Search

Simple Summary: Radiotherapy, a crucial cancer treatment, has evolved significantly over the years. Traditionally, treatment plans were based on initial scans used throughout the treatment course, accounting for changes in the patient's anatomy by additional margins to targets. However, the field has moved towards decreasing margins with the advancement of delivery and targeting accuracy in order to decrease toxicity, and the increasing use of image guidance has illuminated patient anatomical changes such as organ deformation, weight loss, tumor shrinkage, and even biological changes that are unaccounted for by the conventional approach. Adaptive radiotherapy (ART) addresses this by adjusting treatment plans according to these changes. ART can be conducted in two ways: online (adjustments made during treatment sessions) and offline (adjustments made between treatment sessions). Advances in technology, especially in medical imaging (CT, MRI, and PET scans) and artificial intelligence, have made ART more feasible and efficient. ART offers more precise cancer treatment by adapting to changes in the patient's body, leading to better outcomes with fewer side effects. Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiotherapy (ART), a notable innovation that addresses anatomy changes and optimizes the therapeutic ratio. ART utilizes advanced imaging techniques such as CT, MRI, and PET to modify the treatment plan based on observed anatomical changes and even biological changes during the course of treatment. The narrative review provides a comprehensive guide on ART for healthcare professionals and trainees in radiation oncology and anyone else interested in the topic. The incorporation of artificial intelligence in ART has played a crucial role in improving effectiveness, particularly in contour segmentation, treatment planning, and quality assurance. This has expedited the process to render online ART feasible, lowered the burden for radiation oncology practitioners, and enhanced the precision of dynamically personalized treatment. Current technical and clinical progress on ART is discussed in this review, highlighting the ongoing development of imaging technologies and AI and emphasizing their contribution to enhancing the applicability and effectiveness of ART. [ABSTRACT FROM AUTHOR]

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations. [ABSTRACT FROM AUTHOR]

Positron emission tomography (PET)-guided radiation therapy is a novel tracked dose delivery modality that uses real-time PET to guide radiation therapy beamlets. The BIOGUIDE-X study was performed with sequential cohorts of participants to (1) identify the fluorodeoxyglucose (FDG) dose for PET-guided therapy and (2) confirm that the emulated dose distribution was consistent with a physician-approved radiation therapy plan. This prospective study included participants with at least 1 FDG-avid targetable primary or metastatic tumor (2-5 cm) in the lung or bone. For cohort I, a modified 3 + 3 design was used to determine the FDG dose that would result in adequate signal for PET-guided therapy. For cohort II, PET imaging data were collected on the X1 system before the first and last fractions among patients undergoing conventional stereotactic body radiation therapy. PET-guided therapy dose distributions were modeled on the patient's computed tomography anatomy using the collected PET data at each fraction as input to an "emulated delivery" and compared with the physician-approved plan. Cohort I demonstrated adequate FDG activity in 6 of 6 evaluable participants (100.0%) with the first injected dose level of 15 mCi FDG. In cohort II, 4 patients with lung tumors and 5 with bone tumors were enrolled, and evaluable emulated delivery data points were collected for 17 treatment fractions. Sixteen of the 17 emulated deliveries resulted in dose distributions that were accurate with respect to the approved PET-guided therapy plan. The 17th data point was just below the 95% threshold for accuracy (dose-volume histogram score = 94.6%). All emulated fluences were physically deliverable. No toxicities were attributed to multiple FDG administrations. PET-guided therapy is a novel radiation therapy modality in which a radiolabeled tumor can act as its own fiducial for radiation therapy targeting. Emulated therapy dose distributions calculated from continuously acquired real-time PET data were accurate and machine-deliverable in tumors that were 2 to 5 cm in size with adequate FDG signal characteristics. [ABSTRACT FROM AUTHOR]