Prediction of Local Recurrence Using Clinical and Radiomic Features in Lung Oligometastases Treated with Stereotactic Body Radiotherapy.

IntroductionThis study aimed to develop machine learning-based models to predict local recurrence in patients with lung oligometastases receiving stereotactic body radiotherapy (SBRT), using both clinical and radiomic features.MethodsA total of 80 lung oligometastases from 65 patients treated with SBRT were retrospectively evaluated. Clinical variables and radiomic features extracted from non-contrast planning computed tomography (CT) scans were collected. The dataset was randomly divided into training (70%) and test (30%) sets. Multivariable Cox proportional hazards models were developed to predict local recurrence using three feature sets: clinical only, radiomic only, and combined. Predictive performance was assessed using the concordance index (C-index).ResultsThe median follow-up duration was 11.8 months (range, 6.0-31.5), during which local recurrence was observed in 12 out of 80 lesions (15.0%) treated with SBRT. Multivariable Cox proportional hazards models for predicting local recurrence achieved C-index of 0.75 for the clinical model, 0.74 for the radiomic model, and 0.78 for the combined model. The combined model incorporated three features: soft tissue sarcoma histology (HR 7.70, 95% CI 1.65-35.87, p = 0.009), metastasis size (HR 1.07, 95% CI 1.01-1.14, p = 0.036), and Rad-score (HR 4.05, 95% CI 1.58-10.36, p = 0.003).ConclusionThese findings highlight the potential of machine learning-based models that integrate clinical and radiomic features to predict local recurrence in patients with lung oligometastases undergoing SBRT. Further validation in large, multicenter, and independent cohorts is needed.