Analyzing the Usage of Standards in Radiation Therapy Clinical Studies

Predictors of radiation-induced late rectal toxicity in prostate cancer treatment: a volumetric and dosimetric analysis

Introduction

Prostate cancer (PCa) is a prevalent malignancy in European men, often treated with radiotherapy (RT) for localized disease. While modern RT achieves high success rates, concerns about late gastrointestinal (GI) toxicities persist. This retrospective study aims to identify predictors for late GI toxicities following definitive conventionally fractionated external beam RT (EBRT) for PCa, specifically exploring the dose to the rectal wall.

Materials and methods

A cohort of 96 intermediate- to high-risk PCa patients underwent EBRT between 2008 and 2016. Rectum and rectum wall contours were delineated, and 3D dose matrices were extracted. Volumetric and dosimetric indices were computed, and statistical analyses were performed to identify predictors using the Mann-Whitney U-rank test, logistic regression, and recursive feature elimination.

Results

In our cohort, 15 out of 96 patients experienced grade II late proctitis. Our analysis reveals distinct optimal predictors for rectum and rectum wall (RW) structures varying with α/β values (3.0 and 2.3 Gy) across prescribed doses of 68 to 76 Gy. Despite variability, RW predictors demonstrate greater consistency, notably V68Gy[%] to V74Gy[%] for α/β 3.0 Gy, and V68Gy[%] to V70Gy[%] for α/β 2.3 Gy. The model with α/β 2.3 Gy, featuring RW volume receiving 70 Gy (V70Gy[%]), stands out with a BIC value of 62.92, indicating its superior predictive effectiveness. Finally, focusing solely on the rectum structure, the V74Gy[%] emerges the best predictor for α/β 3.0 Gy, with a BIC value of 66.73.

Conclusion

This investigation highlights the critical role of V70Gy[%] in the rectum wall as a robust predictor for grade II late gastrointestinal (GI) toxicity following external beam radiation therapy (EBRT) for prostate cancer (PCa). Furthermore, our findings suggest that focusing on the rectum wall specifically, rather than the entire rectum, may offer improved accuracy in assessing proctitis development. A V70Gy (in EQD2 with α/β 2.3 Gy) of ≤5% and if possible ≤1% for the rectal wall should be achieved to minimize the risk of late grade II proctitis.

Overview of radiotherapy-induced chronic pain in childhood cancer survivors: A narrative review

Radiotherapy is an important aspect of oncological treatment in several childhood cancers. However, radiotherapy is known to have numerous side effects, including detrimental effects on growth, neurocognitive impairment, and the development of secondary malignancies. One less studied long-term side effect of pediatric radiotherapy treatment is chronic pain. While the short-term toxicities of radiotherapy resolve over a few weeks to months, the chronic pain caused by radiotherapy-induced tissue damage can significantly affect children's quality of life. As long-term childhood cancer survivors age into adulthood, they are typically followed up by a wide variety of doctors, not all of whom may be familiar with radiotherapy-induced chronic pain and its management. The aim of this review is to discuss the various common manifestations of radiotherapy-related pain in children, as well as ways to identify and manage these. Common radiotherapy-related side effects leading to chronic pain symptoms include radiation fibrosis, enteritis, dermatitis, lymphedema, neuropathic pain, and effects on bone development. The pathophysiology, evaluation and management of these are briefly summarized in this review. This is followed by an overview of radiotherapy techniques that allow greater sparing of normal tissue, minimizing future painful side effects. Finally, the assessment of pain in children is described, as well as strategies for management, and red flag symptoms that should prompt urgent specialist referral. In conclusion, a good understanding of the long-term side effects of radiotherapy treatment in children is essential for the various medical professionals that follow-up the child in the years after treatment. For young children, the evaluation of pain is in itself a challenge, and effects on growth, development, and learning are crucial. For older children, social and psychological factors become increasingly important. As radiation therapy techniques continue to advance, the spectrum and incidence of chronic pain syndromes may change over time.

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning. With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: dose-volume histogram (DVH), Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.

The increasing potential of nuclear medicine imaging for the evaluation and reduction of normal tissue toxicity from radiation treatments

Radiation therapy is an effective treatment modality for a variety of cancers. Despite several advances in delivery techniques, its main drawback remains the deposition of dose in normal tissues which can result in toxicity. Common practices of evaluating toxicity, using questionnaires and grading systems, provide little underlying information beyond subjective scores, and this can limit further optimization of treatment strategies. Nuclear medicine imaging techniques can be utilised to directly measure regional baseline function and function loss from internal/external radiation therapy within normal tissues in an in vivo setting with high spatial resolution. This can be correlated with dose delivered by radiotherapy techniques to establish objective dose-effect relationships, and can also be used in the treatment planning step to spare normal tissues more efficiently. Toxicity in radionuclide therapy typically occurs due to undesired off-target uptake in normal tissues. Molecular imaging using diagnostic analogues of therapeutic radionuclides can be used to test various interventional protective strategies that can potentially reduce this normal tissue uptake without compromising tumour uptake. We provide an overview of the existing literature on these applications of nuclear medicine imaging in diverse normal tissue types utilising various tracers, and discuss its future potential.

Clinical Outcomes of Dose-escalated Radiotherapy for Localised Prostate Cancer: A Single-institution Experience

BACKGROUND/AIM

To report the outcomes of patients with prostate cancer treated with dose-escalated radiotherapy over a 15-year period at our Institution.

PATIENTS AND METHODS

Patients with biopsy-proven cT1-4N0M0 disease who received radical external beam radiotherapy (EBRT) were reviewed. The endpoints were 5-year overall survival (OS), freedom from biochemical failure (FFBF) and late treatment toxicities.

RESULTS

A total of 236 patients were eligible. Median follow-up was 70 months. Low-, intermediate- and high-risk disease was found in 9%; 29% and 62% of patients, respectively. The median radiation dose was 73.8 Gy. Overall 42% of patients had dose escalation to >74 Gy. Five-year OS and FFBF were 95.2%/81.6%/75.4% and 95.0%/98.0%/82.0% for low-/intermediate-/high-risk patients, respectively. Dose escalation to >74 Gy did not improve FFBF (hazard ratio=0.97, 95% confidence intervaI=0.43-2.19, p=0.93) and was associated with a 4.3-fold increase in the odds of grade 3 or more rectal bleeding (p<0.01).

CONCLUSION

Dose escalation to >74 Gy did not improve OS or FFBF but was associated with a higher rate of grade 3 or more rectal haemorrhage.