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Purpose@#This study evaluated epidermal cyst elasticity using multiple parameters of strain elastography (SE) and shear wave elastography (SWE) and assessed the reproducibility of each parameter. @*Methods@#This retrospective study included 73 patients with epidermal cysts who underwent SE and SWE. SE scores were classified as 1-4 according to elasticity. The strain ratio was evaluated using the elasticity ratio of lesions and adjacent subcutaneous fat tissue. For SWE, the shear wave velocity (m/s), elasticity (kPa) according to the Young modulus, velocity ratio, and elasticity ratio were evaluated. All values were measured twice. The reproducibility of SE and SWE measurements was assessed. The relationships among SE and SWE measurements were evaluated. @*Results@#The strain ratio on SE images showed good reproducibility (intra-class correlation coefficient [ICC]=0.789), and SE scores showed substantial reproducibility (kappa=0.753 and kappa=0.758 for readers 1 and 2, respectively). Moderate reproducibility was found for shear wave velocity and elasticity (ICC=0.750 and ICC=0.648, respectively), as well as for the shear wave velocity of the reference tissue and velocity ratio (ICC=0.747 and ICC=0.713, respectively). All SE scores were positively correlated with the strain ratio (P<0.001). The strain ratio in the second SE session was significantly correlated with the elasticity ratio and velocity ratio in the first SWE session (r=0.245, P=0.037; r=0.243, P=0.038, respectively). Other variables were not correlated. @*Conclusion@#SE and SWE parameters of epidermal cysts showed moderate to good reproducibility. The strain ratio on SE showed good reproducibility and could provide relatively objective and consistent measurements of epidermal cyst elasticity.
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Objective@#Intracranial atherosclerotic stroke occurs through various mechanisms, mainly by artery-to-artery embolism (AA) or branch occlusive disease (BOD). This study evaluated the spatial relationship between middle cerebral artery (MCA) plaques and perforating arteries among different MCA territory infarction types using vessel wall magnetic resonance imaging (VW-MRI). @*Materials and Methods@#We retrospectively enrolled patients with acute MCA infarction who underwent VW-MRI. Thirty-four patients were divided into three groups according to infarction pattern: 1) BOD, 2) both BOD and AA (BOD-AA), and 3) AA.To determine the factors related to BOD, the BOD and BOD-AA groups were combined into one group (with striatocapsular infarction [BOD+]) and compared with the AA group. To determine the factors related to AA, the BOD-AA and AA groups were combined into another group (with cortical infarction [AA+]) and compared with the BOD group. Plaque morphology and the spatial relationship between the perforating artery orifice and plaque were evaluated both quantitatively and qualitatively. @*Results@#The plaque margin in the BOD+ group was closer to the perforating artery orifice than that in the AA group (p = 0.011), with less enhancing plaque (p = 0.030). In the BOD group, plaques were mainly located on the dorsal (41.2%) and superior (41.2%) sides where the perforating arteries mainly arose. No patient in the AA group had overlapping plaques with perforating arteries at the cross-section where the perforator arose. Perforating arteries associated with culprit plaques were most frequently located in the middle two-thirds of the M1 segment (41.4%). The AA+ group had more stenosis (%) than the BOD group (39.73 ± 24.52 vs. 14.42 ± 20.96; p,/i> = 0.003). @*Conclusion@#The spatial relationship between the perforating artery orifice and plaque varied among different types of MCA territory infarctions. In patients with BOD, the plaque margin was closer and blocked the perforating artery orifice, and stenosis degree and enhancement were less than those in patients with AA.
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Purpose@#This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. @*Materials and Methods@#In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. @*Results@#The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI:89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall falsepositive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. @*Conclusion@#The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.
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Objective@#The aim of this study was to compare the survival rates of Korean females aged 40 to 49 years with breast cancer detected by supplemental screening ultrasound (US) or screening mammography alone. @*Materials and Methods@#This single-institution retrospective study included 240 patients with breast cancer (mean age, 45.1 ± 2.8 years) detected by US or mammography who had undergone breast surgery between 2003 and 2008. Medical records were reviewed for clinicopathologic characteristics and detection methods. Disease-free survival (DFS) and overall survival (OS) were compared between patients with breast cancer in the US and mammography groups using the log-rank test. Multivariable cox regression analysis was used to identify independent variables associated with DFS and OS. @*Results@#Among the 240 cases of breast cancer, 43 were detected by supplemental screening US and 197 by screening mammography (mean follow-up: 7.4 years, 93.3% with dense breasts). There were 19 recurrences and 16 deaths, all occurring in the mammography group. While the US group did not differ from the mammography group in tumor stage, the patients in this group were more likely to undergo breast-conserving surgery and radiation therapy than the mammography group.The US group also showed better DFS (p = 0.016); however, OS did not differ between the two groups (p = 0.058). In the multivariable analysis, the US group showed a lower risk of recurrence (hazard ratio, 0.097; 95% confidence interval, 0.001–0.705) compared to the mammography group. @*Conclusion@#Our study found that Korean females aged 40–49 years with US-detected breast cancer showed better DFS than those with mammography-detected breast cancer. However, there were no statistically significant differences in OS.
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Purpose@#This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. @*Materials and Methods@#In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. @*Results@#The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI:89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall falsepositive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. @*Conclusion@#The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.
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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.
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PURPOSE@#The objective of this study was to evaluate the diagnostic value and threshold levels of cytokeratin fragment 21-1 (CYFRA 21-1) in fine-needle aspiration (FNA) washouts for detection of lymph node (LN) recurrence in postoperative breast cancer patients.@*MATERIALS AND METHODS@#FNA cytological assessments and CYFRA 21-1 measurement in FNA washouts were performed for 64 axillary LNs suspicious for recurrence in 64 post-operative breast cancer patients. Final diagnosis was made on the basis of FNA cytology and follow-up data over at least 2 years. The concentration of CYFRA 21-1 was compared between recurrent LNs and benign LNs. Diagnostic performance and cut-off value were evaluated using a receiver operating characteristic curve.@*RESULTS@#Regardless of the non-diagnostic results, the median concentration of CYFRA 21-1 in recurrent LNs was significantly higher than that in benign LNs (p < 0.001). The optimal diagnostic cut-off value was 1.6 ng/mL. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CYFRA 21-1 for LN recurrence were 90.9%, 100%, 100%, 98.1%, and 98.4%, respectively.@*CONCLUSION@#Measurement of CYFRA 21-1 concentration from ultrasound-guided FNA biopsy aspirates showed excellent diagnostic performance with a cut-off value of 1.6 ng/mL. These results indicate that measurement of CYFRA 21-1 concentration in FNA washouts is useful for the diagnosis of axillary LN recurrence in post-operative breast cancer patients.
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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.
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PURPOSE: The purpose of this study was to externally validate the diagnostic usefulness of transrectal ultrasound (TRUS) or transvaginal ultrasound (TVUS)-guided biopsy for pelvic masses, and to analyze the diagnostic performance of these methods in oncologic patients. METHODS: A consecutive series of 30 patients who underwent TRUS- or TVUS-guided biopsy for pelvic masses was included. Tissue samples were obtained using an 18-gauge core biopsy needle under local anesthesia for lesions detected on computed tomography or magnetic resonance imaging. We analyzed the rates of diagnostic biopsies upon pathologic examination and biopsy-related major complications requiring treatment. In diagnostic biopsy cases, the performance was also investigated for all patients and patients with underlying malignancy. RESULTS: The diagnostic biopsy rate was 93.3% (28 of 30) for all patients and 95.0% (19 of 20) for oncologic patients. No patients had major complications. In diagnostic biopsies, the sensitivity, specificity, positive and negative predictive value, and accuracy for identifying malignancy were 100% (17 of 17), 72.7% (8 of 11), 85.0% (17 of 20), 100% (8 of 8), and 89.3% (25 of 28) for all patients and 100% (14 of 14), 60.0% (3 of 5), 87.5% (14 of 16), 100% (3 of 3), and 89.5% (17 of 19) for oncologic patients, respectively. CONCLUSION: This study externally validated the feasibility and safety of TRUS- or TVUS-guided biopsy. In addition, these techniques appear to enable accurate pathologic diagnoses of pelvic masses in oncologic patients to be made safely and relatively noninvasively.