Cochlear schwannomas, which are categorized into intracochlear and intravestibulocochlear schwannomas (ICs and IVCs, respectively) are rare and may cause hearing loss (HL). The affected region is invariably correlated with tumor location, which can be detected on magnetic resonance imaging (MRI). We describe the cochleovestibular manifestations of ICs and IVCs. Subjects and Methods: The study included 31 patients with ICs or IVCs. Tumor extent and exact locations were delineated using MRI. Types of HL were subcategorized into the low-to-mid frequency (250 Hz to 1 kHz), mid-to-high frequency (>1 kHz), and all-frequency (universal) HL groups. Results: The tumors involved the entire cochlear turn (two ICs) or extended beyond the cochleae (nine IVCs) in 11 patients, and 20 ICs were located in specific locations as follows: 14 in the basal, 3 in the middle, and 3 in the middle and apical turns. No patient showed tumor invasion of the internal auditory canal or middle ear. The pattern of HL usually reflects the location or extent of a tumor. We observed HL at all frequencies, at low-to-mid frequencies, and at mid-to-high frequencies in 13, 4, and 14 patients, respectively. Dizziness or tinnitus was observed in >50% of patients. Surgical tumor removal was performed in 10 patients, and the remaining patients are undergoing annual monitoring. Conclusions: Cochlear schwannomas may be associated with HL, which may worsen over time and reflect tumor location. Therefore, these lesions should be considered in the differential diagnosis in patients who present with idiopathic, fluctuating, progressive or sudden HL.

Purpose: Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and18F-fluorodeoxyglucose ( 18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients withOPSCC. Materials and Methods: Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set. Results: A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564–0.959], 0.638 (95% CI, 0.404–0.871), and 0.823 (95% CI, 0.668–0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119. Conclusion When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18 F-fluorodeoxyglucose ( 18 F-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 18 F-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 18 F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

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.

The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613–0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467–0.759) and 0.663 (95% CI, 0.531–0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.

Purpose: A thyroid nodule with an isolated macrocalcification is visualized as a calcified nodule with complete posterior shadowing on ultrasonography (US). This study aimed to determine the computed tomography (CT) features of isolated macrocalcifications detected using US. Methods: This study included 20 patients who had thyroid nodules with isolated macroalcifications and underwent neck CT or chest CT. The patients were enrolled from a sample of 82 patients with isolated macrocalcifications detected by US drawn from 7,142 consecutive patients who underwent thyroid biopsy at two institutions. We evaluated the CT features of nodules with isolated macrocalcifications and categorized them as central or rim calcifications. We assessed the nodule size and the frequency of nondiagnostic fine-needle aspiration (FNA) results and malignant tumors according to the CT features of isolated macrocalcifications. Results: CT scans showed central calcifications in 18 (90.0%) and rim calcifications in two (10.0%) of the 20 nodules with isolated macrocalcifications. Among the 18 nodules with central isolated macrocalcifications, complete compact calcification was found in six nodules and partial coarse calcification in 12 nodules. In 18 nodules with central isolated macrocalcifications, the nondiagnostic FNA rate and frequency of malignant tumors were not significantly different between complete and partial central calcifications (P=0.620 and P=0.999, respectively). Malignant tumors were only found in nodules with central isolated macrocalcifications. Conclusion The majority of nodules with isolated macrocalcifications showed central calcifications on CT. Thyroid nodules with isolated macrocalcifications detected by US should not be classified as having a type of rim or peripheral calcification.

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.

OBJECTIVE: The purpose of this study was to determine the diagnostic utility of low-dose CT with knowledge-based iterative model reconstruction (IMR) for the evaluation of parotid gland tumors. MATERIALS AND METHODS: This prospective study included 42 consecutive patients who had undergone low-dose contrast-enhanced CT for the evaluation of suspected parotid gland tumors. Prior or subsequent non-low-dose CT scans within 12 months were available in 10 of the participants. Background noise (BN), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between non-low-dose CT images and images generated using filtered back projection (FBP), hybrid iterative reconstruction (iDose⁴; Philips Healthcare), and knowledge-based IMR. Subjective image quality was rated by two radiologists using five-point grading scales to assess the overall image quality, delineation of lesion contour, image sharpness, and noise. RESULTS: With the IMR algorithm, background noise (IMR, 4.24 ± 3.77; iDose⁴, 8.77 ± 3.85; FBP, 11.73 ± 4.06; p = 0.037 [IMR vs. iDose⁴] and p < 0.001 [IMR vs. FBP]) was significantly lower and SNR (IMR, 23.93 ± 7.49; iDose⁴, 10.20 ± 3.29; FBP, 7.33 ± 2.03; p = 0.011 [IMR vs. iDose⁴] and p < 0.001 [IMR vs. FBP]) was significantly higher compared with the other two algorithms. The CNR was also significantly higher with the IMR compared with the FBP (25.76 ± 11.88 vs. 9.02 ± 3.18, p < 0.001). There was no significant difference in BN, SNR, and CNR between low-dose CT with the IMR algorithm and non-low-dose CT. Subjective image analysis revealed that IMR-generated low-dose CT images showed significantly better overall image quality and delineation of lesion contour with lesser noise, compared with those generated using FBP by both reviewers 1 and 2 (4 vs. 3; 4 vs. 3; and 3–4 vs. 2; p < 0.05 for all pairs), although there was no significant difference in subjective image quality scores between IMR-generated low-dose CT and non-low-dose CT images. CONCLUSION: Iterative model reconstruction-generated low-dose CT is an alternative to standard non-low-dose CT without significantly affecting image quality for the evaluation of parotid gland tumors.
Feasibility Studies* ; Humans ; Image Processing, Computer-Assisted ; Noise ; Parotid Gland* ; Prospective Studies ; Radiation Dosage ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed* ; Weights and Measures

PURPOSE: The purpose of this study is to compare the performance of the T1 3D subtraction technique and the conventional 2D dynamic contrast enhancement (DCE) technique in diagnosing Cushing's disease. MATERIALS AND METHODS: Twelve patients with clinically and biochemically proven Cushing's disease were included in the study. In addition, 23 patients with a Rathke's cleft cyst (RCC) diagnosed on an MRI with normal pituitary hormone levels were included as a control, to prevent non-blinded positive results. Postcontrast T1 3D fast spin echo (FSE) images were acquired after DCE images in 3T MRI and image subtraction of pre- and postcontrast T1 3D FSE images were performed. Inter-observer agreement, interpretation time, multiobserver receiver operating characteristic (ROC), and net benefit analyses were performed to compare 2D DCE and T1 3D subtraction techniques. RESULTS: Inter-observer agreement for a visual scale of contrast enhancement was poor in DCE (κ = 0.57) and good in T1 3D subtraction images (κ = 0.75). The time taken for determining contrast-enhancement in pituitary lesions was significantly shorter in the T1 3D subtraction images compared to the DCE sequence (P < 0.05). ROC values demonstrated increased reader confidence range with T1 3D subtraction images (95% confidence interval [CI]: 0.94–1.00) compared with DCE (95% CI: 0.70–0.92) (P < 0.01). The net benefit effect of T1 3D subtraction images over DCE was 0.34 (95% CI: 0.12–0.56). For Cushing's disease, both reviewers misclassified one case as a nonenhancing lesion on the DCE images, while no cases were misclassified on T1 3D subtraction images. CONCLUSION: The T1 3D subtraction technique shows superior performance for determining the presence of enhancement on pituitary lesions compared with conventional DCE techniques, which may aid in diagnosing Cushing's disease.
Humans ; Magnetic Resonance Imaging ; ROC Curve ; Subtraction Technique

OBJECTIVE: The purpose of this study was to correlate permeability parameters measured with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a clinical 3-tesla scanner with extravasation of Evans blue in a rat model with transient cerebral ischemia. MATERIALS AND METHODS: Sprague-Dawley rats (n = 13) with transient middle cerebral artery occlusion were imaged using a 3-tesla MRI with an 8-channel wrist coil. DCE-MRI was performed 12 hours, 18 hours, and 36 hours after reperfusion. Permeability parameters (K(trans), v(e), and v(p)) from DCE-MRI were calculated. Evans blue was injected after DCE-MRI and extravasation of Evans blue was correlated as a reference with the integrity of the blood-brain barrier. Correlation analysis was performed between permeability parameters and the extravasation of Evans blue. RESULTS: All permeability parameters (K(trans), v(e), and v(p)) showed a linear correlation with extravasation of Evans blue. Among them, K(trans) showed highest values of both the correlation coefficient and the coefficient of determination (0.687 and 0.473 respectively, p < 0.001). CONCLUSION: Permeability parameters obtained by DCE-MRI at 3-T are well-correlated with Evans blue extravasation, and K(trans) shows the strongest correlation among the tested parameters.
Animals ; Blood-Brain Barrier/pathology ; Capillary Permeability ; Contrast Media ; Disease Models, Animal ; Evans Blue/analysis ; Ischemic Attack, Transient/*diagnosis ; Magnetic Resonance Imaging/instrumentation/*methods ; Male ; Rats ; Rats, Sprague-Dawley ; Stroke/diagnosis