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.

For the effective execution of SBRT, characterized by tightly confined dose distributions enveloping the tumor with a rapid dose fall-off in adjacent normal tissues, precise image guidance for target volume localization is imperative. ⁸ The HyTEC group (Hypofractionated Treatment Effects in the Clinic), formed as working group of the American Association of Physicists in Medicine (AAPM), has released publications addressing volume dose constraints in non-uniform dose distributions for SBRT. ⁹ Monte Carlo models accurately predict the dose field distribution for photon beams generated by linear accelerators. ¹⁰ Nevertheless, when crafting beam models in treatment planning systems to simulate small fields, it is essential to meticulously account for the influence of both the primary beam source and collimating devices on the calculation of energy fluence and dose. ¹¹

Cone-beam computed tomography (CBCT) preceding beam delivery has become increasingly feasible and popular in recent years for image-guided radiation therapy. Accurately determining the precise position of the tumor during radiation therapy presents a significant challenge. Stick et al evaluated intrafractional tumor variations with fiducial marker by using pre- and posttreatment CBCTs during SBRT for liver metastases. ¹² Recent developments have emerged for evaluating intra-fractional tumor positioning during radiation therapy through real-time registration using kilovoltage x-ray volume imaging. ¹³ Vogel et al ¹⁴ sought to evaluate intrafractional motion in abdominal SBRT during deep-inspiration-breath-hold by employing ultrasound images to track the motion of target structures. Real-time tumor tracking has substantial benefits to target coverage and the potential for safe dose escalation in SBRT. ¹⁵ However, recommendations for implementing motion management strategies tailored to different patient-specific scenarios. ¹⁶

The adaptive approach involves real-time adjustments to treatment plans based on changes in tumor size, shape, or position during radiotherapy. Adaptive radiation therapy aims to ensure precise tumor targeting while minimizing exposure to normal organs, optimizing treatment plans, and striving to enhance target coverage while mitigating treatment-related side effects. ¹⁷ Recently, magnetic resonance image-guided linear accelerator systems now enable real-time adaptation to anatomical changes during treatment. ¹⁸ Henke et al ¹⁹ explores the use of Stereotactic Magnetic Resonance-guided Online Adaptive Radiation Therapy (SMART) for treating challenging ultracentral thorax lesions. SMART achieved excellent local control of 100% at 3 and 6 months, with no severe acute toxicities. The utilization of real-time positron emission signals is also being attempted in novel radiotherapy systems to realize biologically guided radiotherapy. ²⁰ Future developments aim for addressing image distortion, ensuring consistent imaging across magnetic resonance image systems, and validating quantitative imaging's biological relevance.

The convergence of SBRT with systemic therapy offers a promising therapeutic paradigm. Radiation therapy not only reprograms the tumor microenvironment but also exerts immunomodulatory effects in cancer treatment. ²¹ Chang et al ²² investigated the efficacy of combining immunotherapy with SBRT versus SBRT alone for early-stage lung cancer. The combined therapy arm demonstrated significantly improved clinical outcomes with manageable side effects. Zhu et al ²³ explored the effectiveness of combining SBRT, pembrolizumab, and trametinib in treating locally recurrent pancreatic cancer postsurgery, revealing enhanced overall survival compared to conventional regimens. Moore et al ²⁴ tested a new radiation dosing scheme called personalized ultrafractionated stereotactic adaptive radiation therapy (PULSAR) in conjunction with alpha-PD-L1 therapy using mouse models of cancer. They revealed the importance of radiation therapy dosing and scheduling in combination with immunotherapy, emphasizing the potential of PULSAR-style dosing in preclinical models for designing clinical trials.

SBRT has shown commendable efficacy in various cases, yet it is not without limitations. A noteworthy concern is the potential for heightened toxicities compared to conventional fractionation, particularly in radiosensitive organs adjacent to the target area. The clinical application of SBRT warrants careful consideration. Notably, the NRG trial did not demonstrate the superiority of SBRT for the primary endpoint of patient-reported pain response at 3 months. Moreover, in the phase IIR/III NRG-BR002 trial, metastases-directed treatment, including SBRT alongside first-line systemic treatment for breast cancer, did not result in a survival benefit. Further studies are needed to explore the optimal applications of SBRT.

As oncology increasingly incorporates precision medicine, SBRT will also focus on personalized treatment strategies. Advances in imaging, genomics, and other biomarkers will be critical to tailor SBRT regimens to individual patient characteristics and optimize treatment efficacy while minimizing potential side effects. Engaging in research pertaining to SBRT necessitates a profound and intricate amalgamation of knowledge spanning diverse domains, encompassing disciplines as wide-ranging as medical physics, radiation biology, and an array of related fields. To advance effectively in this endeavor, it is imperative to adopt a holistic and interdisciplinary approach, recognizing that the intricacies of this field mandate a comprehensive understanding and collaboration among experts hailing from diverse scientific backgrounds.