Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.

Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.

Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.

Importance: Large language models (LLMs) can assist in various health care activities, but current evaluation approaches may not adequately identify the most useful application areas.

Objective: To summarize existing evaluations of LLMs in health care in terms of 5 components: (1) evaluation data type, (2) health care task, (3) natural language processing (NLP) and natural language understanding (NLU) tasks, (4) dimension of evaluation, and (5) medical specialty.

Data Sources: A systematic search of PubMed and Web of Science was performed for studies published between January 1, 2022, and February 19, 2024.

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms.

Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment.

Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera.

Impact of mental illness on end-of-life emergency department use in elderly patients with gastrointestinal malignancies.

Background: Elderly patients with gastrointestinal cancer and mental illness have significant comorbidities that can impact the quality of their care. We investigated the relationship between mental illness and frequent emergency department (ED) use in the last month of life, an indicator for poor end-of-life care quality, among elderly patients with gastrointestinal cancers.

Methods: We used SEER-Medicare data to identify decedents with gastrointestinal cancers who were diagnosed between 2004 and 2013 and were at least 66 years old at time of diagnosis (median age: 80 years, range: 66-117 years).

Longitudinal Analysis of Mental Disorder Burden Among Elderly Patients With Gastrointestinal Malignancies.

Background: Patients with cancer are at high risk for having mental disorders, resulting in widespread psychosocial screening efforts. However, there is a need for population-based and longitudinal studies of mental disorders among patients who have gastrointestinal cancer and particular among elderly patients.

Patients And Methods: We used the SEER-Medicare database to identify patients aged ≥65 years with colorectal, pancreatic, gastric, hepatic/biliary, esophageal, or anal cancer.

The clinical and financial cost of mental disorders among elderly patients with gastrointestinal malignancies.

The clinical and financial effects of mental disorders are largely unknown among gastrointestinal (GI) cancer patients. Using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database, we identified patients whose first cancer was a primary colorectal, pancreatic, gastric, hepatic/biliary, esophageal, or anal cancer as well as those with coexisting depression, anxiety, psychotic, or bipolar disorder. Survival, chemotherapy use, total healthcare expenditures, and patient out-of-pocket expenditures were estimated and compared based on the presence of a mental disorder.

Intensified systemic therapy and stereotactic ablative radiotherapy dose for patients with unresectable pancreatic adenocarcinoma.

Purpose: We aimed to report the long-term impact of modern chemotherapy and SABR dose regimens on oncologic outcomes of unresectable pancreatic adenocarcinoma (PA).

Materials And Methods: We reviewed the treatment characteristics and outcomes of all patients who received multi-fraction SABR for unresectable PA between February 2007 and August 2018 at our institution. Time-to-events were calculated from date of diagnosis treating death as a competing risk.

Predicting per-lesion local recurrence in locally advanced non-small cell lung cancer following definitive radiation therapy using pre- and mid-treatment metabolic tumor volume.

Background: We evaluated whether pre- and mid-treatment metabolic tumor volume (MTV) predicts per lesion local recurrence (LR) in patients treated with definitive radiation therapy (RT, dose≥60 Gy) for locally advanced non-small cell lung cancer (NSCLC).

Methods: We retrospectively reviewed records of patients with stage III NSCLC treated from 2006 to 2018 with pre- and mid-RT PET-CT. We measured the MTV of treated lesions on the pre-RT (MTV) and mid-RT (MTV) PET-CT.

Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

Objective: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network.

SIRT6 represses LINE1 retrotransposons by ribosylating KAP1 but this repression fails with stress and age.

L1 retrotransposons are an abundant class of transposable elements that pose a threat to genome stability and may have a role in age-related pathologies such as cancer. Recent evidence indicates that L1s become more active in somatic tissues during the course of ageing; however the mechanisms underlying this phenomenon remain unknown. Here we report that the longevity regulating protein, SIRT6, is a powerful repressor of L1 activity.