Researchers built AI models that predict immunotherapy outcomes in advanced lung cancer. The I3LUNG study enrolled patients at six hospitals in Italy, Greece, Germany, Spain, the United States and Israel. The team combined clinical and blood data, CT scans, digital pathology slides and genetic test results. Models using only clinical and blood data performed consistently across survival and disease-control outcomes. Accuracy fell when the models were tested on patients from a separate hospital. The authors attribute the drop to differences between patient populations. The models beat PD-L1 and other established markers in the independent test set. Both lung cancer specialists and non-specialists predicted outcomes better when using the explainable tool. Adding imaging and pathology raised performance in some analyses, but the added benefit remains uncertain. A prospective study is now testing the decision support system.
What changed
Immunotherapy choices in advanced lung cancer relied on PD-L1 levels and simple clinical scores.
What it unlocks
Predicting immunotherapy outcomes in advanced lung cancer from routine clinical and blood data, with reasons shown to the physician.
- 2,396 patients enrolled
- AUC up to 0.77 in test set
- external validation AUC 0.55-0.72
- prospective validation in 2,000+ patients
What you need to act on it
- baseline clinical and blood data at treatment start
- prospective validation before clinical use
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