The Development of Volumetric Quantitative Evaluation Software for Assessing Respiratory-Induced Target Motion.

Purpose We developed a volumetric quantitative evaluation software called vector volume histogram (VVH) to evaluate respiratory-induced organ motion using deformable image registration (DIR). Methods The B-spline-based DIR algorithm was used to compute the deformation vector field (DVF), which included the DVF (left-right), DVF (anterior-posterior), and DVF (craniocaudal). The VVH software was written as a plug-in using Python, thus allowing anyone to easily modify the code.

Evaluation of monthly variations in electrometer calibration coefficients using a charge generator for radiation therapy.

Electrometers are important devices that are part of the standard dosimetry system. Therefore, we evaluated the variation of electrometer calibration coefficients (k) over 1 year in this study. We investigated two types of electrometers: a rate mode and an integrate mode.

Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset.

Lateral response artifact correction method using image stitching technique in radiochromic film dosimetry.

Purpose: Lateral response artifact (LRA) is caused by the interaction between film and flatbed scanner in the direction perpendicular to the scanning direction. This can significantly affect the accuracy of patient-specific quality assurance (QA) in cases involving large irradiation fields. We hypothesized that by utilizing the central area of the flatbed scanner, where the magnitude of LRA is relatively small, the LRA could be mitigated effectively.

Application of stoichiometric CT number calibration method for dose calculation of tissue heterogeneous volumes in boron neutron capture therapy.

Background: Monte Carlo simulation code is commonly used for the dose calculation of boron neutron capture therapy. In the past, dose calculation was performed assuming a homogeneous mass density and elemental composition inside the tissue, regardless of the patient's age or sex. Studies have shown that the mass density varies with patient to patient, particularly for those that have undergone surgery or radiotherapy.

Unbiased evaluation of predicted gamma passing rate by an event-mixing technique.

Background: Predicting models of the gamma passing rate (GPR) have been studied to substitute the measurement-based gamma analysis. Since these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array, it has been difficult to compare the performances of the predicting models among institutions with different radiotherapy systems.

Purpose: We aimed to develop unbiased scoring methods to evaluate the performance of the models predicting the GPR, by introducing both best and worst limits for the performance of the GPR prediction.

Radio Frequency Identification Gate System to Identify Misused Personal Dosimeters.

The use of two personal dosimeters, one worn over and one worn under a protective apron, provides the best estimate of effective dose. However, inappropriate positioning of dosimeters is a common occurrence, resulting in abnormally high or low radiation exposure records. Although such incorrect positioning can be identified by radiation exposure records, doing so is time-consuming and labor-intensive for administrators.

Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model.

Objectives: To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features.

Methods: The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed.

Assessment of a self-inspection method and reporting measured electrometer correction factors for reference-class electrometers in radiotherapy.

Background And Purpose: The standard dosimetry system of medical accelerators in radiotherapy consists of an ionization chamber, an electrometer, and cables. Guidance for TG-51 reference dosimetry reported that the electrometer correction factor (P ) should be checked every few years. Therefore, continuous Pelec measurements have not been reported.

Multicenter retrospective study of stereotactic body radiotherapy for patients with previously untreated initial small hepatocellular carcinoma.

Aim: We aimed to verify the therapeutic efficacy and safety of stereotactic body radiotherapy (SBRT) for previously untreated initial small hepatocellular carcinoma (HCC) in a multicenter, retrospective study.

Methods: Patients who underwent SBRT for HCC at the Japanese Society of Clinical Oncology (JCOG) member hospitals in Japan between July 2013 and December 2017 and met the following eligibility criteria were included: (1) initial HCC; (2) ≤3 nodules, ≤5 cm in diameter; (3) Child-Pugh score of A or B; and (4) unsuitability for or refusal of standard treatment. We analyzed the overall survival, recurrence-free survival, and cumulative incidence of local recurrence rate, and adverse events directly related to SBRT.

MRI image synthesis for fluid-attenuated inversion recovery and diffusion-weighted images with deep learning.

This study aims to synthesize fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted images (DWI) with a deep conditional adversarial network from T1- and T2-weighted magnetic resonance imaging (MRI) images. A total of 1980 images of 102 patients were split into two datasets: 1470 (68 patients) in a training set and 510 (34 patients) in a test set. The prediction framework was based on a convolutional neural network with a generator and discriminator.

Physical and biological dosimetric margin according to prescription method for stereotactic body radiation therapy.

This study aimed to expand the biological conversion factor (BCF) model, which converts the physical dosimetric margin (PDM) to the biological dosimetric margin (BDM) for point prescription with 3-dimensional conformal radiation therapy (3DCRT) and the marginal prescription method with volumetric-modulated arc radiotherapy (VMAT). The VMAT of the marginal prescription and the 3DCRT of the point prescription with lung stereotactic body radiation therapy (SBRT) by using RayStation were planned. The biological equivalent dose (BED) for a dose per fraction (DPF) of 3-20 Gy was calculated from these plans.

A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network.

Background: Artificial intelligence (AI)-based gamma passing rate (GPR) prediction has been proposed as a time-efficient virtual patient-specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy.

Purpose: The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN).

Improved biological dosimetric margin model for different PTV margins with stereotactic body radiation therapy in homogeneous and nonhomogeneous tumor regions.

Background: The purpose of this study was to improve the biological dosimetric margin (BDM) corresponding to different planning target volume (PTV) margins in homogeneous and nonhomogeneous tumor regions using an improved biological conversion factor (BCF) model for stereotactic body radiation therapy (SBRT).

Materials And Methods: The PTV margin was 5-20 mm from the clinical target volume. The biologically equivalent dose (BED) was calculated using the linear-quadratic model.

Image synthesis of effective atomic number images using a deep convolutional neural network-based generative adversarial network.

Background: The effective atomic numbers obtained from dual-energy computed tomography (DECT) can aid in characterization of materials. In this study, an effective atomic number image reconstructed from a DECT image was synthesized using an equivalent single-energy CT image with a deep convolutional neural network (CNN)-based generative adversarial network (GAN).

Materials And Methods: The image synthesis framework to obtain the effective atomic number images from a single-energy CT image at 120 kVp using a CNN-based GAN was developed.