Journal Article

Integrating Radiomics and Artificial Intelligence (AI) in Stereotactic Body Radiotherapy (SBRT)/Stereotactic Radiosurgery (SRS): Predictive Tools for Tailored Cancer Care

Lastrucci, Andrea, Visani, Luca, Livi, Lorenzo, Loi, Mauro, Bertini, Niccolò, Tamberi, Stefano, Garlatti, Pietro, Valzano, Marianna, Banini, Marco, Desideri, Isacco, Morelli, Ilaria, Greto, Daniela, Salvestrini, Viola, Aquilano, Michele

2025

Overview

Purpose: This systematic review aims to analyze the literature on the application of AI in predicting patient outcomes and treatment-related toxicity in those undergoing SBRT or SRS across heterogeneous tumor sites. Materials and methods: Our review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. PubMed, EMBASE and Scopus were systematically searched for English-language human studies evaluating AI for outcome and toxicity prediction in patients undergoing SBRT or SRS for solid tumors. Search terms included (“Stereotactic Body Radiotherapy” OR “SBRT” OR “Stereotactic Radiosurgery” OR “SRS” OR “Stereotactic Ablative Radiotherapy” OR “SABR”) AND (“Artificial Intelligence” OR “AI” OR “Machine Learning” OR “Deep Learning” OR “Radiomics”) AND (“Response Prediction” OR “Response to Treatment” OR “Outcome Prediction”) AND (“Toxicity” OR “Side Effects” OR “Treatment Toxicities” OR “Adverse Events”). Results: The search yielded 29 eligible retrospective studies, published between 2020 and 2025. Eight studies addressed early-stage primary lung cancer, highlighting the potential of AI-based models in predicting radiation-induced pneumonitis, fibrosis and local control. Five studies investigated AI models for predicting hepatobiliary toxicity following SBRT for liver tumors. Sixteen studies involved SRS-treated patients with brain metastases or benign intracranial neoplasms (e.g., arteriovenous malformations, vestibular schwannomas, meningiomas), exploring AI algorithms for predicting treatment response and radiation-induced changes. In the results, AI might have been exploited to both reaffirm already known clinical predictors and to identify novel imaging, dosimetric or biological biomarkers. Examples include predicting radiation pneumonitis in lung cancer, residual liver function in hepatic tumors and local recurrence in brain metastases, thus supporting tailored treatment decisions. Conclusions: Combining AI with SBRT could greatly enhance personalized cancer care by predicting patient-specific outcomes and toxicity. AI models analyze complex datasets, including imaging and clinical data, to identify patterns that traditional methods may miss, thus enabling more accurate risk stratification and reducing variability in treatment planning. With further research and clinical validation, this integration could make radiotherapy safer, more effective and contribute to advancement in precision oncology.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Artificial intelligence / Cancer therapies / Citation management software / Decision making / Deep learning / Dosimetry / Drug dosages / Fibrosis / Liver cancer / Lung cancer / Machine learning / Metastases / Neuroimaging / Oncology / Patients / Planning / Pneumonitis / Precision medicine / Predictions / Prostate / Radiation therapy / Radiomics / Radiosurgery / Respiration / Side effects / Solid tumors / Systematic Review / Toxicity / Tumors / Vestibular system