Radiological Physics and Technology最新文献

This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.

This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules. A 0.3 confidence threshold was established for identifying nodules in this phase to enhance the model's sensitivity. The primary objective of the hierarchical model was to locate and categorize all lung nodules while minimizing the false-negative rate. Following the analysis of the results from the first phase, a novel 3D convolutional neural network (CNN) classifier was developed to examine and categorize the potential nodules detected by the YOLOv5s model. The objective was to create a detection framework characterized by an extremely low false positive rate and high accuracy. The Lung Nodule Analysis 2016 (LUNA 16) dataset was used to evaluate the effectiveness of this framework. This dataset comprises 888 CT scans that include the positions of 1186 nodules and 400,000 non-nodular regions in the lungs. The YOLOv5s technique yielded numerous incorrect detections owing to its low confidence level. Nevertheless, the addition of a 3D classification system significantly enhanced the precision of nodule identification. By integrating the outcomes of the YOLOv5s approach using a 30% confidence limit and the 3D CNN classification model, the overall system achieved 98.4% nodule detection accuracy and an area under the curve of 98.9%. Despite producing some false negatives and false positives, the suggested method for identifying lung nodules from CT scans is promising as a valuable aid in decision-making for nodule detection.

This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECT_NC) for AC in ^99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECT_NC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECT_CTAC) and pseudo-CT attenuation correction (SPECT_GAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECT_CTAC and SPECT_GAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECT_CTAC exhibited a higher total count than SPECT_NC and SPECT_GAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECT_CTAC and SPECT_GAN were better than those of SPECT_NC. The mean SSIM value for SPECT_CTAC and SPECT_GAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECT_NC images in ^99mTc-GSA scintigraphy. SPECT_GAN with AC using pseudo-CT images was similar to SPECT_CTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.

The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.

Computed tomography (CT) examinations have been increasingly requested and become the major sources of patient exposure. The cancer risk from CT scans is contingent upon the amount of absorbed dose of organs. This study aims to determine the organ doses and risk of cancer incidence and mortality from CT examinations at high dose (cumulative effective dose, CED ≥ 100 mSv) in a single day to low dose (CED < 100 mSv) from common CT procedures. Data were gathered from two academic centers of patients aged 15 to 75 years old performed CT examinations during the period of 5 years. CED and organ dose were calculated using Monte Carlo simulation software. Lifetime attributable risk (LAR) was determined following Biological Effects of Ionizing Radiation (BEIR) VII report based on life table and baseline cancer rates of Thai population. At high dose, the highest LAR for breast cancer incidence in young female was 82 per 100,000 exposed patients with breast dose of 148 mGy (CT whole abdomen). The highest LAR for liver cancer incidence in male patient was 72 per 100,000 with liver dose of 133 mGy (multiple CT scans). At low dose, the highest average LAR for breast cancer incidence in young female was 23 per 100,000 while for liver cancer incidence in male patients was 22 per 100,000 (CTA whole aorta). Even though the LAR of cancer incidence and mortality was less than 100 per 100,000, they should not be neglected. The risk of cancer incidence may be increased in later life, particularly in young patients.

Interventional cardiologists face significant radiation exposure during interventional cardiology procedures. Therefore, this study focuses on assessing radiation exposure among interventional cardiologists during their procedures. Specifically, it aims to determine the effectiveness of both single and double dosimeter methods in estimating annual occupational radiation doses. This research holds pioneering significance as it represents the very first study undertaken in Sri Lanka. Thirteen interventional cardiologists performed 486 interventional cardiology procedures over three months in three different healthcare institutes. Active Hp(10) dosimeters were placed to measure radiation exposure. Effective doses were calculated using single and double dosimetric algorithms. Annual occupational doses were assessed on an operator basis. Statistical analyses were conducted to assess algorithmic differences and dose variations using the Kruskal-Wallis test and linear regression. The highest annual occupational dose for each dosimetric algorithm received as 2.00 ± 0.24 mSv, 2.29 ± 0.48 mSv, 3.35 ± 0.71 mSv, and 2.64 ± 0.42 mSv, respectively, and remained below the recommended safety limit of 20 mSv/year. The Kruskal-Wallis test revealed no significant differences in the effective doses among double dosimetric algorithms, as well as between single and double dosimetric algorithms (p > 0.05). Linear regression showed strong correlations among various algorithms, demonstrating consistency. The findings of this study hold significant effects on interventional cardiology practice in Sri Lanka, enhancing radiation safety and monitoring.

This study focused on the dosimetric impact of variations in respiratory motion during lung stereotactic body radiotherapy (SBRT). Dosimetric comparisons between volumetric modulated arc therapy (VMAT) and three-dimensional conformal radiotherapy (3DCRT) were performed using four-dimensional computed tomography (4DCT)-based internal target volumes (ITV). We created retrospective plans for ten patients with lung cancer who underwent SBRT using 3DCRT and VMAT techniques. A Delta4 Phantom + (ScandiDos, Uppsala, Sweden) was used to evaluate the dosimetric robustness of 4DCT-based ITV against variations in respiratory motion during treatment. We analyzed respiratory motion during treatment. Dose-volume histogram parameters were evaluated for the 95% dose (D_95%) to the planning target volume (PTV) contoured on CT images obtained under free breathing. The correlations between patient respiratory parameters and dosimetric errors were also evaluated. In the phantom study, the average PTV D_95% dose differences for all fractions were - 2.9 ± 4.4% (- 16.0 - 1.2%) and - 2.0 ± 2.8% (- 11.2 - 0.7%) for 3DCRT and VMAT, respectively. The average dose difference was < 3% for both 3DCRT and VMAT; however, in 5 out of 42 fractions in 3DCRT, the difference in PTV D_95% was > 10%. Dosimetric errors were correlated with respiratory amplitude and velocity, and differences in respiratory amplitude between 4DCT and treatment days were the main factors causing dosimetric errors. The overall average dose error of the PTV D_95% was small; however, both 3DCRT and VMAT cases exceeding 10% error were observed. Larger errors occurred with amplitude variation or baseline drift, indicating limited robustness of 4DCT-based ITV.

This study devised a method to efficiently launch the RapidPlan model for volumetric-modulated arc therapy for prostate cancer in small- and medium-sized facilities using high-quality treatment plans with the PlanIQ software as a reference. Treatment plans were generated for 30 patients with prostate cancer to construct the RapidPlan model using PlanIQ as a reference. In the context of PlanIQ-referenced treatment planning, treatment plans were developed, such that the feasibility dose-volume histogram of each organ-at-risk fell within F ≤ 0.1. For validation of the RapidPlan model, treatment plans were formulated for 20 patients using both RapidPlan and PlanIQ, and the differences were evaluated. The results of RapidPlan model validity assessment revealed that the RapidPlan-produced treatment plans exhibited higher quality in 11 of 20 patients. No significant differences were found between the treatment plans. In conclusion, high-quality treatment plans formulated using PlanIQ as reference facilitated efficient implementation of RapidPlan modeling.

The reference dose for clinical proton beam therapy is based on ionization chamber dosimetry. However, data on uncertainties in proton dosimetry are lacking, and multifaceted studies are required. Monte Carlo simulations are useful tools for calculating ionization chamber dosimetry in radiation fields and are sensitive to the transport algorithm parameters when particles are transported in a heterogeneous region. We aimed to evaluate the proton transport algorithm of the Particle and Heavy Ion Transport Code System (PHITS) using the Fano test. The response of the ionization chamber $f_{\text{Q}}$ and beam quality correction factors $k_{\text{Q}}$ were calculated using the same parameters as those in the Fano test and compared with those of other Monte Carlo codes for verification. The geometry of the Fano test consisted of a cylindrical gas-filled cavity sandwiched between two cylindrical walls. $f_{\text{Q}}$ was calculated as the ratio of the absorbed dose in water to the dose in the cavity in the chamber. We compared the $f_{\text{Q}}$ calculated using PHITS with that of a previous study, which was calculated using other Monte Carlo codes (Geant4, FULKA, and PENH) under similar conditions. The flight mesh, a parameter for charged particle transport, passed the Fano test within 0.15%. This was shown to be sufficiently accurate compared with that observed in previous studies. The $f_{\text{Q}}$ calculated using PHITS were 1.116 ± 0.002 and 1.124 ± 0.003 for NACP-02 and PTW-30013, respectively, and the $k_{\text{Q}}$ were 0.981 ± 0.008 and 1.027 ± 0.008, respectively, at 150 MeV. Our results indicate that PHITS can calculate the $f_{\text{Q}}$ and $k_{\text{Q}}$ with high precision.