Liu, Mei

Abstract Background Neuroendocrine tumors (NETs) are rare, heterogeneous neoplasms associated with prolonged survival and substantial symptom burden. However, patient-reported outcomes across NET subtypes remain poorly characterized, particularly in real-world settings. This study describes baseline health-related quality of life (HRQoL) and care experiences among patients with gastroenteropancreatic and lung NETs, examining differences by tumor site and time since diagnosis. Methods The Neuroendocrine Tumors–Patient Reported Outcomes study is a prospective, multi-institutional US cohort of adults (aged 18 years and older) with incident small intestinal, pancreatic, gastroenteropancreatic, or lung NETs diagnosed from January 2018 through September 2024, identified via a validated electronic medical record–based computable phenotype. Baseline surveys assessed HRQoL, symptoms, care experiences, and clinical characteristics using validated instruments. Descriptive statistics and standardized mean differences compared responses by NET site and time since diagnosis. Results Among 2367 participants (mean age = 57.8 years; 57.3% female), 1974 had gastroenteropancreatic NETs (659 small intestinal NET, 555 pancreatic NET) and 393 had lung NETs. Fatigue (mean = 33.0), insomnia (mean = 32.5), and diarrhea (mean = 25.7) were the most burdensome symptoms. Lung NET patients reported worse dyspnea (standardized mean difference = 0.58, P < .001) and lower physical, role, and global QoL scores than those with gastroenteropancreatic NETs, while pancreatic NET patients reported better functioning. Diarrhea worsened over time, especially in small intestinal NETs. Most rated care highly (75.3%) but cited concerns about treatment side effects (80.4%), costs (60.7%), and travel burden (58.8%). Conclusions This large US cohort reveals persistent symptom burden and HRQoL variation by tumor site and disease duration, underscoring the need for longitudinal HRQoL assessment in NET care.

Abstract Introduction: Recruitment for rare disease studies is challenging due to small eligible populations. Traditional clinical research management systems often lack tools to track recruitment contacts prior to enrollment. The NET-PRO study, focused on neuroendocrine tumors (NETs), implemented a participation monitoring system to enhance recruitment efficiency and representativeness. Methods: NET-PRO is a multicenter cohort study of 2538 adults diagnosed with gastroenteropancreatic (GEP) or lung NETs between January 2018 and September 2024. Recruitment occurred from January 2022 to February 2025 across 14 U.S. medical centers. Sites used flexible recruitment methods (email, mail, phone, in-clinic) and tracked contacts using REDCap-based tools. Participant characteristics were analyzed by enrollment mode (online or mail) and recruitment difficulty (number of contacts required prior to enrollment) using standardized mean differences, chi-square tests, and ANOVA. Results: Of 9279 contacted patients, 2675 consented (28.8%) and 2538 enrolled (27.4%). Most enrolled online (83.2%), while 16.8% enrolled by mail. Mail respondents were older, had lower education and income, and more comorbidities. Among those enrolled, recruitment difficulty was associated with older age, lower education and income, but not comorbidity. Over half of the most difficult-to-recruit participants enrolled online. Contact methods varied by attempt, with email dominating early contacts and phone/mail used more in later attempts. Conclusions: A participation monitoring tool supported flexible, multimodal recruitment and improved sample representativeness in a rare cancer study. Tracking recruitment contacts enabled adaptive strategies and may reduce bias in observational research by enabling better outreach to harder-to-reach populations.

Abstract Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.