Handling missing data: AI approach for survival prediction in lung cancer despite missing data.
Where this comes from
- Record sourced from PubMed, PMID 42463901.
- Also identified by DOI 10.1038/s41746-026-03019-0 and PMC identifier 13376391.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Optimal cancer prognostication combines multimodal data including biopsy results, CT scan, patient characteristics, and clinical trajectory thus far. However, many patients do not have all modalities of data available, so requiring physicians to have all patient data modalities to use an AI prediction algorithm limits the tool’s clinical utility. To increase the clinical potential of these AI algorithms, Ruffini et al. developed a “missing data aware” survival prediction approach that is able to handle inputs with missing data for risk stratification in non-small cell lung cancer patients.