État des lieux de la radiothérapie adaptative en 2019 : de la mise en place à l’utilisation clinique

The Current use of Adaptive Strategies for External Beam Radiotherapy in Cervical Cancer: A Systematic Review

AIMS

Variability in the target and organs at risk (OARs) in cervical cancer treatment presents challenges for precise radiotherapy. Adaptive radiotherapy (ART) offers the potential to enhance treatment precision and outcomes. However, the increased workload and a lack of consensus on the most suitable ART approach hinder its clinical adoption. This systematic review aims to assess the current use of adaptive strategies for cervical cancer and define the optimal approach.

MATERIALS AND METHODS

A systematic review of current literature published between January 2012 and May 2023 was conducted. Searches used PubMed/Medline, Cochrane Library, and Web of Science databases, supplemented with the University of Manchester, Google Scholar, and papers retrieved from reference lists. The review assessed workflows, compared dosimetric benefits, and examined resources for each identified strategy. Excluded were abstracts, conference abstracts, reviews, articles unrelated to ART management, proton therapy, brachytherapy, or qualitative studies. A narrative synthesis involved data tabulation, summarizing selected studies detailing workflow for cervical cancer and dosimetric outcomes for targets and OARs.

RESULTS

Sixteen articles met the inclusion criteria; these were mostly retrospective simulation planning studies, except four studies that had been clinically implemented. We identified five approaches for ART radiotherapy for cervical cancer: reactive and scheduled adaptation, internal target volume (ITV)-based approach using library of plans (LOP), fixed-margin approach using LOP, and real-time adaptation, with each approach reducing irradiated volumes without compromising target coverage compared to the non-ART approach. The LOP-based ITV approach is the most used and clinically assessed.

CONCLUSION

Identifying the optimal strategy is challenging due to dosimetric assessment limitations. Implementing cervical cancer ART necessitates strategic optimization of clinical benefits and resources through research, including studies to identify the optimal frequency, and prospective evaluations of toxicity.

Personalizing radiotherapy with adaptive radiotherapy: Interest and challenges

Adaptive radiotherapy (ART) is a recent development in radiotherapy technology and treatment personalization that allows treatment to be tailored to the daily anatomical changes of patients. While it was until recently only performed "offline", i.e. between two radiotherapy sessions, it is now possible during ART to perform a daily online adaptive process for a given patient. Therefore, ART allows a daily customization to ensure optimal coverage of the treatment target volumes with minimized margins, taking into account only the uncertainties related to the adaptive process itself. This optimization appears particularly relevant in case of daily variations in the positioning of the target volume or of the organs at risk (OAR) associated with a proximity of these volumes and a tenuous therapeutic index. ART aims to minimize severe acute and late toxicity and allows tumor dose escalation. These new achievements have been possible thanks to technological development, the contribution of new multimodal and onboard imaging modalities and the integration of artificial intelligence tools for the contouring, planning and delivery of radiation therapy. Online ART is currently available on two types of radiotherapy machines: MR-linear accelerators and recently CBCT-linear accelerators. We will first describe the benefits, advantages, constraints and limitations of each of these two modalities, as well as the online adaptive process itself. We will then evaluate the clinical situations for which online adaptive radiotherapy is particularly indicated on MR- and CBCT-linear accelerators. Finally, we will detail some challenges and possible solutions in the development of online ART in the coming years.

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer

PURPOSE

To generate and investigate a supervised deep learning algorithm for creating synthetic computed tomography (sCT) images from kilovoltage cone-beam computed tomography (kV-CBCT) images for adaptive radiation therapy (ART) in head and neck cancer (HNC).

MATERIALS AND METHODS

This study generated the supervised U-Net deep learning model using 3,491 image pairs from planning computed tomography (pCT) and kV-CBCT datasets obtained from 40 HNC patients. The dataset was split into 80% for training and 20% for testing. The evaluation of the sCT images compared to pCT images focused on three aspects: Hounsfield units accuracy, assessed using mean absolute error (MAE) and root mean square error (RMSE); image quality, evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between sCT and pCT images; and dosimetric accuracy, encompassing 3D gamma passing rates for dose distribution and percentage dose difference.

RESULTS

MAE, RMSE, PSNR, and SSIM showed improvements from their initial values of 53.15 ± 40.09, 153.99 ± 79.78, 47.91 ± 4.98 dB, and 0.97 ± 0.02 to 41.47 ± 30.59, 130.39 ± 78.06, 49.93 ± 6.00 dB, and 0.98 ± 0.02, respectively. Regarding dose evaluation, 3D gamma passing rates for dose distribution within sCT images under 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria, yielded passing rates of 92.1% ± 3.8%, 93.8% ± 3.0%, and 96.9% ± 2.0%, respectively. The sCT images exhibited minor variations in the percentage dose distribution of the investigated target and structure volumes. However, it is worth noting that the sCT images exhibited anatomical variations when compared to the pCT images.

CONCLUSION

These findings highlight the potential of the supervised U-Net deep learningmodel in generating kV-CBCT-based sCT images for ART in patients with HNC.

Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.

[What do we need to deliver "online" adapted radiotherapy treatment plans?]

During the joint SFRO/SFPM session of the 2019 congress, a state of the art of adaptive radiotherapy announced a strong impact in our clinical practice, in particular with the availability of treatment devices coupled to an MRI system. Three years later, it seems relevant to take stock of adaptive radiotherapy in practice, and especially the "online" strategy because it is indeed more and more accessible with recent hardware and software developments, such as coupled accelerators to a three-dimensional imaging device and algorithms based on artificial intelligence. However, the deployment of this promising strategy is complex because it contracts the usual time scale and upsets the usual organizations. So what do we need to deliver adapted treatment plans with an "online" strategy?

Poly-Lactide-Co-Glycolide-Polyethylene Glycol-Ginsenoside Rg3-Ag Exerts a Radio-Sensitization Effect in Non-Small Cell Lung Cancer

Radiotherapy is an effective anti-cancer therapy for patients with non-small cell lung cancer (NSCLC), however, the prognosis is unsatisfactory owing to radio-resistance and toxicity. It is crucial to improve radiotherapy efficacy. Ag nanoparticles (NPs) and ginsenoside Rg3 (Rg3) exerted antitumor and radio-sensitization effects. Therefore, we investigated whether poly-lactide-co-glycolide-polyethylene glycol (PLGA-PEG)-Rg3-Ag will function as a noninvasive, tracing, radiotherapy sensitizer. The morphology of NPs was visualized with transmission electron microscopy (TEM). The drug loading content, encapsulation efficiency, and cumulative drug release of Rg3 was determined by HPLC. Cellular uptake of NPs in A549 and SPCA-1 was measured by immunostaining. The radio-sensitization effect of PLGA-PEG-Rg3-Ag in vitro was determined in A549 by detecting proliferation, colony formation, and apoptosis with CCK-8, clonogenic survival assay, and flow cytometry, while in vivo was determined in nude mice by testing the body weight and tumor volume. PLGA-PEG-Rg3-Ag exerted radio-sensitization effect by reducing cell proliferation and colony formation while enhancing cell apoptosis in A549; reduced tumor volume in nude mice. PLGA-PEG-Rg3-Ag exhibits radio-sensitization effects in NSCLC.

Image-guided radiotherapy

We present the updated recommendations of the French society for oncological radiotherapy on image-guided radiotherapy (IGRT). The objective of the IGRT is to take into account the anatomical variations of the target volume occurring between or during the irradiation fractions, such as displacements and/or deformations, so that the delivered dose corresponds to the planned dose. This article presents the different IGRT devices, their use and quality control, and quantify the possible additional dose generated by each of them. The practical implementation of IGRT in various tumour locations is summarised, from the different "RecoRad™" guideline articles. Adaptive radiotherapy is then detailed, due to its complexity and its probable development in the next years. The place of radiation technologist in the practice of IGRT is then specified. Finally, a brief update is proposed on the delicate question of the additional dose linked to the in-room imaging, which must be estimated and documented at a minimum, as long as it is difficult to integrate it into the calculation of the dose distribution.

Precision radiotherapy for non-small cell lung cancer

Precision medicine is becoming the standard of care in anti-cancer treatment. The personalized precision management of cancer patients highly relies on the improvement of new technology in next generation sequencing and high-throughput big data processing for biological and radiographic information.

Systemic precision cancer therapy has been developed for years. However, the role of precision medicine in radiotherapy has not yet been fully implemented. Emerging evidence has shown that precision radiotherapy for cancer patients is possible with recent advances in new radiotherapy technologies, panomics, radiomics and dosiomics.

This review focused on the role of precision radiotherapy in non-small cell lung cancer and demonstrated the current landscape.

Multimodal imaging in radiotherapy: Focus on adaptive therapy and quality control

Improved computer resources in radiation oncology department have greatly facilitated the integration of multimodal imaging into the workflow of radiation therapy. Nowadays, physicians have highly informative imaging modalities of the anatomical region to be treated. These images contribute to the targeting accuracy with the current treatment device, impacting both segmentation or patient's positioning. Additionally, in a constant effort to deliver personalized care, many teams seek to confirm the benefits of adaptive radiotherapy. The published works highlight the importance of registration algorithms, particularly those of elastic or deformable registration necessary to take into account the anatomical evolutions of the patients during the course of their therapy. These algorithms, often considered as "black boxes", tend to be better controlled and understood by physicists and physicians thanks to the generalization of evaluation and validation methods. Given the still significant development of medical imaging techniques, it is foreseeable that multimodal registration needs require more efficient algorithms well integrated within the flow of data.