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.

Biochemical recurrences after radical prostatectomy (RP) can be managed with curative purpose through salvage radiation therapy (SRT). RT dose escalation, such as stereotactic RT (SSRT), may improve relapse-free survival in this setting. STARR trial (NCT05455736) is a prospective multicenter study including patients affected by macroscopic recurrence within the prostate bed after RP treated with SSRT. Recurrence was detected with a Choline or PSMA CT-PET. In the current analysis, the early biochemical response (BR) rate and toxicity profile after three months of follow-up were assessed. Twenty-five patients were enrolled, and data about BR and toxicity at three months after treatment were available for 19 cases. Overall, BR was detected after three months in 58% of cases. Four G1–G2 adverse events were recorded; no G ≥ 3 adverse events were detected. SSRT appears feasible and safe, with more than half of patients experiencing BR and an encouraging toxicity profile. The STARR trial is one of the few prospective studies aimed at implementing this promising treatment strategy in this scenario.

Purpose/Objectives: Salvage radiotherapy (SRT) after a radical prostatectomy is a curative approach for patients with biochemical recurrence (BR). However, outcomes are often less favorable when imaging reveals macroscopic local recurrence. In such cases, dose escalation through stereotactic salvage radiotherapy (SSRT) may offer improved disease control. The STARR trial (NCT05455736) is a prospective, multicenter study evaluating the efficacy and safety of SSRT in patients with macroscopic prostate bed recurrence. This interim analysis reports early findings from the initial patient cohort. Materials and Methods: Patients with BR (PSA > 0.2 ng/mL) post-prostatectomy and PET-confirmed macroscopic recurrence (PSMA or Choline PET, confirmed by MRI) were eligible. Treatment involved CyberKnife^®-based SSRT delivering 35 Gy in five fractions to the visible lesion. Androgen deprivation therapy (ADT) was not permitted. Complete biochemical response (CBR) was defined as PSA < 0.2 ng/mL, and biochemical response (BR) as a ≥50% PSA reduction. Additional outcomes included biochemical, radiological, and ADT-free survival (bPFS, rPFS, aPFS). Results: As of analysis, 51 patients were enrolled, with a median follow-up of 16 months (95% CI: 16–22). CBR and BR were achieved in 45.1% and 80.4% of patients, respectively. Events affecting bPFS, rPFS, and aPFS occurred in 12, 5, and 6 patients, with median values not yet reached. Toxicity was minimal, with two cases each of acute grade 2 GI and GU events, and one late grade 2 GI event. No grade ≥ 3 toxicities were reported. Conclusion: Early data support SSRT as a safe and a promising option for macroscopic local recurrence, with encouraging response rates and minimal toxicity.