Journal of X-Ray Science and Technology - Volume 29, issue 6

Authors: Wang, Zhongfei | Yun, Qinghui | Liu, Changhao | Sun, Xiaohuan | Wang, Wei | Yin, Yutian | Xiao, Feng | Zhao, Lina

Article Type: Research Article

Abstract: OBJECTIVE: To improve safety and efficiency of radiotherapy process by customizing a Varian ARIA oncology information system following the guidelines provided in AAPM TG-100 report. METHODS: First, failure mode and effects analysis (FMEA) and quality management program were implemented for radiotherapy process. We have customized the visual care path in the ARIA system and set up a series of templates for simulation, prescription, contouring, treatment planning, and multiple checklists. Average time of activities’ completion and amount of planning errors were compared before and after the use of the customized ARIA to evaluate its impact on the efficiency and …safety of radiotherapy. RESULTS: Completion time and on-time completion rate of the key activities in the care path are improved. The time of OAR/targets contouring decreases from (1.94±1.51) days to (1.64±1.07) days (p  = 0.003), with the on-time completion rate increases from 77.4%to 83.3%(p  = 0.048). Treatment planning time decreases from (0.81±0.65) days to (0.55±0.51) days (p  < 0.001), with the on-time completion rate increases from 96.6%to 98.3%(p  = 0.163). Waiting time of patients decreases from (4.50±1.83) days to (4.04±1.34) days (p  < 0.001), with the on-time completion rate increases from 81.9%to 89.7%(p  = 0.003). In addition, the average plan error rate decreases from 5.5%(2.9%for safety errors and 2.6%for non-normative errors) to 2.4%(1.6%for safety errors and 0.8%for non-normative errors) (p  = 0.029). CONCLUSION: Our study demonstrates that the customized ARIA system has the potential to promote efficiency and safety in radiotherapy process management. It is beneficial to organize and accelerate the treatment process with more effective communications and fewer errors. Show more

Keywords: TG-100, ARIA system, quality control, quality assurance

DOI: 10.3233/XST-210952

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 6, pp. 1103-1112, 2021

Authors: Zhang, Chao | Huang, Yu-Qin | Liu, Zhi-Long

Article Type: Research Article

Abstract: OBJECTIVE: To evaluate diagnostic value of Thyroid Imaging Reporting and Data System published by American College of Radiology (ACR TI-RADS) in 2017, ultrasound-guided fine-needle aspiration (US-FNA), and the combination of both methods in differentiation between benign and malignant thyroid nodules. METHODS: The data of US-FNA and ACR TI-RADS are collected from 159 patients underwent thyroid surgery in our hospital, which include a total of 178 thyroid nodules. A Bethesda System for Reporting Thyroid Cytopathology category of ≥IV and an ACR TI-RADS category ≥4 are regarded as diagnosis standards for malignancy in US-FNA and ACR TI-RADS, respectively. The pathological …results after surgery are considered as the gold standard. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the ACR TI-RADS, US-FNA and the combination of both methods for the differential diagnosis of thyroid nodules are calculated, respectively. RESULTS: The sensitivity, specificity and accuracy of ACR TI-RADS are 85.4%, 37.5%and 72.5%, respectively. The sensitivity, specificity and accuracy of US-FNA are 70.0%, 100%and 78.1%, respectively. After combining these two methods, the sensitivity, specificity and accuracy increase to 99.23%, 37.50%and 82.58%, respectively. The sensitivity of ACR TI-RADS is higher than that of US-FAN, and the sensitivity of combining these two methods is also higher than that of using ACR TI-RADS and US-FNA alone. CONCLUSION: The established ACR TI-RADS can help in selecting the target during nodule puncture, while the combination of ACR TI-RADS and US-FAN can further improve diagnostic ability for detecting malignant thyroid nodules. Show more

Keywords: ACR TI-RADS, ultrasound imaging, fine-needle aspiration biopsy (FNAB), thyroid nodule, differential diagnosis

DOI: 10.3233/XST-210949

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 6, pp. 1113-1122, 2021

Authors: Tan, Wenjun | Zhou, Luyu | Li, Xiaoshuo | Yang, Xiaoyu | Chen, Yufei | Yang, Jinzhu

Article Type: Research Article

Abstract: BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and …3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation rate and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation. Show more

Keywords: Pulmonary vascular segmentation, U-Net, deep neural network, lung computed tomography (CT) images,, computed tomography angiography (CTA)

DOI: 10.3233/XST-210955

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 6, pp. 1123-1137, 2021

Authors: Wu, Zhonghang | Hou, Pengfei | Li, Wei | Zhu, Tianbao | Wang, Peipei | Yuan, Mingyuan | Sun, Jiuai

Article Type: Research Article

Abstract: BACKGROUND: Manual or machine-based analysis of chest radiographs needs the images acquired with technical adequacy. Currently, the equidistance between the medial end of clavicles and the center of spinous processes serves as the only criterion to assess whether a frontal PA chest radiograph is taken with any rotation. However, this measurement is normally difficult to implement because there exists overlapping of anatomies within the region. Moreover, there is no way available to predict exact rotating angles even the distances were correctly measured from PA chest radiographs. OBJECTIVE: To quantitatively assess positioning adequacy of PA chest examination, this study …proposes and investigates a new method to estimate rotation angles from asymmetric projection of thoracic cage on radiographs. METHOD: By looking into the process of radiographic projection, generalized expressions have been established to correlate rotating angles of thorax with projection difference of left and right sides of thoracic cage. A trunk phantom with different positioning angles is employed to acquire radiographs as standard reference to verify the theoretical expressions. RESULTS: The angles estimated from asymmetric projections of thoracic cage yield good agreement with those actual rotated angles, and an approximate linear relationship exists between rotation angle and asymmetric projection of thoracic cage. Under the experimental projection settings, every degree of rotation corresponds to the width difference of two sides of thoracic cage around 13–14 pixels. CONCLUSION: The proposed new method may be used to quantify rotating angles of chest and assess image quality for thoracic radiographic examination. Show more

Keywords: Technical adequacy, PA chest images, rotation angle of thorax, asymmetric radiographic projection, thoracic cage

DOI: 10.3233/XST-210990

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 6, pp. 1139-1147, 2021

Authors: Yin, Ruo-Han | Yang, You-Chang | Tang, Xiao-Qiang | Shi, Hai-Feng | Duan, Shao-Feng | Pan, Chang-Jie

Article Type: Research Article

Abstract: OBJECTIVE: To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different …machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS: The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION: svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision. Show more

Keywords: Radiomics, renal clear cell carcinoma, X-ray computed tomography, machine learning

DOI: 10.3233/XST-210997

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 6, pp. 1149-1160, 2021