Creating an Artificial Intelligence System Using Multi-Modal Data to Assess Tumor Risk and Improve Diagnosis and Treatment of Lung, Stomach, and Colorectal Cancers

What this Trial Is About

Researchers aim to create a comprehensive medical database to improve the prediction of cancer risk, screening accuracy, and treatment outcomes. The study focuses on common tumors including lung cancer and pulmonary nodules, stomach cancer, and colorectal cancer. This research combines clinical diagnosis, treatment information, imaging, pathology, and multi-omics data to develop advanced artificial intelligence AI technologies for better diagnosis and personalized treatment predictions. The study gathers multiple types of data such as electronic medical records, pathological images, medical imaging like CT and MRI, and various molecular data including genome and proteome information. Using AI, researchers plan to analyze these diverse data sources through deep learning and bioinformatics methods. This approach will address challenges like unbalanced data and small sample sizes to create accurate diagnostic and treatment response models. Participants include those suspected of having lung, stomach, or colorectal cancers, as well as healthy individuals without these diagnoses. The study involves collecting detailed clinical and imaging data along with multi-omics information. Researchers will evaluate outcomes such as diagnosis accuracy for benign or malignant lesions and treatment responses over the study period from 2022 to 2026. Follow-up and detailed record-keeping will support ongoing assessments of cancer diagnosis and therapy effectiveness.

Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment