Recent Advances and Challenges in Stereotactic Body Radiotherapy

Stereotactic body radiotherapy (SBRT) represents a sophisticated radiotherapeutic paradigm, delivering exceptionally precise and potent radiation doses to target tumor sites while preserving the integrity of surrounding healthy tissues.This unparalleled precision is achieved through the utilization of cutting-edge image-guided technologies, pioneering treatment planning algorithms, and stringent quality assurance protocols.There is a growing body of favorable evidence supporting SBRT compared to conventional fractionation regimens in radiotherapy.Ball et al 1 reported that SBRT demonstrated enhanced local control of the primary cancer without a rise in significant adverse effects when compared to conventional radiotherapy for patients with unresectable stage I NSCLC located in the peripheral region.Sahgal et al 2 revealed that SBRT was superior to conventional fractionated radiotherapy in improving the complete response rate for pain in patients with painful spinal metastases.In the PACE-B trial, prostate SBRT was found to be safe and associated with low rates of side-effects compared to conventional schedules of radiotherapy.3 Artificial intelligence (AI) has seen an escalating integration into radiotherapy, akin to its presence in other fields, in order to address intricate and multifaceted challenges.4 AI enables the efficient processing of large volumes of information and data stored in radiotherapy systems, tasks that are challenging for individuals or groups to manage.It facilitates the automation of complex tasks in large datasets, such as organ delineation and treatment planning and outcome prediction.5 However, there are concerns regarding the need for standardization and addressing ethical, legal, and skill-related challenges in the adoption of AI in radiotherapy.Radiomics involves the extraction of quantitative data in the form of radiomic features from medical images.These radiomic features, obtained prior to treatment initiation, offer invaluable insights into treatment outcomes and the likelihood of adverse events.Sawayanagi et al 6 investigated SBRT for early-stage NSCLC, identifying a radiomic feature and predictive model for overall survival, as well as assessing recurrence rates.Shen et al 7 established a predictive model employing radiomic features for radiation-induced liver disease in patients with hepatocellular carcinoma undergoing SBRT.