Lung cancer remains the leading cause of cancer related deaths worldwide. Lung cancer
screening using low-dose computed tomography (LDCT) has been shown to reduce lung …

Background Describe and evaluate the methodological conduct of prognostic prediction
models developed using machine learning methods in oncology. Methods We conducted a …

The goal of oncology is to provide the longest possible survival outcomes with the
therapeutics that are currently available without sacrificing patients' quality of life. In lung …

Objectives In biomedical research, spin is the overinterpretation of findings, and it is a
growing concern. To date, the presence of spin has not been evaluated in prognostic model …

Background Artificial intelligence approaches can integrate complex features and can be
used to predict a patient's risk of developing lung cancer, thereby decreasing the need for …

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical
simulation is developed, systematically integrated with mixed reality (MR), 5G cloud …

Aims This study aimed to develop a machine learning–based prediction model for
gestational diabetes mellitus (GDM) in early pregnancy in Chinese women. Materials and …

BACKGROUND Incidental durotomy is a common intraoperative complication during spine
surgery with potential implications for postoperative recovery, patient-reported outcomes …

In the past few years, Artificial Intelligence (AI) techniques have been applied to almost
every facet of oncology, from basic research to drug development and clinical care. In the …

PURPOSE Ability to predict symptom severity and progression across treatment trajectories
would allow clinicians to provide timely intervention and treatment planning. However, such …