Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

DEVELOPMENT AND CLINICAL VALIDATION OF KNOWLEDGE-BASED PLANNING MODELS FOR STEREOTACTIC BODY RADIOTHERAPY OF EARLY-STAGE NON-SMALL-CELL LUNG CANCER PATIENTS

Lung stereotactic body radiotherapy (SBRT) is a viable alternative to surgical intervention for the treatment of early-stage non-small-cell lung cancer (NSCLC) patients. This therapy achieves strong local control rates by delivering ultra-high, conformal radioablative doses in typically one to five fractions. Historically, lung SBRT plans are manually generated using 3D conformal radiation therapy, dynamic conformal arcs (DCA), intensity-modulated radiation therapy, and more recently via volumetric modulated arc therapy (VMAT) on a C-arm linear accelerator (linac). Manually planned VMAT is an advanced technique to deliver high-quality lung SBRT due to its dosimetric capabilities and utilization of flattening-filter free beams to improve patient compliance. However, there are limitations in manual treatment planning as the final plan quality heavily depends on a planner’s skill and available planning time. This could subject the plan quality to inter-planner variability from a single institution with multiple planners. Generally, the standard lung SBRT patient ‘simulation-to-treatment’ time is 7 working days. This delays clinic workflow and degrades the quality of treatment by eliminating adaptive re-planning capabilities. There is an ongoing effort to automate treatment planning by creating a model library of previously treated, high-quality plans and using it to prospectively generate new plans termed model-based knowledge-based planning (KBP). KBP aims to mitigate the previously mentioned limitations of manual planning and improve clinic workflow. As part of this dissertation, lung SBRT KBP models were created using a commercially available KBP engine that was trained using non-coplanar VMAT lung SBRT plans with the final dose reported from an advanced Acuros-based algorithm. The dissertation begins with the development of a robust and adaptable lung SBRT KBP model for early-stage, centrally-located NSCLC tumors that is fully compliant with Radiation Therapy Oncology Group (RTOG)-0813 protocol’s requirements. This new model provided similar or better plan quality to clinical plans, however it significantly increased total monitor units and plan complexity. This prompted the development and validation of an automated KBP routine for SBRT of peripheral lung tumors via DCA-based VMAT per RTOG-0618 criteria. This planning routine helped incorporate a historical DCA-based treatment planning approach with a VMAT optimization automated KBP engine that helps reduce plan complexity. For both central and peripheral lung lesions, the validated models are able to generate high-quality, standardized plans in under 30 min with minimal planner effort compared to an estimated 129 ± 34 min of a dedicated SBRT planner’s time. In practice, planners are expected to meticulously work on multiple plans at once, significantly increasing manual planning time. Thus, these KBP models will shorten the ‘simulation-to-treatment’ time down to as few as 3 working days, reduce inter-planer variability and improve patient safety. This will help standardize clinics and enable offline adaptive re-planning of lung SBRT treatment to account for physiological changes errors resulting from improper patient set-up. Lastly, this dissertation sought to further expand these KBP models to support delivering lung SBRT treatments on a new O-ring linac that was recently introduced to support underserved areas and fast patient throughput. Despite learning from a C-arm modality training dataset, these KBP models helped the O-ring linac to become a viable treatment modality for lung SBRT by providing an excellent plan quality similar to a C-arm linac in under 30 min. These KBP models will facilitate the easy transfer of patients across these diverse modalities and will provide a solution to unintended treatment course disruption due to lengthy machine downtime. Moreover, they will relieve the burden on a single machine in a high-volume lung SBRT clinic. Further adaptation and validation of these KBP models for large lung tumors (\u3e 5 cm) with multi-level dosing scheme and synchronous multi-lesion lung SBRT is ongoing

Optimizing Respiratory Gated Intensity Modulated Radiation Therapy Planning and Delivery of Early-Stage Non-Small Cell Lung Cancer

Stereotactic ablative body radiotherapy (SABR) is the standard of care for inoperable early-stage non-small cell lung cancer (NSCLC) patients. However, thoracic tumours are susceptible to respiratory motion and, if unaccounted for, can potentially lead to dosimetric uncertainties. Respiratory gating is one method that limits treatment delivery to portions of the respiratory cycle, but when combined with intensity-modulated radiotherapy (IMRT), requires rigorous verification. The goal of this thesis is to optimize respiratory gated IMRT treatment planning and develop image-guided strategies to verify the dose delivery for future early-stage NSCLC patients. Retrospective treatment plans were generated for various IMRT delivery techniques, including fixed-beam, volumetric modulated arc therapy (VMAT), and helical tomotherapy. VMAT was determined the best technique for optimizing dose conformity and efficiency. A second treatment planning study that considered patients exhibiting significant tumour motion was conducted. Respiratory ungated and gated VMAT plans were compared. Significant decreases in V20Gy and V50%, predictors for radiation pneumonitis and irreversible fibrosis, respectively, were observed. The predominant uncertainty of respiratory gating lies in the ability of an external surrogate marker to accurately predict internal target motion. Intrafraction triggered kV imaging was validated in a programmable motion phantom study as a method to determine how correlated the internal and external motion are during ungated and gated VMAT deliveries and to identify potential phase shifts between the motions. KV projections acquired during gated VMAT delivery were used to reconstruct gated cone-beam CT (CBCT), providing 3D tumour position verification. Image quality and target detectability, in the presence of MV scatter from the treatment beam to the kV detector, was evaluated with various imaging parameters and under real-patient breathing motion conditions. No significant difference in image quality was observed for the CBCT acquisitions with or without the presence of MV scatter. This thesis explores the benefits of combining respiratory gating with IMRT/VMAT for the treatment of early stage NSCLC with SABR, and evaluates advanced on-board imaging capabilities to develop dose delivery verification protocols. The results of this thesis will provide the tools necessary to confidently implement a respiratory gated radiotherapy program aimed at improving the therapeutic ratio for early-stage NSCLC

The impact of technology on the changing practice of lung SBRT

Stereotactic body radiotherapy (SBRT) for lung tumours has been gaining wide acceptance in lung cancer. Here, we review the technological evolution of SBRT delivery in lung cancer, from the first treatments using the stereotactic body frame in the 1990's to modern developments in image guidance and motion management. Finally, we discuss the impact of current technological approaches on the requirements for quality assurance as well as future technological developments

Volumetric modulated arc therapy for stereotactic body radiotherapy: Planning considerations, delivery accuracy and efficiency

Senan, S. [Promotor]Slotman, B.J. [Promotor]Verbakel, W.F.A.R. [Copromotor

Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy

Purpose: To enhance an in-house graphic-processing-unit (GPU) accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS). Methods and Materials: A block aperture module was integrated into VPMC. VPMC was validated by an opensource code, MCsquare, in eight water phantom simulations with 3cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45mm water equivalent thickness. VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 cc). Finally, 3 patients were selected for robust optimization with aperture blocks using VPMC. Results: In the water phantoms, 3D gamma passing rate (2%/2mm/10%) between VPMC and MCsquare were 99.71$\pm$0.23%. In the patient geometries, 3D gamma passing rates (3%/2mm/10%) between VPMC/MCsquare and RayStation MC were 97.79$\pm$2.21%/97.78$\pm$1.97%, respectively. The calculation time was greatly decreased from 112.45$\pm$114.08 seconds (MCsquare) to 8.20$\pm$6.42 seconds (VPMC), both having statistical uncertainties of about 0.5%. The robustly optimized plans met all the dose-volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 seconds. Conclusion: VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.Comment: 3 tables, 3 figure

IGRT and motion management during lung SBRT delivery.

Patient motion can cause misalignment of the tumour and toxicities to the healthy lung tissue during lung stereotactic body radiation therapy (SBRT). Any deviations from the reference setup can miss the target and have acute toxic effects on the patient with consequences onto its quality of life and survival outcomes. Correction for motion, either immediately prior to treatment or intra-treatment, can be realized with image-guided radiation therapy (IGRT) and motion management devices. The use of these techniques has demonstrated the feasibility of integrating complex technology with clinical linear accelerator to provide a higher standard of care for the patients and increase their quality of life

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy

Purpose: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. Results: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s

The Possibilities and Dosimetric Limitations of MLC-Based Intensity-Modulated Radiotherapy Delivery and Optimization Techniques

The use of intensity-modulated radiotherapy (IMRT) has increased extensively in the modern radiotherapy (RT) treatments over the past two decades. Radiation dose distributions can be delivered with higher conformality with IMRT when compared to the conventional 3D-conformal radiotherapy (3D-CRT). Higher conformality and target coverage increases the probability of tumour control and decreases the normal tissue complications. The primary goal of this work is to improve and evaluate the accuracy, efficiency and delivery techniques of RT treatments by using IMRT. This study evaluated the dosimetric limitations and possibilities of IMRT in small (treatments of head-and-neck, prostate and lung cancer) and large volumes (primitive neuroectodermal tumours). The dose coverage of target volumes and the sparing of critical organs were increased with IMRT when compared to 3D-CRT. The developed split field IMRT technique was found to be safe and accurate method in craniospinal irradiations. By using IMRT in simultaneous integrated boosting of biologically defined target volumes of localized prostate cancer high doses were achievable with only small increase in the treatment complexity. Biological plan optimization increased the probability of uncomplicated control on average by 28% when compared to standard IMRT delivery. Unfortunately IMRT carries also some drawbacks. In IMRT the beam modulation is realized by splitting a large radiation field to small apertures. The smaller the beam apertures are the larger the rebuild-up and rebuild-down effects are at the tissue interfaces. The limitations to use IMRT with small apertures in the treatments of small lung tumours were investigated with dosimetric film measurements. The results confirmed that the peripheral doses of the small lung tumours were decreased as the effective field size was decreased. The studied calculation algorithms were not able to model the dose deficiency of the tumours accurately. The use of small sliding window apertures of 2 mm and 4 mm decreased the tumour peripheral dose by 6% when compared to 3D-CRT treatment plan. A direct aperture based optimization (DABO) technique was examined as a solution to decrease the treatment complexity. The DABO IMRT technique was able to achieve treatment plans equivalent with the conventional IMRT fluence based optimization techniques in the concave head-and-neck target volumes. With DABO the effective field sizes were increased and the number of MUs was reduced with a factor of two. The optimality of a treatment plan and the therapeutic ratio can be further enhanced by using dose painting based on regional radiosensitivities imaged with functional imaging methods.Siirretty Doriast

Clinical Implementation of Hypofractionated Radiation therapy for Lung Malignancies

For patients with oligometastases, metastases limited in number and site, the use of radiation therapy treatment with a hypofractionated dose scheme has been proposed as a potential ablative approach. There are a limited number of prospective studies looking at hypofractionated radiation therapy (HRT) for lung oligometastasis. Normal lung tissue complication and radiation planning technique are significant limiting factors for the implementation of hypofractionated lung metastasis. The problem statement of this study is how to improve the clinical implementation of HRT for lung metastasis exploring lung toxicity predictors, and developing an efficient radiation planning method. In the first study, we analyzed the dose distribution for 28 patients with lung oligometastasis and treated with HRT to multiple metastases in the lungs. We identified several significant predictors for lung radiation pneumonitis (RP) including the mean lung dose (MLD), V13 and V20. In addition a dose-effect relation between the lung normalized total dose (NTD) and RP may exist up to 48 Gy in three fractions. The dose-response parameters derived in our study appear to agree with other hypofractionated results published in the literature. In the second study, we used an inverse planning algorithm to develop a new radiation planning method by limiting the number of segments per beam angle down to 1 segment. Single segment plans were able to significantly improve tumor coverage and conformality, reduce the risk of lung RP, while simplifying the planning process and delivery. Target conformality and normal lung tissue sparing did not gain much improvement from an increase of plan complexity to five segments over the simplified one segment technique. The automation of our method is a good alternative to more traditional methods and offers significant dosimetric benefits. In the third study, we verified the single segment planning technique via patient specific quality assurance (QA) in a motion phantom. We found good agreement between calculated and measured doses via thermoluminescent detectors (TLD) inside the target. A dose to distance agreement of 3%/3 mm and 2%/2 mm between calculation and film measurements for representative plans in a motion phantom was verified at 98.99% and 97.15%, respectively