Clinical Introduction of Stem Cell Sparing Radiotherapy to Reduce the Risk of Xerostomia in Patients with Head and Neck Cancer.

Background/objectives: Studies have shown that dose to the parotid gland stem cell rich (SCR) regions should be reduced to lower the risk of xerostomia after radiotherapy (RT). This study aimed to assess whether stem cell sparing (SCS)-RT can be adopted in routine clinical practice.

Methods: Multiple planning studies were performed to compare SCS-RT with standard (ST)-RT using 30 head and neck cancer patients.

Challenges and opportunities in stereotactic body proton radiotherapy of liver malignancies.

Stereotactic body proton radiotherapy (SBPT) has the potential to be an effective tool for treating liver malignancies. While proton therapy enables near-zero exit dose and could improve normal tissue sparing, including liver and other surrounding structures, there are challenges in implementing the SBPT technique for proton therapy, including respiratory motion, range uncertainties, dose regimen, treatment planning, and image guidance. This article summarizes the technical and clinical challenges facing SBPT, along with the potential benefits of SBPT for liver malignancies.

Daily Head and Neck Treatment Assessment for Optimal Proton Therapy Planning Robustness.

Robust optimization in proton therapy ensures adequate target coverage; however, validation of fractional plan quality and setup uncertainty in patients has not been performed. We aimed to assess plan robustness on delivered head and neck proton plans classified into two categories: (1) primary only (PO) and (2) primary and neck nodal (PNN) coverage. Registration at the machine was utilized for daily CBCT to generate a synthetic CT.

The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation.

Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images.

Bayesian feature selection for radiomics using reliability metrics.

Imaging of tumors is a standard step in diagnosing cancer and making subsequent treatment decisions. The field of radiomics aims to develop imaging based biomarkers using methods rooted in artificial intelligence applied to medical imaging. However, a challenging aspect of developing predictive models for clinical use is that many quantitative features derived from image data exhibit instability or lack of reproducibility across different imaging systems or image-processing pipelines.

Planning and Treatment Recommendations for Breast Proton Therapy From a Single Center's Experience.

Purpose: Proton therapy use for breast cancer has grown due to advantages in coverage and potentially reduced late toxicities compared with conventional radiation therapy. We aimed to provide recommendations for robustness criteria, daily imaging, and quality assurance computed tomography (QA CT) frequency for these patients.

Methods And Materials: All patients treated for localized breast cancer at the Johns Hopkins Proton Center between November 2019 and February 2022 were eligible for inclusion.

Evaluating Proton Dose and Associated Range Uncertainty Using Daily Cone-Beam CT.

Purpose: This study aimed to quantitatively evaluate the range uncertainties that arise from daily cone-beam CT (CBCT) images for proton dose calculation compared to CT using a measurement-based technique.

Methods: For head and thorax phantoms, wedge-shaped intensity-modulated proton therapy (IMPT) treatment plans were created such that the gradient of the wedge intersected and was measured with a 2D ion chamber array. The measured 2D dose distributions were compared with 2D dose planes extracted from the dose distributions using the IMPT plan calculated on CT and CBCT.

Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer.

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics).

Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms.

Purpose: To investigate the impact of computed tomography (CT) image acquisition and reconstruction parameters, including slice thickness, pixel size, and dose, on automatic contouring algorithms.

Methods: Eleven scans from patients with head-and-neck cancer were reconstructed with varying slice thicknesses and pixel sizes. CT dose was varied by adding noise using low-dose simulation software.

Radiomics feature robustness as measured using an MRI phantom.

Radiomics involves high-throughput extraction of large numbers of quantitative features from medical images and analysis of these features to predict patients' outcome and support clinical decision-making. However, radiomics features are sensitive to several factors, including scanning protocols. The purpose of this study was to investigate the robustness of magnetic resonance imaging (MRI) radiomics features with various MRI scanning protocol parameters and scanners using an MRI radiomics phantom.

Simultaneously spatial and temporal Higher-Order Total Variations for noise suppression and motion reduction in DCE and IVIM.

In many applications based on kinetic evaluation analysis and model fitting, quantitative mapping retrieved on data series from modalites such as MRI is completed on a voxel-by-voxel basis, where motion and low signal to noise ratio (SNR) would considerably degenerate the reliability of estimations. The coherence of image series in space and time can be used as prior knowledge to mitigate this occurrence. In this study, spatial and temporal higher-order total variations (HOTVs) are applied on a data series of MRI signal (e.

Multi-energy computed tomography and material quantification: Current barriers and opportunities for advancement.

Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose.

Technical Note: Proof of concept for radiomics-based quality assurance for computed tomography.

Purpose: Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA.

Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients.

Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models.

Effects of alterations in positron emission tomography imaging parameters on radiomics features.

Radiomics studies require large patient cohorts, which often include patients imaged using different imaging protocols. We aimed to determine the impact of variability in imaging protocol parameters and interscanner variability using a phantom that produced feature values similar to those of patients. Positron emission tomography (PET) scans of a Hoffman brain phantom were acquired on GE Discovery 710, Siemens mCT, and Philips Vereos scanners.

Dosimetric impact and detectability of multi-leaf collimator positioning errors on Varian Halcyon.

The purpose of this study is to investigate the dosimetric impact of multi-leaf collimator (MLC) positioning errors on a Varian Halcyon for both random and systematic errors, and to evaluate the effectiveness of portal dosimetry quality assurance in catching clinically significant changes caused by these errors. Both random and systematic errors were purposely added to 11 physician-approved head and neck volumetric modulated arc therapy (VMAT) treatment plans, yielding a total of 99 unique plans. Plans were then delivered on a preclinical Varian Halcyon linear accelerator and the fluence was captured by an opposed portal dosimeter.

Matching and Homogenizing Convolution Kernels for Quantitative Studies in Computed Tomography.

The sharpness of the kernels used for image reconstruction in computed tomography affects the values of the quantitative image features. We sought to identify the kernels that produce similar feature values to enable a more effective comparison of images produced using scanners from different manufactures. We also investigated a new image filter designed to change the kernel-related component of the frequency spectrum of a postreconstruction image from that of the initial kernel to that of a preferred kernel.

Design, fabrication, and validation of patient-specific electron tissue compensators for postmastectomy radiation therapy.

Background And Purpose: Postmastectomy radiotherapy (PMRT) is complex to plan and deliver, but could be improved with 3D-printed, patient-specific electron tissue compensators. The purposes of this study were to develop an algorithm to design patient-specific compensators that achieve clinical goals, to 3D-print the planned compensators, and validate calculated dose distributions with film and thermoluminescent dosimeter (TLD) measurements in 3D-printed phantoms of PMRT patients.

Materials And Methods: An iterative algorithm was developed to design compensators corresponding to single-field, single-energy electron plans for PMRT patients.

A snapshot of medical physics practice patterns.

A large number of surveys have been sent to the medical physics community addressing many clinical topics for which the medical physicist is, or may be, responsible. Each survey provides an insight into clinical practice relevant to the medical physics community. The goal of this study was to create a summary of these surveys giving a snapshot of clinical practice patterns.

Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis.

Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone.

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges.

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated.

Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies.

Radiomics has shown promise in improving models for predicting patient outcomes. However, to maximize the information gain of the radiomics features, especially in larger patient cohorts, the variability in radiomics features owing to differences between scanners and scanning protocols must be accounted for. To this aim, the imaging variability of radiomics feature values was evaluated on 100 computed tomography scanners at 35 clinics by imaging a radiomics phantom using a controlled protocol and the commonly used chest and head protocols of the local clinic.

Effect of tube current on computed tomography radiomic features.

Variability in the x-ray tube current used in computed tomography may affect quantitative features extracted from the images. To investigate these effects, we scanned the Credence Cartridge Radiomics phantom 12 times, varying the tube current from 25 to 300 mA∙s while keeping the other acquisition parameters constant. For each of the scans, we extracted 48 radiomic features from the categories of intensity histogram (n = 10), gray-level run length matrix (n = 11), gray-level co-occurrence matrix (n = 22), and neighborhood gray tone difference matrix (n = 5).