Research on the Construction of an Early Cancer Warning AI Model Based on Genomic Data and Clinical Phenotypes

The objective of this study is to develop an artificial intelligence model for early cancer warning that integrates genomic data, clinical phenotypic characteristics, and other elements, yielding insights through comprehensive analysis. The aim is to significantly improve the detection rate and accuracy of early cancer symptom screening, address the challenge of early-stage malignancy's concealment, enable identification and feedback on micro lesions, and support clinical diagnosis. This study employs multimodal data fusion techniques to incorporate genomic features such as gene mutations and copy number variations, combined with patient clinical indicators, and utilizes deep learning approaches to establish a dual-channel feature extraction network. Preliminary simulation results demonstrate that the model achieves a sensitivity of 92.3%, specificity of 89.7%, and an AUC value of 0.94 on the independent test set, markedly outperforming traditional single-marker detection methods (P <0.01). Overall, this study provides a novel approach for early cancer screening with significant potential for clinical translation.