Machine Learning Predicts Overall Survival After Lung SBRT: 158 Patients at Six Centres and 180 Features Give a Pooled Concordance Index of 0.62, in Scientific Reports on November 13
A multicenter study published in Scientific Reports on November 13, 2026, developed machine-learning models to predict overall survival after lung stereotactic body radiotherapy, or SBRT, for primary and metastatic lung tumours, using planning-CT-derived features. Clinical and dosimetric data were retrospectively collected from six centres, covering 181 lesions in 158 patients, with 180 features aggregated to the patient level, namely 124 radiomic, 45 clinical, and 11 dosimetric features. The authors are Invernizzi and colleagues, and the paper carries digital object identifier 10.1038/s41598-026-77575-6, was received on January 30, 2026, accepted on October 21, 2026, and published on November 13, 2026. Overall survival was modelled as a time-to-event outcome using random survival forests, per tumour origin and pooled with tumour origin as a covariate, with features selected by LASSO within cross-validation folds and interpreted with SHAP. The journal abstract reported that the pooled model achieved a concordance index of 0.62, with a 95 percent confidence interval of 0.54 to 0.71, and time-dependent areas under the curve of 0.69, 0.62 and 0.52 at 1, 2 and 3 years. A temporal split preserved the concordance index at 0.61, whereas leave-one-centre-out validation gave a pooled concordance index of 0.57, with a 95 percent confidence interval of 0.48 to 0.65. The authors described these leakage-free, censoring-appropriate models as an honest, hypothesis-generating benchmark, most informative at one year, that will require external validation. The figures in this item are taken from the journal version, not from the study's preprint version.
(Source: Scientific)
Source: Scientific
Published by GSM · USA (original Chinese edition)
Photo: 图：一台用于放射治疗的医用直线加速器设备。Narenfox，CC BY-SA 4.0，经 Wikimedia Commons。 / Photo: Narenfox, CC BY-SA 4.0, via Wikimedia Commons.
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