• Journal of radiological science and technology
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• v.46 no.4
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• pp.315-323
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• 2023

Post-mortem computed tomography (PMCT) is commonly employed in the field of forensic pathology. PMCT was mainly performed using a whole-body scan with a wide field of view (FOV), which lead to a decrease in spatial resolution due to the increased pixel size. This study aims to evaluate the potential for developing a super-resolution model based on conditional generative adversarial networks (CGAN) to enhance the image quality of CT. 1761 low-resolution images were obtained using a whole-body scan with a wide FOV of the head phantom, and 341 high-resolution images were obtained using the appropriate FOV for the head phantom. Of the 150 paired images in the total dataset, which were divided into training set (96 paired images) and validation set (54 paired images). Data augmentation was perform to improve the effectiveness of training by implementing rotations and flips. To evaluate the performance of the proposed model, we used the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Deep Image Structure and Texture Similarity (DISTS). Obtained the PSNR, SSIM, and DISTS values of the entire image and the Medial orbital wall, the zygomatic arch, and the temporal bone, where fractures often occur during head trauma. The proposed method demonstrated improvements in values of PSNR by 13.14%, SSIM by 13.10% and DISTS by 45.45% when compared to low-resolution images. The image quality of the three areas where fractures commonly occur during head trauma has also improved compared to low-resolution images.

• Korean Journal of Radiology
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• v.24 no.1
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• pp.51-61
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• 2023

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent ¹⁸F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from ¹⁸F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.1989-1994
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• 2012

Background and Purpose: An analysis of the current nuclear medicine (NM) status and future demand in Turkey in line with the international benchmarks was conducted to establish a comprehensive baseline reference. Methods: Data from all NM centers on major equipment and manpower in Turkey were collected through a survey and cross-checked with the primary research and governmental data. Data regarding manpower currently working were obtained from the relevant academic centers and occupational societies. Results: The current numbers of NM laboratories, NM specialists, gamma cameras, PET/CT scanners, radioiodine treatment units for thyroid cancer are 217, 474, 287, 75 and 39, respectively. There was personnel and equipment need underestimated in the field compared to developed countries. Equipment insufficiency was more significant in the Ministry of Health (MoH) hospitals. These gaps should be eliminated with strategic planning of equipment and NM laboratories. Currently, the number of the PET/CT devices is at the level of the developed countries. The number of specialists in the field should reach the expected goal in 2023. By 2023, Turkey will need around 820 NM specialists, 498 gamma cameras and 99 PET/CT devices. In addition, further studies should be made regarding other related staff, particularly for health physicians, radiopharmacists and NM technicians. Conclusion: There is an insufficiency of personnel and equipment in Turkey's NM field. Comprehensive strategic planning is required to allocate limited resources and the purchase of the equipment and employment policies should be structured as part of "National Special Feature Requiring Health Service Plan".

• Journal of Korean Neurosurgical Society
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• v.47 no.2
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• pp.124-127
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• 2010

Objective : The incidence of subarachnoid hemorrhage (SAH) worldwide varies considerably. In spite of many reports about the incidence of SAH, there has been no report about the incidence of SAH on the basis of the Korean population. The purpose of this hospital-based study was to assess the actual incidence rates of aneurysmal SAH in Gwangju city and Jeollanamdo province. Methods : All cases of SAH confirmed by computerized tomography (CT) between January 2007 and December 2007 were selected for analysis. For the data collection, three major training hospital and ten general hospitals working the CT in Gwangju city and four major general hospitals in Jeollanamdo province participate in this study. Results : According to the official census of Korea, the population was 1,413,444 in Gwangju city and 1.929,836 in Jeollanamdo province in 2007. There were 163 patients in Gwangju city and 266 patients in Jeollanamdo province confirmed SAH by CT in 2007. The crude and the age- and sex-adjusted annual incidence rates per 100,000 population for all ages in Gwangju city were 11.5 and 12.4 for aneurysmal SAH and in Jeollanamdo province were 13.8 and 10.8. The incidence was higher in women and increased with age. The gender distribution varied with age. At young ages. the incidence was higher in men while after the age of 40 years, the incidence was higher in women. Conclusion : In the present study, the age- and sex-adjusted annual incidence rates is 11.8 in Gwangju city and Jeollanamdo province. The incidence was higher in women and increased with age.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.912-921
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• 2021

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.149-159
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• 2016

Purpose : To evaluate the usefulness of the breathing exercise,we analyzed the change in the RPM signal and the diaphragm imagebefore 4D respiratory gated radiation therapy planning of lung cancer patients. Materials and Methods : The breathing training was enforced on 11 patients getting the 4D respiratory gated radiation therapy from April, 2016 until August. At the same time, RPM signal and diaphragm image was obtained respiration training total three steps in step 1 signal acquisition of free-breathing state, 2 steps respiratory signal acquisition through the guide of the respiratory signal, 3 steps, won the regular respiration signal to the description and repeat training. And then, acquired the minimum value, maximum value, average value, and a standard deviation of the inspiration and expiration in RPM signal and diaphragm image in each steps. Were normalized by the value of the step 1, to convert the 2,3 steps to the other distribution ratio (%), by evaluating the change in the interior of the respiratory motion of the patient, it was evaluated breathing exercise usefulness of each patient. Results : The mean value and the standard deviation of each step were obtained with the procedure 1 of the RPM signal and the diaphragm amplitude as a 100% reference. In the RPM signal, the amplitudes and standard deviations of four patients (36.4%, eleven) decreased by 18.1%, 27.6% on average in 3 steps, and 2 patients (18.2%, 11 people) had standard deviation, It decreased by an average of 36.5%. Meanwhile, the other four patients (36.4%, eleven) decreased by an average of only amplitude 13.1%. In Step 3, the amplitude of the diaphragm image decreased by 30% on average of 9 patients (81.8%, 11 people), and the average of 2 patients (18.2%, 11 people) increased by 7.3%. However, the amplitudes of RPM signals and diaphragm image in 3steps were reduced by 52.6% and 42.1% on average from all patients, respectively, compared to the 2 steps. Relationship between RPM signal and diaphragm image amplitude difference was consistent with patterns of movement 1, 2 and 3steps, respectively, except for No. 2 No. 10 patients. Conclusion : It is possible to induce an optimized respiratory cycle when respiratory training is done. By conducting respiratory training before treatment, it was possible to expect the effect of predicting the movement of the lung which could control the patient's respiration. Ultimately, it can be said that breathing exercises are useful because it is possible to minimize the systematic error of radiotherapy, expect more accurate treatment. In this study, it is limited to research analyzed based on data on respiratory training before treatment, and it will be necessary to verify with the actual CT plan and the data acquired during treatment in the future.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.476-488
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• 2021

Objective: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. Materials and Methods: Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31-89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation. Results: The Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model). Conclusion: The deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.189-198
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• 2024

Objective: To investigate the prognostic utility of radiomics features extracted from ¹⁸F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent ¹⁸F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.