Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy

Evolution and Recent Radiation Therapy Advancement in Uganda: A Precedent on How to Increase Access to Quality Radiotherapy Services in Low- and Middle-Income Countries

The evolution of radiation therapy in Uganda has been a journey marked by significant milestones and persistent challenges. Since the inception of radiotherapy services in 1988-1989, there has been a concerted effort to enhance cancer treatment services. The early years were characterized by foundational developments, such as the installation of the first teletherapy units, low-dose-rate brachytherapy units, and conventional simulators, and the recognition of radiation oncologists and medical physicist professionals laid the groundwork for radiotherapy treatment modalities. With more support from the International Atomic Energy Agency, the acquisition of dosimetry equipment, treatment planning systems, and additional professional training signaled a new era in the fight against cancer. As we entered the second decade of the millennium, the Uganda Cancer Institute (UCI) witnessed a progression in sophisticated radiotherapy services, including high-dose-rate brachytherapy, initiation of intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT), and use of artificial intelligence. These advancements improved the efficiency/precision of treatments and the time patients spent undergoing therapy. Around the second decade of radiotherapy services, about 600 new patients with cancer were annually treated compared with about 2,600 in 2023. Currently, an average of 1,440 brachytherapy insertions are done annually compared with 300 insertions for the first 20 years. Despite the technological strides, the UCI faced numerous obstacles, including limited equipment, knowledge gaps in appropriate tumor/organs at risk segmentations, treatment planning, and protocols. However, international support and collaboration efforts have led to significant improvement in the precision and effectiveness of treatments. Currently, about 51% of all patients are treated with image-guided techniques-IMRT/VMAT (42%) and three-dimensional conformal radiation treatment (10%). The Government has commenced the decentralization of radiotherapy services to other regions. This review can be a learning lesson for the more than 25 countries in Africa and other low-middle-income countries globally that do not have access to radiotherapy and/or are in the process of starting such facilities.

Simultaneous reduction of number of spots and energy layers in intensity modulated proton therapy for rapid spot scanning delivery

BACKGROUND

Reducing proton treatment time improves patient comfort and decreases the risk of error from intrafractional motion, but must be balanced against clinical goals and treatment plan quality.

PURPOSE

To improve the delivery efficiency of spot scanning proton therapy by simultaneously reducing the number of spots and energy layers using the reweightedl 1 $l_1$ regularization method.

METHODS

We formulated the proton treatment planning problem as a convex optimization problem with a cost function consisting of a dosimetric plan quality term plus a weightedl 1 $l_1$ regularization term. We iteratively solved this problem and adaptively updated the regularization weights to promote the sparsity of both the spots and energy layers. The proposed algorithm was tested on four head-and-neck cancer patients, and its performance, in terms of reducing the number of spots and energy layers, was compared with existing standardl 1 $l_1$ and groupl 2 $l_2$ regularization methods. We also compared the effectiveness of the three methods (l 1 $l_1$ , groupl 2 $l_2$ , and reweightedl 1 $l_1$ ) at improving plan delivery efficiency without compromising dosimetric plan quality by constructing each of their Pareto surfaces charting the trade-off between plan delivery and plan quality.

RESULTS

The reweightedl 1 $l_1$ regularization method reduced the number of spots and energy layers by an average over all patients of40 % $40\%$ and35 % $35\%$ , respectively, with an insignificant cost to dosimetric plan quality. From the Pareto surfaces, it is clear that reweightedl 1 $l_1$ provided a better trade-off between plan delivery efficiency and dosimetric plan quality than standardl 1 $l_1$ or groupl 2 $l_2$ regularization, requiring the lowest cost to quality to achieve any given level of delivery efficiency.

CONCLUSIONS

Reweightedl 1 $l_1$ regularization is a powerful method for simultaneously promoting the sparsity of spots and energy layers at a small cost to dosimetric plan quality. This sparsity reduces the time required for spot scanning and energy layer switching, thereby improving the delivery efficiency of proton plans.

Natural Guardians: Natural Compounds as Radioprotectors in Cancer Therapy

Cancer remains a significant global health challenge, with millions of deaths attributed to it annually. Radiotherapy, a cornerstone in cancer treatment, aims to destroy cancer cells while minimizing harm to healthy tissues. However, the harmful effects of irradiation on normal cells present a formidable obstacle. To mitigate these effects, researchers have explored using radioprotectors and mitigators, including natural compounds derived from secondary plant metabolites. This review outlines the diverse classes of natural compounds, elucidating their roles as protectants of healthy cells. Furthermore, the review highlights the potential of these compounds as radioprotective agents capable of enhancing the body's resilience to radiation therapy. By integrating natural radioprotectors into cancer treatment regimens, clinicians may improve therapeutic outcomes while minimizing the adverse effects on healthy tissues. Ongoing research in this area holds promise for developing complementary strategies to optimize radiotherapy efficacy and enhance patient quality of life.

A Systematic Review of LET-Guided Treatment Plan Optimisation in Proton Therapy: Identifying the Current State and Future Needs

The well-known clinical benefits of proton therapy are achieved through higher target-conformality and normal tissue sparing than conventional radiotherapy. However, there is an increased sensitivity to uncertainties in patient motion/setup, proton range and radiobiological effect. Although recent efforts have mitigated some uncertainties, radiobiological effect remains unresolved due to a lack of clinical data for relevant endpoints. Therefore, RBE optimisations may be currently unsuitable for clinical treatment planning. LET optimisation is a novel method that substitutes RBE with LET, shifting LET hotspots outside critical structures. This review outlines the current status of LET optimisation in proton therapy, highlighting knowledge gaps and possible future research. Following the PRISMA 2020 guidelines, a search of the MEDLINE® and Scopus databases was performed in July 2023, identifying 70 relevant articles. Generally, LET optimisation methods achieved their treatment objectives; however, clinical benefit is patient-dependent. Inconsistencies in the reported data suggest further testing is required to identify therapeutically favourable methods. We discuss the methods which are suitable for near-future clinical deployment, with fast computation times and compatibility with existing treatment protocols. Although there is some clinical evidence of a correlation between high LET and adverse effects, further developments are needed to inform future patient selection protocols for widespread application of LET optimisation in proton therapy.

Built-in wavelet-induced smoothness to reduce plan complexity in intensity modulated radiation therapy (IMRT)

Objective.Reducing plan complexity in intensity modulated radiation therapy (IMRT) to ensure dosimetric accuracy and delivery efficiency of the radiation treatment plans. We propose a novel approach by representing the beamlet intensities using an incomplete wavelet basis that explicitly excludes fluctuating intensity maps from the decision space (explicit hard constraint). This technique provides a built-in wavelet-induced smoothness that improves both dosimetric plan quality and delivery efficiency.Approach.The beamlet intensity maps need to be especially smooth in the leaf travel direction (referred to as theX-direction). We treat the intensity map of each beam as a 2D image and represent it using the wavelets corresponding to low-frequency changes in theX-direction (i.e. approximation and horizontal). The absence of wavelets corresponding to high-frequency changes (i.e. vertical and diagonal) induces built-in smoothness. We still utilize a regularization term in the objective function to promote smoothness in theY-direction (perpendicular to theX-direction) and further possible smoothness in theX-direction. This technique has been tested on three patient cases of different disease sites (paraspinal, lung, prostate) and all final evaluations and comparisons have been performed on an FDA-approved commercial treatment planning system (Varian Eclipse^TM).Main results.Wavelet-induced smoothness reduced monitor units by about 10%, 45%, and 14% for paraspinal, lung, and prostate cases, respectively. It also improved organ at risk sparing, especially on the complex paraspinal case where it resulted in about 7%, 13%, and 14% less mean dose to esophagus, lung, and cord, respectively. Moreover, built-in wavelet-induced smoothness desensitizes the results to changing the weight associated to the regularization term, and thereby mitigates the weight fine-tuning difficulty.Significance.Fluence smoothness is often achieved by smoothing the beamlet intensity maps using a proper regularization term in the objective function aiming at disincentivizing fluctuation in the beamlet intensities (implicit soft constraint). This work reports a novel application of wavelets in imposing an explicit smoothness hard constraint in the search space using an incomplete wavelet basis. This idea has been successfully applied to exclude complex and clinically irrelevant radiation plans from the search space. The code and pertained models along with a sample dataset are released on our LowDimRT GitHub (https://github.com/PortPy-Project/LowDimRT).