AI model using routine data improves lung cancer immunotherapy outcome prediction
On 13 September, Nature Medicine published work of the I3LUNG project analyzing 2,396 patients from six clinical centers. Authors checked whether an AI prediction based on routinely collected pretreatment data could outperform standard biomarkers and change the assessment of completed cases.
Immunotherapy helps the immune system attack tumors, yet in metastatic non‑small‑cell lung cancer it is hard to know who will achieve disease control and who will live longer. PD‑L1 tumor level is one guide but captures only part of the patient’s situation. The study examined how much prognostic information already exists in ordinary medical records.
For the main model the authors selected nine pretreatment features: sex, smoking status, ability to perform daily activities, PD‑L1, metastasis location, and blood‑test results. In an independent patient cohort the model surpassed single biomarkers and the LIPI blood‑based index in distinguishing disease control and several survival outcomes.
Adding CT scans and digital tumor‑slice images improved performance when tested on data from the same centers, but this advantage was inconsistent in external groups. The model relying only on clinical data and blood work proved more stable, and the most reliable prediction came from the pretreatment data set that physicians already have.
To test usability, twenty physicians reviewed 100 case histories, first with patient data alone and then with model output and explanations of which features shifted the prediction. After the model’s suggestion, correct identification of cases where disease was kept under control rose from 0.72 to 0.87, and overall accuracy increased from 0.57 to 0.65, although false‑positive control predictions rose slightly.
The next phase of I3LUNG is already underway, testing the system on more than 2,000 patients. The published paper describes its retrospective phase—analysis of accumulated clinical data and review of finished cases together with clinicians.
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