Journal of X-Ray Science and Technology - Volume 31, issue 1

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

Article Type: Research Article

Abstract: BACKGROUND: Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge. OBJECTIVE: The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images. METHODS: We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into …three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network’s attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy. RESULTS: Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively. CONCLUSIONS: This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice. Show more

Keywords: Pancreatic neoplasm, pancreatic tumor classification, Convolutional Neural Network, transfer learning, computed tomography (CT), computer-aided diagnosis (CAD)

DOI: 10.3233/XST-221281

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 167-180, 2023

Authors: Meng, Wan-Bin | Li, Qiang | Ye, Yan-Cheng | Wu, Jia-Ming

Article Type: Research Article

Abstract: OBJECTIVE: This study aims to develop and test a new technique by using the convergent arcTAN (cATAN) method capable of dealing with the virtual source position delivered by different carbon ion energies from the pattern of scanning-passive scatter beam. MATERIALS AND METHODS: A homemade large-format CMOS sensor and Gaf Chromic EBT3 films are used for the virtual source position measurement. The Gaf films are embedded in a self-designed rectangular plastic frame to tighten films and set up on a treatment couch for irradiation in air with the film perpendicular to the carbon ion beam at the nominal source-axis-distance …(SAD) as well as upstream and downstream from the SAD. The horizontal carbon ion beam with 5 energies at a machine opening field size is carried out in this study. The virtual source position is determined by using the convergent arcTAN (cATAN) method and compared with a linear regression by back projecting FWHM to zero at a distance upstream from the various source-film-distance. RESULTS: The film FWHM measurement error of 0.5 mm leads to 0.001% deviation of α (cATAN) at every assumed textend . The overall uncertainty for the reproducibility of calculated virtual source position by the assumed textend in the vertical and horizontal directions amounts to 0.1%. The errors of calculated virtual source position by assumed textend with back projecting FWHM to zero methods are within 1.1±0.001, p  = 0.033. CONCLUSION: We develop a new technique capable of dealing with the virtual source position with a convergent arcTAN method to avoid any manual measurement mistakes in scanning-passive scatter carbon ion beam. The readers are encouraged to conduct the proposed cATAN method in this study to investigate the virtual source position in the Linac-based external electron beams and the proton beams. Show more

Keywords: Carbon ion beams, virtual source position, scanning-passive scatter beam

DOI: 10.3233/XST-221274

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 181-197, 2023

Authors: Chen, Dandan | Guo, Yuan | Liu, Weifeng | Yuan, Zhaohu | Mo, Wenjian | Wei, Xinhua

Article Type: Research Article

Abstract: BACKGROUND: Anemia is an important clinical symptom for aplastic anemia (AA) patients who are suffered with peripheral pancytopenia. OBJECTIVE: To evaluate the accuracy of diagnosing anemia with non-invasive chest computed tomography (CT) for AA patients. METHODS: The CT attenuation of left ventricular (LV) cavity and interventricular septum (IVS) on unenhanced thoracic CT images of AA patients are retrospectively analyzed, including 84 AA patients in pre-transplant and 1-month (n  = 82), 2-month (n  = 72), 3-month (n  = 75), 6-month (n  = 74) and 12-month (n  = 70) followed patients in post-transplant. The difference (IVS-LV) and ratio (LV/IVS) of the CT attenuation …between LV cavity and interventricular septum are calculated. Serum hemoglobin is estimated within 24 hours of CT imaging. The CT attenuations of IVS-LV and LV/IVS are correlated with hemoglobin, and their variation tendency is analyzed during the treatment of a-HSCT. A receiver operating characteristic (ROC) curve analysis is then performed for the diagnosis of anemia. RESULTS: The CT attenuations of IVS-LV and LV/IVS well correlate with hemoglobin (r  = –0.618 and 0.628, respectively, P  <  0.001). The variation tendency of IVS-LV and LV/IVS is similar to that of hemoglobin with opposite directions during one-year follow-up of a-HSCT. When a threshold of CT attenuation of IVS-LV and LV/IVS is set at 11.5HU and 0.77, respectively, both the sensitivity and specificity in diagnosing anemia are good (74.7% and 73.8% in CT attenuation of IVS-LV; 77.4% and 70.4% in LV/LVS, respectively). CONCLUSIONS: Both CT attenuation of LV/IVS and IVS-LV had similar accuracy in diagnosing anemia for AA patients. The non-invasive chest CT can offer a new possibility to complementarily evaluate anemia for AA patients in the diagnostic radiology reports. Show more

Keywords: Anemia, computed tomography, allogeneic hematopoietic stem cell transplantation, hemoglobin, aplastic anemia

DOI: 10.3233/XST-221296

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 199-209, 2023

Authors: Sethy, Prabira Kumar | Geetha Devi, A. | Padhan, Bikash | Behera, Santi Kumari | Sreedhar, Surampudi | Das, Kalyan

Article Type: Research Article

Abstract: Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was …to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples. Show more

Keywords: Lung cancer, histopathological images, wavelet, AlexNet, support vector machine (SVM), cancer diagnosis

DOI: 10.3233/XST-221301

Citation: Journal of X-Ray Science and Technology, vol. 31, no. 1, pp. 211-221, 2023