Challenges in the maintenance of an open hospital-based cancer registry system in a low-to-middle-income country (LMIC): 2017-2022 experience.

Hospital-based cancer registries (HBCRs) record data on all patients diagnosed and/or treated for cancer at healthcare facilities and evaluate the burden of the disease and the quality of healthcare services at that hospital, helping improve patient care, and providing an assessment of healthcare quality. The CARE PH app was created as a tool to facilitate a system of hospital-based cancer registries in the Philippines, a lower middle-income country. From 2017 to 2022, a total of 60,021 cancer registrants from 44 CARE PH hospitals were entered into the database.

A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci.

Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the "missing heritability" problem.

Final Safety and Health-Related Quality of LIfe Results of the Phase 2/3 Act.In.Sarc Study With Preoperative NBTXR3 Plus Radiation Therapy Versus Radiation Therapy in Locally Advanced Soft-Tissue Sarcoma.

Purpose: Act.In.Sarc (NCT02379845) demonstrated that the first-in-class radioenhancer NBTXR3, activated by preoperative radiation therapy (RT), doubled the rate of pathologic complete response after resection compared with preoperative RT alone in adult patients with locally advanced soft tissue sarcoma of the extremity or trunk wall (16.

Artificial intelligence on COVID-19 pneumonia detection using chest xray images.

Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources.