Journal of X-Ray Science and Technology - Volume 31, issue 3

Authors: Luo, Zhendong | Li, Jing | Liao, YuTing | Huang, Wenxiao | Li, Yulin | Shen, Xinping

Article Type: Research Article

Abstract: PURPOSE: This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n  = 72; validation dataset, n  = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected …using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS: AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583–0.937), 0.706 (95% CI: 0.506–0.905), 0.751 (95% CI: 0.572–0.930), 0.796 (95% CI: 0.629–0.963), 0.828 (95% CI: 0.676–0.980), respectively. The DeLong test showed no significant difference between any pair of models (p  > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). CONCLUSION: Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data. Show more

Keywords: Radiomics, prognostic indicator, X-ray, Magnetic resonance imaging, Osteosarcoma, chemotherapy response

DOI: 10.3233/XST-221352

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 611-626, 2023

Authors: Haraguchi, Takafumi | Kobayashi, Yasuyuki | Hirahara, Daisuke | Kobayashi, Tatsuaki | Takaya, Eichi | Nagai, Mariko Takishita | Tomita, Hayato | Okamoto, Jun | Kanemaki, Yoshihide | Tsugawa, Koichiro

Article Type: Research Article

Abstract: BACKGROUND: In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE: This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS: A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in …DWIBS and STIR sequences and were divided into training (n  = 75) and test (n  = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS: For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548–0.982), 0.801 (0.597–1.000), and 0.779 (0.567–0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548–0.982), 0.757 (0.538–0.977), and 0.779 (0.567–0.992), respectively. CONCLUSIONS: Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status. Show more

Keywords: Diffusion-weighted whole-body imaging, background signal suppression, DWIBS, radiomics, axillary lymph node status, breast cancer, machine learning

DOI: 10.3233/XST-230009

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 627-640, 2023

Authors: Yang, Fan | Weng, Xin | Wu, Yuhui | Miao, Yuehong | Lei, Pinggui | Hu, Zuquan

Article Type: Research Article

Abstract: BACKGROUND: Ulna and radius segmentation of dual-energy X-ray absorptiometry (DXA) images is essential for measuring bone mineral density (BMD). OBJECTIVE: To develop and test a novel deep learning network architecture for robust and efficient ulna and radius segmentation on DXA images. METHODS: This study used two datasets including 360 cases. The first dataset included 300 cases that were randomly divided into five groups for five-fold cross-validation. The second dataset including 60 cases was used for independent testing. A deep learning network architecture with dual residual dilated convolution module and feature fusion block based on residual U-Net …(DFR-U-Net) to enhance segmentation accuracy of ulna and radius regions on DXA images was developed. The Dice similarity coefficient (DSC), Jaccard, and Hausdorff distance (HD) were used to evaluate the segmentation performance. A one-tailed paired t -test was used to assert the statistical significance of our method and the other deep learning-based methods (P  < 0.05 indicates a statistical significance). RESULTS: The results demonstrated our method achieved the promising segmentation performance, with DSC of 98.56±0.40% and 98.86±0.25%, Jaccard of 97.14±0.75% and 97.73±0.48%, and HD of 6.41±11.67 pixels and 8.23±7.82 pixels for segmentation of ulna and radius, respectively. According to statistics data analysis results, our method yielded significantly higher performance than other deep learning-based methods. CONCLUSIONS: The proposed DFR-U-Net achieved higher segmentation performance for ulna and radius on DXA images than the previous work and other deep learning approaches. This methodology has potential to be applied to ulna and radius segmentation to help doctors measure BMD more accurately in the future Show more

Keywords: DXA images, deep learning, ulna and radius segmentation, feature fusion block, dual residual dilated convolution module

DOI: 10.3233/XST-230010

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 641-653, 2023

Authors: Li, Jin | Yin, Wei | Wang, Yuanjun

Article Type: Research Article

Abstract: BACKGROUND: Automatic segmentation of the pancreas and its tumor region is a prerequisite for computer-aided diagnosis. OBJECTIVE: In this study, we focus on the segmentation of pancreatic cysts in abdominal computed tomography (CT) scan, which is challenging and has the clinical auxiliary diagnostic significance due to the variability of location and shape of pancreatic cysts. METHODS: We propose a convolutional neural network architecture for segmentation of pancreatic cysts, which is called pyramid attention and pooling on convolutional neural network (PAPNet). In PAPNet, we propose a new atrous pyramid attention module to extract high-level features at different …scales, and a spatial pyramid pooling module to fuse contextual spatial information, which effectively improves the segmentation performance. RESULTS: The model was trained and tested using 1,346 CT slice images obtained from 107 patients with the pathologically confirmed pancreatic cancer. The mean dice similarity coefficient (DSC) and mean Jaccard index (JI) achieved using the 5-fold cross-validation method are 84.53% and 75.81%, respectively. CONCLUSIONS: The experimental results demonstrate that the proposed new method in this study enables to achieve effective results of pancreatic cyst segmentation. Show more

Keywords: Pancreatic cyst, medical image segmentation, convolutional neural network, computed tomography

DOI: 10.3233/XST-230011

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 655-668, 2023