• Korean Journal of Radiology
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• v.24 no.12
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• pp.1190-1199
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• 2023

Objective: This study aimed to investigate the feasibility of ultrafast magnetic resonance imaging (MRI) and radiomic features derived from breast MRI for predicting the upstaging of ductal carcinoma in situ (DCIS) diagnosed using percutaneous needle biopsy. Materials and Methods: Between August 2018 and June 2020, 95 patients with 98 DCIS lesions who underwent preoperative breast MRI, including an ultrafast sequence, and subsequent surgery were included. Four ultrafast MRI parameters were analyzed: time-to-enhancement, maximum slope (MS), area under the curve for 60 s after enhancement, and time-to-peak enhancement. One hundred and seven radiomic features were extracted for the whole tumor on the first post-contrast T1WI and T2WI using PyRadiomics. Clinicopathological characteristics, ultrafast MRI findings, and radiomic features were compared between the pure DCIS and DCIS with invasion groups. Prediction models, incorporating clinicopathological, ultrafast MRI, and radiomic features, were developed. Receiver operating characteristic curve analysis and area under the curve (AUC) were used to evaluate model performance in distinguishing between the two groups using leave-one-out cross-validation. Results: Thirty-six of the 98 lesions (36.7%) were confirmed to have invasive components after surgery. Compared to the pure DCIS group, the DCIS with invasion group had a higher nuclear grade (P < 0.001), larger mean lesion size (P = 0.038), larger mean MS (P = 0.002), and different radiomic-related characteristics, including a more extensive tumor volume; higher maximum gray-level intensity; coarser, more complex, and heterogeneous texture; and a greater concentration of high gray-level intensity. No significant differences in AUCs were found between the model incorporating nuclear grade and lesion size (0.687) and the models integrating additional ultrafast MRI and radiomic features (0.680-0.732). Conclusion: High nuclear grade, larger lesion size, larger MS, and multiple radiomic features were associated with DCIS upstaging. However, the addition of MS and radiomic features to the prediction model did not significantly improve the prediction performance.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1260-1268
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• 2022

Objective: To propose standardized MRI-proton density fat fraction (PDFF) cutoff values for diagnosing hepatic steatosis, evaluated using contemporary PDFF measuring methods in a large population of healthy adults, using histologic fat fraction (HFF) as the reference standard. Materials and Methods: A retrospective search of electronic medical records between 2015 and 2018 identified 1063 adult donor candidates for liver transplantation who had undergone liver MRI and liver biopsy within a 7-day interval. Patients with a history of liver disease or significant alcohol consumption were excluded. Chemical shift imaging-based MRI (CS-MRI) PDFF and high-speed T2-corrected multi-echo MR spectroscopy (HISTO-MRS) PDFF data were obtained. By temporal splitting, the total population was divided into development and validation sets. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of the MRI-PDFF method. Two cutoff values with sensitivity > 90% and specificity > 90% were selected to rule-out and rule-in, respectively, hepatic steatosis with reference to HFF ≥ 5% in the development set. The diagnostic performance was assessed using the validation set. Results: Of 921 final participants (624 male; mean age ± standard deviation, 31.5 ± 9.0 years), the development and validation sets comprised 497 and 424 patients, respectively. In the development set, the areas under the ROC curve for diagnosing hepatic steatosis were 0.920 for CS-MRI-PDFF and 0.915 for HISTO-MRS-PDFF. For ruling-out hepatic steatosis, the CS-MRI-PDFF cutoff was 2.3% (sensitivity, 92.4%; specificity, 63.0%) and the HISTO-MRI-PDFF cutoff was 2.6% (sensitivity, 88.8%; specificity, 70.1%). For ruling-in hepatic steatosis, the CS-MRI-PDFF cutoff was 3.5% (sensitivity, 73.5%; specificity, 88.6%) and the HISTO-MRI-PDFF cutoff was 4.0% (sensitivity, 74.7%; specificity, 90.6%). Conclusion: In a large population of healthy adults, our study suggests diagnostic thresholds for ruling-out and ruling-in hepatic steatosis defined as HFF ≥ 5% by contemporary PDFF measurement methods.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.455-465
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• 2022

Objective: To compare the reproducibility and performance of quantitative metrics between ZOOMit and conventional intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) in the diagnosis of early- and mid-stage Sjögren's syndrome (SS). Materials and Methods: Twenty-two patients (mean age ± standard deviation, 52.0 ± 10.8 years; male:female, 2:20) with early- or mid-stage SS and 20 healthy controls (46.9 ± 14.6 years; male:female, 7:13) were prospectively enrolled in our study. ZOOMit IVIM and conventional IVIM MRI were performed simultaneously in all individuals using a 3T scanner. Quantitative IVIM parameters - including tissue diffusivity (D), pseudodiffusion coefficient (D^*), and perfusion fraction (f) - inter- and intra-observer reproducibility in measuring these parameters, and their ability to distinguish patients with SS from healthy individuals were assessed and compared between ZOOMit IVIM and conventional IVIM methods, appropriately. MR gland nodular grade (MRG) was also examined. Results: Inter- and intra-observer reproducibility was better with ZOOMit imaging than with conventional IVIM imaging (ZOOMit vs. conventional, intraclass correlation coefficient of 0.897-0.941 vs. 0.667-0.782 for inter-observer reproducibility and 0.891-0.968 vs. 0.814-0.853 for intra-observer reproducibility). Significant differences in ZOOMit f, ZOOMit D^*, D^*, conventional D^*, and MRG between patients with SS and healthy individuals (all p < 0.05) were observed. ZOOMit D^* outperformed conventional D^* in diagnosing early- and mid-stage SS (area under receiver operating curve, 0.867 and 0.658, respectively; p = 0.002). The combination of ZOOMit D^*, MRG, and ZOOMit f as a new diagnostic index for SS, increased diagnostic area under the curve to 0.961, which was higher than that of any single parameter (all p < 0.01). Conclusion: Considering its better reproducibility and performance, ZOOMit IVIM may be preferred over conventional IVIM MRI, and may subsequently improve the ability to diagnose early- and mid-stage SS.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1078-1088
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• 2022

Objective: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). Materials and Methods: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. Results: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. Conclusion: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

• Animal Bioscience
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• v.37 no.8
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• pp.1418-1427
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• 2024

Objective: This study aimed to investigate the efficacy of Bacillus-based probiotics supplemented at two different levels to modulate the productive performance, egg quality, tibia traits, and specific cecal bacteria counts of Hy-Line Brown layers from 25 to 37 weeks of age. Methods: A total of 216 twenty-five-week-old hens were randomly distributed into 3 experimental diets with 12 replicates of 6 birds per cage. Diets included basal diet supplemented with 0 (CON), 3×10⁸ (PRO1), or 3×10⁹ (PRO2) colony-forming unit (CFU) of the test probiotic containing Bacillus subtilis PB6, Bacillus subtilis FXA, and Bacillus licheniformis G3 per kilogram of feed. Results: Improved egg weights and mass at 29 weeks; and feed intake at 31 weeks (p<0.10) were noticed with the probiotic-supplemented PRO1 and PRO2 diets. Considering egg quality, the shell thickness, Haugh units, and yolk color were improved; but yolk cholesterol was lowered (p<0.05) with PRO1 and PRO2 diets at 29 weeks. At both 33 and 37 weeks, the egg-breaking strength, shell color and thickness, albumen height, Haugh units, and yolk color were improved; but yolk cholesterol was similarly lowered (p<0.05) with the PRO1 and PRO2 diets. Improved tibia Ca, ash, weights, and density; and raised cecal counts of Bifidobacteria and Lactobacilli (p<0.05) were noticed with PRO1 and PRO2 diets. Improved tibia P but reduced Clostridia counts (p<0.10) were also observed with the PRO1 and PRO2 diets. Conclusion: Probiotic supplementation of Bacillus subtilis PB6, Bacillus subtilis FXA, and Bacillus licheniformis G3 at 3×10⁸ CFU/kg of feed is adequate to significantly improve egg quality, lower yolk cholesterol, enhance several tibia traits, and raise the populations of beneficial cecal bacteria. Modest improvements in several productive parameters and tibia P but reduced Clostridia were also observed; and could warrant further investigation of probiotic effects beyond the current test period.

• Animal Bioscience
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• v.37 no.1
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• pp.95-104
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• 2024

Objective: In the present study, we aimed to investigate the effects of enzymolysis fermentation of Chinese herbal medicines (CHMs) on egg production performance, egg quality, lipid metabolism, serum reproductive hormone levels, and the mRNA expression of the ovarian hormone receptor of laying hens in the late-laying stage. Methods: A total of 360 Hy-Line Brown laying hens (age, 390 days) were randomly categorized into four groups. Hens in the control (C) group were fed a basic diet devoid of CHMs, the crushed CHM (CT), fermented CHM (FC), and enzymatically fermented CHM (EFT) groups received diets containing 2% crushed CHM, 2% fermented CHM, and 2% enzymatically fermented CHM, respectively. Results: Compared with crushed CHM, the acid detergent fiber, total flavonoids, and total saponins contents of fermented CHM showed improvement (p<0.05); furthermore, the neutral and acid detergent fiber, total flavonoids, and total saponins contents of enzymatically fermented CHM improved (p<0.05). At 5 to 8 weeks, hens in the FC and EFT groups showed increased laying rates, haugh unit, albumin height, yolk color, shell thickness, and shell strength compared with those in the C group (p<0.05). Compared with the FC group, the laying rate, albumin height, and Shell thickness in the EFT group was increased (p<0.05). Compared with the C, CT, and FC groups, the EFT group showed reduced serum total cholesterol and increased serum luteinizing hormone levels and mRNA expressions of follicle stimulating hormone receptor and luteinizing hormone receptor (p<0.05). Conclusion: These results indicated that the ETF group improved the laying rate and egg quality and regulated the lipid metabolism in aged hens. The mechanism underlying this effect was likely related to cell wall degradation of CHM and increased serum levels of luteinizing hormone and mRNA expression of the ovarian hormone receptor.

• Korean Journal of Radiology
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• v.21 no.8
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• pp.1007-1017
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• 2020

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1345-1354
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• 2020

Objective: To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use. Materials and Methods: PubMed MEDLINE and EMBASE were searched using the terms 'cognitive impairment' or 'Alzheimer' or 'dementia' and 'radiomic' or 'texture' or 'radiogenomic' for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science. Results: The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence. Conclusion: The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.

• Korean Journal of Radiology
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• v.21 no.12
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• pp.1317-1325
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• 2020

Objective: The aim of this study was to prospectively evaluate whether liver stiffness (LS) assessments, obtained by two-dimensional (2D)-shear wave elastography (SWE) with a propagation map, can evaluate liver fibrosis stage using histopathology as the reference standard. Materials and Methods: We prospectively enrolled 123 patients who had undergone percutaneous liver biopsy from two tertiary referral hospitals. All patients underwent 2D-SWE examination prior to biopsy, and LS values (kilopascal [kPa]) were obtained. On histopathologic examination, fibrosis stage (F0-F4) and necroinflammatory activity grade (A0-A4) were assessed. Multivariate linear regression analysis was performed to determine the significant factors affecting the LS value. The diagnostic performance of the LS value for staging fibrosis was assessed using receiver operating characteristic (ROC) analysis, and the optimal cut-off value was determined by the Youden index. Results: Reliable measurements of LS values were obtained in 114 patients (92.7%, 114/123). LS values obtained from 2D-SWE with the propagation map positively correlated with the progression of liver fibrosis reported from histopathology (p < 0.001). According to the multivariate linear regression analysis, fibrosis stage was the only factor significantly associated with LS (p < 0.001). The area under the ROC curve of LS from 2D-SWE with the propagation map was 0.773, 0.865, 0.946, and 0.950 for detecting F ≥ 1, F ≥ 2, F ≥ 3, and F = 4, respectively. The optimal cut-off LS values were 5.4, 7.8, 9.4, and 12.2 kPa for F ≥ 1, F ≥ 2, F ≥ 3, and F = 4, respectively. The corresponding sensitivity and specificity of the LS value for detecting cirrhosis were 90.9% and 88.4%, respectively. Conclusion: The LS value obtained from 2D-SWE with a propagation map provides excellent diagnostic performance in evaluating liver fibrosis stage, determined by histopathology.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.81-91
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• 2024

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.