized criterion summaries, and per-domain frequency profiles. Feasibility was assessed via successful end-to-end processing and inspection of face validity for cluster coherence and domain-specific patterns. Results: The system successfully processed all 53,872 trials and generated stable clusters of inclusion and exclusion concepts. The LLM summarization layers produced concise, nonredundant labels that improved the interpretability of clustered criteria. The visualization interface enabled rapid exploration of cross-trial patterns and temporal trends within breast, lung, and gastrointestinal oncology, facilitating identification of common inclusion requirements and potential barriers to enrollment. A public, open-source demonstration instance allows for interactive exploration of these clusters and summaries. Benchmarking through human validation on a random sample of eligibility criteria found the system to be 94% (470/500) accurate, reflecting its ability to consistently categorize criteria correctly in congruence with human judgment. Conclusions: A combined embeddings-clustering-LLM pipeline can standardize heterogeneous eligibility text and surface domain-level patterns at scale. This framework provides a foundation for accelerating patient-trial matching and informing future trial design. While the current implementation was evaluated on ClinicalTrials.gov oncology trials, the approach is readily generalizable to additional diseases and alternative modeling configurations.

PURPOSE: To assess the generalizability of cancer clinical trials by comparing trial enrollment with baseline cancer populations across three National Cancer Institute (NCI)–designated cancer centers and to identify institutional barriers and best practices. METHODS: We conducted a retrospective analysis (2020-2024) of three NCI-designated Comprehensive Cancer Centers using Epic electronic health records and Clinical Trial Management Systems. Baseline cancer populations were compared with enrolled patients across solid tumors and hematologic malignancies by age, race/ethnicity, language, geography, and insurance status. We additionally evaluated the impact of an automated clinical trial prescreening tool implemented at Yale Cancer Center on enrollment demographics. RESULTS: Among 311,186 patients with solid tumor and 62,988 with hematology malignancies, non–English-speaking and Medicaid-insured populations were consistently underrepresented. Vanderbilt-Ingram Cancer Center enrolled the largest cohorts (12,053 solid tumor; 2,214 hematology) and demonstrated strong rural representation (23.2% enrolled v 17.8% baseline in solid tumors) but limited enrollment of non-English speakers (0.7% v 2.3% baseline in hematology). Mays Cancer Center at UT Health San Antonio achieved the highest racial/ethnic (68.5% v 57.5% baseline) and Medicaid-insured representation (28.3% v 10.4% baseline) in hematology despite smaller volumes. Yale Cancer Center maintained strong enrollment of older adults but underrepresented young adults; implementation of automated prescreening increased young adult consent (1.2%-5.2%) and minority participation (18.2%-23.8%) in solid tumors. CONCLUSION: Persistent enrollment disparities—particularly among non–English-speaking and Medicaid populations—were observed across institutions, alongside distinct institutional strengths. Technology-enabled interventions and cross-institutional knowledge sharing targeting language, socioeconomic, and geographic barriers are critical to improving equitable trial participation in precision oncology.

Background: Near real-time electronic health record (EHR) data offers significant potential for secondary use in research, operations, and clinical care, yet challenges remain in ensuring data quality and stability. While prior studies have assessed retrospective EHR datasets, few have systematically examined the integrity of real-time data for research readiness. Methods: We developed an automated benchmarking pipeline to evaluate the stability and completeness of real-time EHR data from the Yale New Haven Health clinical data warehouse, transformed into the OMOP common data model. Twenty-nine weekly snapshots of the EHR collected from July to November 2024 and twenty-two daily snapshots collected from April to May 2025 were analyzed. Benchmarks focused on (1) clinical actions such as patient additions, deletions, and merges; (2) changes in demographic variables (date of birth, gender, race, ethnicity); and (3) stability of discharge information (time and status). A synthetic dataset derived from MIMIC-III was used to validate the benchmarking code prior to large-scale analyses. Results: Benchmarking revealed frequent updates due to clinical actions and demographic corrections across consecutive snapshots. Demographic changes were most frequently related to race and ethnicity, highlighting potential workflow and data entry inconsistencies. Discharge time and status values demonstrated instability for several days post-encounter, typically reaching a stable state within 4–7 days. These findings indicate that while near real-time EHR data provide valuable insights, the timing of data stabilization is critical for accurate secondary use. Conclusions: This study demonstrates the feasibility of automated benchmarking to assess the integrity of real-time EHR data and identify when such data become analysis ready. Our findings highlight key challenges for secondary use of dynamic clinical data and provide an automated framework that can be applied across health systems to support high-quality research, surveillance, and clinical trial readiness.

PURPOSE: Cancer clinical trial enrollment remains critically low at 5%-7% of adult patients despite exponential growth in available trials. Manual patient-trial matching represents a fundamental bottleneck, whereas current artificial intelligence (AI) and machine learning patient-trial matching systems lack data standardization and compatibility across health systems. We developed and validated a semiautomated clinical trial patient matching (CTPM) tool to improve recruitment efficiency and scalability. METHODS: We created a hybrid rules–based and natural language processing (NLP)–based pipeline that automatically screens patients using structured and unstructured electronic health record data standardized to the Observational Medical Outcomes Partnership (OMOP) common data model. CTPM performance was first evaluated on one metastatic colorectal cancer (CRC) trial by comparing CTPM accuracy and efficiency to manual chart review. Following the single-trial validation, we then implemented the system across 29 clinical trials spanning multiple cancer specialties and phases. RESULTS: For the single CRC trial, CTPM achieved 94% retrospective and 88% prospective accuracy, matching gold standard clinical chart review with 100% sensitivity. Implementation reduced chart review workload 10-fold and screening time by 41% (3.1 to 1.8 minutes per chart) for those patients who did undergo review. Since September 2022, the system has screened 98,348 patients across 29 trials, identifying 825 eligible candidates and facilitating 117 patient enrollments with 9%-37% consent rates. CONCLUSION: This AI and NLP tool demonstrates improved efficiency in clinical trial recruitment by enabling research teams to focus on qualified candidates rather than exhaustive chart reviews. The OMOP-based framework supports scalability across health systems, with potential to address enrollment challenges that limit patient access to clinical trials.

Objective: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2. Design: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository. Setting: Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas. Populations: The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020. Main outcome and performance measures: Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support. Results: Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 2152 (99.9%) had a discharge disposition at the end of the study period. Of these, 329 (15.3%) required invasive mechanical ventilation and 305 (14.2%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 80.7 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14–1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50–2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups. Conclusions: This observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

Objective: Severe acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2. Design: This was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository. Setting: Yale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas. Populations: The study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020. Main outcome and performance measures: Primary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support. Results: Of the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 1633 (75.8%) had a discharge disposition at the end of the study period. Of these, 192 (11.8%) required invasive mechanical ventilation and 227 (13.5%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 81.9 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups. Conclusions: This observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.