Real-time auto-adaptive margin generation for MLC-tracked radiotherapy

Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

The radiotherapy (RT) process from planning to treatment delivery is a multistep, complex operation involving numerous levels of human-machine interaction and requiring high precision. These steps are labor-intensive and time-consuming and require meticulous coordination between professionals with diverse expertise. We reviewed and summarized the current status and prospects of artificial intelligence and machine learning relevant to the various steps in RT treatment planning and delivery workflow specifically in low- and middle-income countries (LMICs). We also searched the PubMed database using the search terms (Artificial Intelligence OR Machine Learning OR Deep Learning OR Automation OR knowledge-based planning AND Radiotherapy) AND (list of Low- and Middle-Income Countries as defined by the World Bank at the time of writing this review). The search yielded a total of 90 results, of which results with first authors from the LMICs were chosen. The reference lists of retrieved articles were also reviewed to search for more studies. No language restrictions were imposed. A total of 20 research items with unique study objectives conducted with the aim of enhancing RT processes were examined in detail. Artificial intelligence and machine learning can improve the overall efficiency of RT processes by reducing human intervention, aiding decision making, and efficiently executing lengthy, repetitive tasks. This improvement could permit the radiation oncologist to redistribute resources and focus on responsibilities such as patient counseling, education, and research, especially in resource-constrained LMICs.

Marker-less online MR-guided stereotactic body radiotherapy of liver metastases at a 1.5 T MR-Linac - Feasibility, workflow data and patient acceptance

INTRODUCTION

Stereotactic body radiotherapy (SBRT) is an established ablative treatment for liver tumors with excellent local control rates. Magnetic resonance imaging guided radiotherapy (MRgRT) provides superior soft tissue contrast and may therefore facilitate a marker-less liver SBRT workflow. The goal of the present study was to investigate feasibility, workflow parameters, toxicity and patient acceptance of MRgSBRT on a 1.5 T MR-Linac.

METHODS

Ten consecutive patients with liver metastases treated on a 1.5 T MR-Linac were included in this prospective trial. Tumor delineation was performed on four-dimensional computed tomography scans and both exhale triggered and free-breathing T2 MRI scans from the MR-Linac. An internal target volume based approach was applied. Organ at risk constraints were based on the UKSABR guidelines (Version 6.1). Patient acceptance regarding device specific aspects was assessed and toxicity was scored according to the common toxicity criteria of adverse events, version 5.

RESULTS

Nine of ten tumors were clearly visible on the 1.5 T MR-Linac. No patient had fiducial markers placed for treatment. All patients were treated with three or five fractions. Median dose to 98% of the gross tumor volume was 38.5 Gy. The median time from "patient identity check" until "beam-off" was 31 min. Median beam on time was 9.6 min. Online MRgRT was well accepted in general and no treatment had to be interrupted on patient request. No event of symptomatic radiation induced liver disease was observed after a median follow-up of ten month (range 3-17 months).

CONCLUSION

Our early experience suggests that online 1.5 T MRgSBRT of liver metastases represents a promising new non-invasive marker-free treatment modality based on high image quality, clinically reasonable in-room times and high patient acceptance. Further studies are necessary to assess clinical outcome, to validate advanced motion management and to explore the benefit of online response adaptive liver SBRT.

Intrafraction Motion Management of Renal Cell Carcinoma With Magnetic Resonance Imaging-Guided Stereotactic Body Radiation Therapy

PURPOSE

One of the major challenges in stereotactic body radiation therapy (SBRT) of renal cell carcinoma is internal motion during treatment. Previous literature has aimed to mitigate the effects of motion by expanding the treatment margins or respiratory tracking. Online magnetic resonance imaging (MRI)-guided radiation therapy has the potential to further improve the treatment of renal cell carcinoma by direct visualization of the tumor during treatment. The efficacy of 2 motion management techniques were assessed: tumor trailing and respiratory tracking. The simulation of a single-fraction, MRI-based SBRT was performed to quantify intrafraction motion and assess the efficacy of the different motion management strategies.

METHODS AND MATERIALS

Fifteen patients were included in the study. At the beginning and end of the scanning protocol, 2 cine-MRI scans were acquired to assess cyclic respiratory motion. In addition, 3-dimensional spoiled gradient echo scans were acquired at 4 different time points to assess the slow drifts over 25 minutes. The systematic and random errors owing to intrafraction drift were calculated, as well as the random error induced by respiratory motion. The motion margins were calculated for tumor trailing and respiratory tracking and compared with the margin when no motion compensation would be performed to assess the relative efficacy of each technique.

RESULTS

The largest respiratory tumor motion was observed along the caudo-cranial direction with a median 95% maximum amplitude of approximately 12 mm. Σ_DRIFT, σ_DRIFT, and σ_RESP were determined to be 1.0 mm 1.8 mm, and 3.8 mm, respectively. Without mechanical immobilization, intrafraction drift accounted for 75% of the total intrafraction motion margin for online midposition-based SBRT treatments.

CONCLUSIONS

The contribution of intrafraction drift to the total internal motion margin is much larger than periodic respiratory motion. This makes tumor trailing a viable option to consider on the MRI linac because it allows for 3-dimensional MRI acquisitions during beam delivery, which simplifies the introduction of new techniques, such as dose accumulation and online intrafraction replanning.

Cancer risk assessment in modern radiotherapy workflow with medical big data

Modern radiotherapy (RT) is being enriched by big digital data and intensive technology. Multimodality image registration, intelligence-guided planning, real-time tracking, image-guided RT (IGRT), and automatic follow-up surveys are the products of the digital era. Enormous digital data are created in the process of treatment, including benefits and risks. Generally, decision making in RT tries to balance these two aspects, which is based on the archival and retrieving of data from various platforms. However, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow. These errors can lead to imbalance between benefits and risks. In addition, the exact mechanism and dose-response relationship for radiation-induced malignancy are not well understood. The cancer risk in modern RT workflow continues to be a problem. Therefore, in this review, we develop risk assessments based on our current knowledge of IGRT and provide strategies for cancer risk reduction. Artificial intelligence (AI) such as machine learning is also discussed because big data are transforming RT via AI.

Survey on deep learning for radiotherapy

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.

Phase I trial of stereotactic MR-guided online adaptive radiation therapy (SMART) for the treatment of oligometastatic or unresectable primary malignancies of the abdomen

PURPOSE/OBJECTIVES

SBRT is used to treat oligometastatic or unresectable primary abdominal malignancies, although ablative dose delivery is limited by proximity of organs-at-risk (OAR). Stereotactic, magnetic resonance (MR)-guided online-adaptive radiotherapy (SMART) may improve SBRT's therapeutic ratio. This prospective Phase I trial assessed feasibility and potential advantages of SMART to treat abdominal malignancies.

MATERIALS/METHODS

Twenty patients with oligometastatic or unresectable primary liver (n = 10) and non-liver (n = 10) abdominal malignancies underwent SMART. Initial plans prescribed 50 Gy/5 fractions (BED 100 Gy) with goal 95% PTV coverage by 95% of prescription, subject to hard OAR constraints. Daily real-time online-adaptive plans were created as needed, based on daily setup MR-image-set tumor/OAR "anatomy-of-the-day" to preserve hard OAR constraints, escalate PTV dose, or both. Treatment times, patient outcomes, and dosimetric comparisons between initial and adaptive plans were prospectively recorded.

RESULTS

Online adaptive plans were created at time of treatment for 81/97 fractions, due to initial plan violation of OAR constraints (61/97) or observed opportunity for PTV dose escalation (20/97). Plan adaptation increased PTV coverage in 64/97 fractions. Zero Grade ≥ 3 acute (<6 months) treatment-related toxicities were observed.

DISCUSSION

SMART is clinically deliverable and safe, allowing PTV dose escalation and/or simultaneous OAR sparing compared to non-adaptive abdominal SBRT.