Development and Validation of an Ipsilateral Breast Tumor Recurrence Risk Estimation Tool Incorporating Real-World Data and Evidence From Meta-Analyses: A Retrospective Multicenter Cohort Study Data ...

An artificial intelligence (AI) deep learning tool that estimates the malignancy risk of lung nodules achieved high cancer detection rates while significantly reducing false-positive results. Results ...

Machine learning models using patient-reported outcomes, wearables, and clinical data accurately predicted urgent care visits ...

A new study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported outcomes and wearable sensor data can predict which patients ...

Machine learning models can help predict which patients receiving systemic therapy for non-small lung cancer are most likely ...

Every year, approximately 2 billion chest X-rays are performed around the world. They are affordable, quick, and often used in frontline healthcare to efficiently survey for abnormalities such as ...

Cancer diagnoses traditionally require invasive or labor-intensive procedures such as tissue biopsies. Researchers at the Ludwig-Maximilians-Universität München (LMU) have now reported on a method ...

Survival outcomes in non-small cell lung cancer: Real-world analysis of immunotherapy era vs pre-immunotherapy era, with insights into treatment settings, racial disparities, and socioeconomic impacts ...

Robotic-assisted bronchoscopy detected early-stage lung cancer three times more frequently than traditional CT-guided biopsy, potentially improving patient survival outcomes. The robotic approach ...

Explore how machine learning is revolutionising interstitial lung disease management, enhancing early diagnosis, treatment, ...