SHIFT Model: Turning Incomplete Genomic Data into Fuel for Clinical Decisions

According to 2025 statistics from the US National Cancer Institute (NCI), over 40% of cancer patients' genomic sequencing data have at least one key gene locus missing. Heterogeneity between different detection platforms causes prediction accuracy to drop by an average of 12% to 18% after data integration. This kind of "data fragmentation" directly leads to a cliff-like drop in the performance of existing survival prediction models in real clinical scenarios—plummeting from 90% AUC in papers to below 60%. The SHIFT model was born against this backdrop.