Although proton relative biological effectiveness (RBE) depends on factors like linear energy transfer (LET), tissue properties, dose, and biological endpoint, a constant RBE of 1.1 is recommended in clinical practice. This study surveys proton institutions to explore activities using functionalities beyond a constant proton RBE.

To investigate the effect of ion recombination ( ) and polarity ( ) correction factors on percentage depth dose (PDD) curves for three ion chambers, using flat and flattening filter free (FFF) beams, across different broad field sizes. A method to assess these effects and their corresponding corrections is proposed.

In pancreatic radiotherapy, residual tumor motion during treatment increases the risk of toxicity. Cine imaging acquired during magnetic resonance guided radiotherapy (MRgRT) enables real-time treatment gating in response to anatomical motion, which can reduce this risk; however, treatment gating can negatively impact the efficiency of treatment. This study aimed to quantify the extent of residual tumor motion during breath hold and evaluate the appropriateness of the treatment gating margins used in current clinical practice.

The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various treatment contexts, focusing on treatment outcomes such as patient survival, disease recurrence, and treatment-induced toxicity. It emphasizes the ascending trajectory of research efforts and the prominence of survival analysis as a clinical priority. We analyze the use of several common medical imaging modalities in conjunction with clinical data, highlighting the advantages and complexities inherent in this approach. The research reflects a commitment to advancing patient-centered care, advocating for expanded research on abdominal and pancreatic cancers. While data collection, patient privacy, standardization, and interpretability present significant challenges, leveraging ML in radiotherapy holds remarkable promise for elevating precision medicine and improving patient care outcomes.

To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-neck (HN) Intensity Modulated Radiation Therapy (IMRT).

This study aimed to develop and evaluate the validity and reliability of dentists' radiation protection knowledge, attitudes, and practices (DRP-KAPs) questionnaire.

MRI-guided adaptive radiotherapy can directly monitor the anatomical positioning of the intended target during treatment with no additional imaging dose. Elekta has recently released its comprehensive motion management (CMM) solution that enables automatic radiation beam-gating on the Unity MR-Linac. Easily visualized targets that are distinct from the surrounding anatomy can be used to drive automatic gating decisions from the MRI cine imaging. However, poorly visualized targets can compromise the tracking and gating capabilities and may require surrogate tracking structures. This work presents strategies to generate robust tracking surrogates for a variety of treatment sites, enabling a wider application of CMM.

This study investigates potential failure modes and conducts failure mode and effects analysis (FMEA) and fault tree analysis (FTA) on the administration of DOTATATE (LUTATHERA) and PSMA-617 (PLUVICTO). The quality management (QM) process in radiopharmaceutical therapies (RPTs) requires collaboration between nuclear medicine (NM) and radiation oncology (RO) departments. As part of a multi-institutional study, we surveyed various departments to identify and analyze failure modes, leading to a proposed comprehensive QM program. RPT teams in RO or NM clinics can benefit from this study by continually improving their practice.

We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.

Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, but development has primarily been on external beam radiation therapy. Thus, there is a need for translation to brachytherapy.

The routine patient arm position differs between coronary CT angiography (CTA) and craniocervical CTA protocols. To investigate the clinical feasibility of supported bridge position (SBP) in combined coronary and craniocervical CTA.

To compare the effect of two linacs designs on fetal dose sparing on a pregnant patient, including estimation of the fetal dose, and the effect of a lead apron.

A new approach to analysis of prostate hybrid multidimensional MRI (HM-MRI) data was introduced in this study. HM-MRI data were acquired for a combination of a few echo times (TEs) and a few b-values. Naturally, there is a matrix associated with HM-MRI data for each image pixel. To process the data, we first linearized HM-MRI data by taking the natural logarithm of the imaging signal intensity. Subsequently, a hybrid symmetric matrix was constructed by multiplying the matrix for each pixel by its own transpose. The eigenvalues for each pixel could then be calculated from the hybrid symmetric matrix. In order to compare eigenvalues between patients, three b-values and three TEs were used, because this was smallest number of b-values and TEs among all patients. The results of eigenvalues were displayed as qualitative color maps for easier visualization. For quantitative analysis, the ratio (λ) of eigenvalues (λ, λ, λ) was defined as λ = (λ/λ)/λ to compare region of interest (ROI) between prostate cancer (PCa) and normal tissue. The results show that the combined eigenvalue maps show PCas clearly and these maps are quite different from apparent diffusion coefficient (ADC) and T2 maps of the same prostate. The PCa has significant larger λ, smaller ADC and smaller T2 values than normal prostate tissue (p < 0.001). This suggests that the matrix-based method for analyzing HM-MRI data provides new information that may be clinically useful. The method is easy to use and could be easily implemented in clinical practice. The eigenvalues are associated with combination of ADC and T2 values, and could aid in the identification and staging of PCa.

Brain metastases are the most common intracranial malignancy and remain a substantial source of morbidity and mortality in cancer patients. Linear accelerator based stereotactic radiosurgery (SRS) is widely used and is frequently delivered by hypo-fractionnated volumetric modulated arc therapy using non-coplanar beams, where geometric accuracy and planning margins are a major concern.

This paper provides a pedagogical example for systematic machine learning optimization in small dataset image segmentation, emphasizing hyperparameter selections. A simple process is presented for medical physicists to examine hyperparameter optimization. This is also applied to a case-study, demonstrating the benefit of the method.

To explore the accuracy and feasibility of a proposed deep learning (DL) algorithm for clinical target volume (CTV) delineation in cervical cancer radiotherapy and evaluate whether it can perform well in external cervical cancer and endometrial cancer cases for generalization validation.

The goal of this manuscript is to evaluate strengths and weaknesses of a 3-year medical physics residency program with the first year dedicated to research and the remaining 2 years dedicated to clinical training.

To develop and implement a fully automatic iterative planning (AIP) system in the clinical practice, generating volumetric-modulated arc therapy plans combined with simultaneous integrated boost technique VMAT (SIB-VMAT) for locally advanced rectal cancer (LARC) patients.

We developed a software program for swiftly calculating dose distributions for carbon ion beams. This study aims to evaluate the accuracy of dose calculations using this software and assess the robustness of dose distribution in treating prostate cancer.

In craniospinal irradiation, two or three isocenter groups along the craniocaudal axis are required to cover the long treatment target. Adapting the isocenter distance according to daily deviations in patient position is challenging because dosimetric hot or cold spots may occur in the field junction. The aim of this study was to quantify the effect of adapting the isocenter distance to patient position on the dose distribution of the field overlap region in craniospinal irradiation using partial-arc volumetric modulated arc therapy.