Advances in Automated Treatment Planning

Automatic base-dose planning for a robust field junction in total marrow irradiation

PURPOSE

Total marrow (lymph-node) irradiation (TMI/TMLI) is a radiotherapy technique irradiating the whole body of a patient. The limited couch travel range in modern linacs (130-150 cm) forces to split the TMI/TMLI delivery into two plans with opposite orientation. A dedicated field junction is necessary to achieve satisfactory target coverage in the overlapping region of the two plans. In a recent study, we implemented an automatic tool (AT) using the Eclipse Scripting API for the creation of a field junction and optimization of TMI/TMLI. In this work, we improved the AT by developing an automatic base-dose planning approach.

METHODS

Ten patients were selected to compare the manual procedure, original automatic planning approach, and new base-dose approach. Treatment plans were evaluated with the D98%, Dmean, and D2% for the planning target volume at the junction (PTV_J), while Dmean and D2% were considered for the PTV without the junction (PTVNoJ) and healthy tissues.

RESULTS

Base-dose planning significantly improved the PTV_J coverage compared with the manual approach, with unaltered Dmean, consistently lower D2% (2.24 Gy vs. 2.30 Gy) and higher D98% (1.98 Gy vs. 1.89 Gy). Moreover, it significantly reduced the hotspots in healthy tissues (2.02 Gy vs. 2.15 Gy). No significant differences for PTVNoJ dose statistics were observed among the procedures, demonstrating that the automatic approaches produced a target coverage similar to the one obtained manually.

CONCLUSIONS

Base-dose planning improved the field junction optimization of TMI/TMLI. Automatic planning tools can incrementally improve procedures that would be challenging or error-prone to achieve manually.

Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality

Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. Despite technical advancements, the widespread integration of AI into RT treatment planning faces challenges. The transition from controlled retrospective environments to real-world clinical settings introduces heightened scrutiny from clinical end users, potentially leading to decreased clinical acceptance. Key considerations for implementing AI RT planning include ensuring the AI model performance aligns with clinical standards, using high-quality training data, and incorporating sufficient data variation through meticulous curation by clinical experts. Beyond technical aspects, factors such as potential biases and the level of trust clinical end users place in AI may present unforeseen obstacles for real-world clinical use. Addressing these challenges requires bridging education and expertise gaps among clinical end users, enabling them to confidently embrace and utilize AI for routine RT planning. By fostering a better understanding of AI capabilities, building trust, and providing comprehensive training, the promises of AI RT planning can be a reality in the clinical setting. This article assesses the current clinical use of AI RT planning and explores challenges and considerations for bridging gaps in knowledge and expertise for AI operationalization, with focus on training data curation, workflow integration, explainability, bias, and domain knowledge. Remaining challenges in clinical implementation of AI RT treatment planning are examined in the context of trust building approaches.

Monte Carlo methods to assess biological response to radiation in peripheral organs and in critical organs near the target

Background

The biological effects and clinical consequences of out-of-field radiation in peripheral organs can be difficult to determine, especially for low doses (0.1 Gy-1 Gy). In recent years, Monte Carlo (MC) methods have been proposed to more accurately predict nontarget doses. The aim of the present study was to assess the feasibility of using Monte Carlo methods to predict the biological response of tissues and critical organs to low dose radiation (0.1 to 1 Gy) based on results published in the literature.

Materials and methods

Literature review, including studies published by our group.

Results and Conclusions

It has long been assumed that radiation doses to peripheral organs located far from the target volume are too low to have any clinical impact. In recent years, however, concerns about the risk of treatment-induced secondary cancers, even in peripheral organs, have continued to grow in line with increasing life expectancy. At present, it is difficult in routine calculations to accurately determine radiation doses to the whole body and peripheral organs. Moreover, the potential clinical impact of these doses remains uncertain and the biological response to low dose radiation depends on the organ. In this context, MC methods can predict biological response in those organs. Monte Carlo methods have become a powerful tool to better predict the consequences of interactions between ionising radiation and biological matter. MC modelling can also help to characterise microscopic system dynamics and to provide a better understanding of processes occurring at the cellular, molecular, and nanoscales.

Deep learning architecture with shunted transformer and 3D deformable convolution for voxel-level dose prediction of head and neck tumors

Intensity-modulated radiation therapy (IMRT) has been widely used in treating head and neck tumors. However, due to the complex anatomical structures in the head and neck region, it is challenging for the plan optimizer to rapidly generate clinically acceptable IMRT treatment plans. A novel deep learning multi-scale Transformer (MST) model was developed in the current study aiming to accelerate the IMRT planning for head and neck tumors while generating more precise prediction of the voxel-level dose distribution. The proposed end-to-end MST model employs the shunted Transformer to capture multi-scale features and learn a global dependency, and utilizes 3D deformable convolution bottleneck blocks to extract shape-aware feature and compensate the loss of spatial information in the patch merging layers. Moreover, data augmentation and self-knowledge distillation are used to further improve the prediction performance of the model. The MST model was trained and evaluated on the OpenKBP Challenge dataset. Its prediction accuracy was compared with three previous dose prediction models: C3D, TrDosePred, and TSNet. The predicted dose distributions of our proposed MST model in the tumor region are closest to the original clinical dose distribution. The MST model achieves the dose score of 2.23 Gy and the DVH score of 1.34 Gy on the test dataset, outperforming the other three models by 8%-17%. For clinical-related DVH dosimetric metrics, the prediction accuracy in terms of mean absolute error (MAE) is 2.04% for D 99 , 1.54% for D 95 , 1.87% for D 1 , 1.87% for D mean , 1.89% for D 0.1 c c , respectively, superior to the other three models. The quantitative results demonstrated that the proposed MST model achieved more accurate voxel-level dose prediction than the previous models for head and neck tumors. The MST model has a great potential to be applied to other disease sites to further improve the quality and efficiency of radiotherapy planning.

Quality and Safety Considerations for Adaptive Radiation Therapy: An ASTRO White Paper

PURPOSE

Adaptive radiation therapy (ART) is the latest topic in a series of white papers published by the American Society for Radiation Oncology addressing quality processes and patient safety. ART widens the therapeutic index by improving the precision of radiation dose to targets, allowing for dose escalation and/or minimization of dose to normal tissue. ART is performed via offline or online methods; offline ART is the process of replanning a patient's treatment plan between fractions, whereas online ART involves plan adjustment with the patient on the treatment table. This is achieved with in-room imaging capable of assessing anatomic changes and the ability to reoptimize the treatment plan rapidly during the treatment session. Although ART has occurred in its simplest forms in clinical practice for decades, recent technological developments have enabled more clinical applications of ART. With increased clinical prevalence, compressed timelines, and the associated complexity of ART, quality and safety considerations are an important focus area.

METHODS

The American Society for Radiation Oncology convened an interdisciplinary task force to provide expert consensus on key workflows and processes for ART. Recommendations were created using a consensus-building methodology, and task force members indicated their level of agreement based on a 5-point Likert scale, from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters selecting "strongly agree" or "agree" indicated consensus. Content not meeting this threshold was removed or revised.

SUMMARY

Establishing and maintaining an adaptive program requires a team-based approach, appropriately trained and credentialed specialists, significant resources, specialized technology, and implementation time. A comprehensive quality assurance program must be developed, using established guidance, to make sure all forms of ART are performed in a safe and effective manner. Patient safety when delivering ART is everyone's responsibility, and professional organizations, regulators, vendors, and end users must demonstrate a clear commitment to working together to deliver the highest levels of quality and safety.

Safe Hypofractionation Amid Diverse Technologies: Using Teamwork to Manage the Complexity

Radiation oncology caregivers worldwide are dedicated to advancing cancer treatment with the ultimate goal of eradicating the disease. Recognizing the inherent complexity of cancer treatment using hypo-fractionation radiotherapy (HFRT), these caregivers are committed to exploring avenues for progress and providing personalized care to each patient. Strong teams and effective workflows are an essential component to implementing safe HFRT. Every patient presents unique challenges, and as a united team of clinical and administrative professionals, radiation oncology care teams strive to drive advancements and streamline complexities in their field, guided by continuous technological innovation.

Artificial intelligence (AI) applications in improvement of IMRT and VMAT radiotherapy treatment planning processes: A systematic review

INTRODUCTION

Radiotherapy is a common option in the treatment of many types of cancer. Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT) are the latest radiotherapy techniques. However, clinicians face problems due to these techniques' complexity and time-consuming planning. Various studies have pointed out the importance and role of artificial intelligence (AI) in radiotherapy and accelerating and improving its quality. This research explores different AI methods in different fields of IMRT and VMAT. This study evaluated both quantitative and qualitative methods used within the reviewed articles.

METHODS

Various articles were reviewed from Google Scholar, Science Direct, and PubMed databases between 2018 and 2024. According to PRISMA 2020 guidelines, study selection processes, screening, and inclusion and exclusion criteria were defined. The critical Appraisal Skill Program qualitative checklist tool was used for the qualitative evaluation of articles.

RESULTS

26 articles met the inclusion among the 33 articles obtained. The search procedure was displayed using the PRISMA flow diagram. The evaluation of the articles shows the automation of various treatment planning processes by AI methods and their better performance than traditional methods. The qualitative evaluation of studies has demonstrated the high quality of all studies. The lowest score obtained from the qualitative evaluation of the article is 7 out of 9.

CONCLUSION

AI methods used in radiotherapy reduce time and increase prediction accuracy. They also work better than other methods in different areas, such as dose prediction, treatment design, and dose delivery.

IMPLICATIONS FOR PRACTICE

Healthcare providers should consider integrating artificial intelligence technologies into their practice to optimize treatment planning and enhance patient care in radiation therapy. Additionally, fostering collaboration between radiotherapy experts and artificial intelligence specialists can significantly improve the development and application of AI technologies in this field.

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

BACKGROUND AND PURPOSE

Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.

METHODS AND MATERIALS

A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.

RESULTS

The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.

CONCLUSION

A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

Development and validation of an automated Tomotherapy planning method for cervical cancer

PURPOSE

This study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness.

MATERIALS AND METHODS

The study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. We compared A-TOMO with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis, with a P-value < 0.05 indicating statistical significance.

RESULTS

A-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P+ < 0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V50Gy, V40Gy and Dmean for the bladder and rectum, as well as Dmean for the bowel bag, femoral heads, and kidneys (all P+ < 0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 min.

CONCLUSION

We have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning.

Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic adjustment of optimization parameters

In radiotherapy treatment planning, optimization is essential for achieving the most favorable plan by adjusting optimization criteria. This study introduced an innovative approach to automatically fine-tune optimization parameters for volumetric modulated arc therapy prostate planning, ensuring all constraints were met. A knowledge-based planning model was invoked, and the fine-tuning process was applied through an in-house developed script. Among 25 prostate plans, this fine-tuning increased the number of plans meeting all constraints from 10/25 to 22/25, with a reduction in mean monitor units per gray without increasing plan's complexity. This automation improved efficiency by saving time and resources in treatment planning.

Evaluation of Ethos intelligent optimization engine for left locally advanced breast cancer

Purpose

To evaluate the feasibility to use a standard Ethos planning template to treat left-sided breast cancer with regional lymph nodes.

Material/Methods

The tuning cohort of 5 patients was used to create a planning template. The validation cohort included 15 patients treated for a locally advanced left breast cancer randomly enrolled. The Ethos planning template was tuned using standard 3 partial arc VMAT and two collimator rotation configurations: 45/285/345° and 30/60/330°. Re-planning was performed automatically using the template without editing. The study was conducted with a schedule of 42.3 Gy in 18 fractions to the breast/chestwall, internal mammary chain (IMC) and regional lymph nodes ("Nodes"). The PTV was defined as a 3D extension of the CTV with a margin of 7 mm, excluding the 5mm below the skin. The manual treatment plans were performed using Eclipse treatment planning system with AAA and PO algorithms (v15.6) and a manual arc VMAT configuration and imported in Ethos TPS (v1.1) for a dose calculation with Ethos Acuros algorithm. The automated plans were compared with the manual plans using PTV and CTV coverage, homogeneity and conformity indices (HI and CN) and doses to organs at risk (OAR) via DVH metrics. For each plan, the patient quality assurance (QA) were performed using Mobius3D and gamma index. Finally, two breast radiation oncologists performed a blinded assessment of the clinical acceptability of each of the three plans (manual and automated) for each patient.

Results

The manual and automated plans provided suitable treatment planning as regards dose constraints. The dosimetric comparison showed the CTV_breast D99% were significantly improved with both automated plans (p< 0,002) while PTV coverage was comparable. The doses to the organs at risk were equivalent for the three plans. Concerning treatment delivery, the Ethos-45° and Ethos-30° plans led to an increase in MUs compared to the manual plans, without affecting the beam on time. The average gamma index pass rates remained consistently above 98% regardless of the type of plan utilized. In the blinded evaluation, clinicians 1 and 2 assessed 13 out of 15 plans for Ethos 45° and 11 out of 15 plans for Ethos 30° as clinically acceptable.

Conclusion

Using a standard planning template for locally advanced breast cancer, the Ethos TPS provided automated plans that were clinically acceptable and comparable in quality to manually generated plans. Automated plans also dramatically reduce workflow and operator variability.

Artificial general intelligence for radiation oncology

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.

The role of artificial intelligence in radiotherapy clinical practice

This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical practice. We will discuss how AI has a place in the modern radiotherapy workflow at the level of automatic segmentation and planning, two applications which have seen real-work implementation. A special emphasis will be placed on the role AI can play in online adaptive radiotherapy, such as performed at MR-linacs, where online plan adaptation is a procedure which could benefit from automation to reduce on-couch time for patients. Pseudo-CT generation and AI for motion tracking will be introduced in the scope of online adaptive radiotherapy as well. We further discuss the use of AI for decision-making and response assessment, for example for personalized prescription and treatment selection, risk stratification for outcomes and toxicities, and AI for quantitative imaging and response assessment. Finally, the challenges of generalizability and ethical aspects will be covered. With this, we provide a comprehensive overview of the current and future applications of AI in radiotherapy.

Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM Watson Assistant Platform

Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: Using the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed following a pre-designed flowchart that outlines the conversation flow. This approach ensured the development of the chatbot with a clear mindset and allowed for effective tracking of the conversation. The chatbot is equipped to furnish users with information and quizzes on radiotherapy to assess their understanding of the subject. Results: By adopting a question-and-answer approach, the chatbot can engage in human-like communication with users seeking information about radiotherapy. As some users may feel anxious and struggle to articulate their queries, the chatbot is designed to be user-friendly and reassuring, providing a list of questions for the user to choose from. Feedback on the chatbot's content was mostly positive, despite a few limitations. The chatbot performed well and successfully conveyed knowledge as intended. Conclusions: There is a need to enhance the chatbot's conversation approach to improve user interaction. Including translation capabilities to cater to individuals with different first languages would also be advantageous. Lastly, the newly launched ChatGPT could potentially be developed into a medical chatbot to facilitate knowledge transfer.