A new project at Purdue University that combines many types of available data in a “digital twin” model of bladder cancer may prove powerful enough to predict patient outcomes.

Cancer models developed with 3D bioprinting technology allow for rapid evaluation of individual drug responses.

A novel 3D bioprinted gastric cancer model using patient-derived tissues predicts drug responses, enhancing personalized ...

Beyond improving accuracy, xAI allowed researchers to compare prognostic markers across different cancer types, unveiling ...

A collaborative research team led by Professor Jinah Jang from the Department of Mechanical Engineering and the Department of ...

Using preoperative chest CT scans, researchers identified novel preoperative biomarkers for lung cancer recurrence ...

A collaborative research team from POSTECH has successfully developed a gastric cancer model using 3D bioprinting technology ...

A recent editorial explores the effectiveness of complex risk models for lung cancer screening, suggesting that simpler ...

AbdomenAtlas also serves as a benchmark that allows other research groups to evaluate the accuracy of their medical ...

An innovative machine learning model predicts the success of immune checkpoint inhibitors in cancer patients using routine blood tests and clinical data, outperforming traditional methods like tumor ...

AI-enabled ECG biological age (ECG-BA) improves disease risk classification beyond chronological age, enhancing early ...