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0The paper introduce Oncoformer, a cutting-edge multimodal transformer designed to enhance oncology through integrated data analysis. This artificial intelligence framework processes a variety of longitudinal clinical inputs, including vital signs, lab tests, and chest X-rays, to analyze several different malignancies. By mapping this diverse information into a unified latent space, the system can assist in current diagnoses and the estimation of future risks. Furthermore, the model demonstrates high performance in determining tumor stages and predicting how patients might respond to specific treatment interventions. Visualizations within the sources confirm the tool’s ability to track cancer trajectories and provide prognosis predictions across large patient cohorts. Ultimately, this resource represents a significant advancement in using routine medical data for more personalized and precise cancer care.
References:
Liu F, Wang K, Xu H, et al. Advancing cancer detection and treatment using longitudinal routine clinical data[J]. Cell, 2026.
