Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy.

OBJECTIVE: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learning-based image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI (routine MRI) and 1.5-mm slice thickness MRI without DLR (1.5-mm MRI without DLR) for evaluating temporal lobe epilepsy (TLE). MATERIALS AND METHODS: This retrospective study included 117 MR image sets comprising 1.5-mm MRI + DLR, 1.5-mm MRI without DLR, and routine MRI from 117 consecutive patients (mean age, 41 years; 61 female; 34 patients with TLE and 83 without TLE). Two neuroradiologists evaluated the presence of hippocampal or temporal lobe lesions, volume loss, signal abnormalities, loss of internal structure of the hippocampus, and lesion conspicuity in the temporal lobe. Reference standards for TLE were independently constructed by neurologists using clinical and radiological findings. Subjective image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed. Performance in diagnosing TLE, lesion findings, and image quality were compared among the three protocols. RESULTS: The pooled sensitivity of 1.5-mm MRI + DLR (91.2%) for diagnosing TLE was higher than that of routine MRI (72.1%, P < 0.001). In the subgroup analysis, 1.5-mm MRI + DLR showed higher sensitivity for hippocampal lesions than routine MRI (92.7% vs. 75.0%, P = 0.001), with improved depiction of hippocampal T2 high signal intensity change (P = 0.016) and loss of internal structure (P < 0.001). However, the pooled specificity of 1.5-mm MRI + DLR (76.5%) was lower than that of routine MRI (89.2%, P = 0.004). Compared with 1.5-mm MRI without DLR, 1.5-mm MRI + DLR resulted in significantly improved pooled accuracy (91.2% vs. 73.1%, P = 0.010), image quality, SNR, and CNR (all, P < 0.001). CONCLUSION: The use of 1.5-mm MRI + DLR enhanced the performance of MRI in diagnosing TLE, particularly in hippocampal evaluation, because of improved depiction of hippocampal abnormalities and enhanced image quality.

Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study.

The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment. This retrospective study included patients who underwent CT for abnormal cervical LN and lymphoma assessment between January 2021 and July 2022. Patients were grouped into the development (n = 76), internal test 1 (n = 27), internal test 2 (n = 87), and external test (n = 26) cohorts. A 3D SegResNet model was used to train the CT images. The volume change rates of cervical LN across longitudinal CT scans were compared among patients with different treatment outcomes (stable, response, and progression). Dice similarity coefficient (DSC) and the Bland-Altman plot were used to assess the model's segmentation performance and reliability, respectively. No significant differences in baseline clinical characteristics were found across cohorts (age, P = 0.55; sex, P = 0.13; diagnoses, P = 0.06). The mean DSC was 0.39 ± 0.2 with a precision and recall of 60.9% and 57.0%, respectively. Most LN volumes were within the limits of agreement on the Bland-Altman plot. The volume change rates among the three groups differed significantly (progression (n = 74), 342.2%; response (n = 8), - 79.2%; stable (n = 5), - 8.1%; all P < 0.01). Our proposed DL segmentation model showed modest performance in quantifying the cervical LN burden on CT in patients with lymphoma. Longitudinal changes in cervical LN volume, as predicted by the DL model, were useful for treatment response assessment.

Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.

PURPOSE: To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. METHODS: This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. RESULTS: All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93). CONCLUSION: The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. CLINICAL RELEVANCE STATEMENT: Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. KEY POINTS: • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.

Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints.

PURPOSE: Recent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma. METHODS: PubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test. RESULTS: Among 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58-0.72) and 2.88 (95 % CI, 2.28-3.64), whereas those for PFS were 0.61 (95 % CI, 0.55-0.66) and 2.78 (95 % CI, 1.91-4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58-0.89) and 0.60 (95 % CI, 0.45-0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall. CONCLUSION: Pretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.

Ultrasound LI-RADS Visualization Scores on Surveillance Ultrasound for Hepatocellular Carcinoma: A Systematic Review With Meta-analysis.

We performed a systematic review and meta-analysis to determine the proportions of each surveillance ultrasound (US) visualization score for hepatocellular carcinoma based on the US Liver Imaging Reporting and Data System (LI-RADS) and to identify the factors associated with visualization score C. Original publications reporting US LI-RADS visualization scores were identified in MEDLINE and EMBASE from January 1, 2017, to November 25, 2022. The meta-analytic pooled proportion of each visualization score based on US examination was calculated using the DerSimonian‒Laird random-effects model. Subgroup meta-regression analyses were performed to explore study heterogeneity. US LI-RADS visualization scores were reported from a total of 25,698 US examinations across 12 studies. The pooled proportions of visualization scores A, B and C were 56.7% (95% confidence interval [CI]: 38.6-73.2%, I2 = 99.2%), 30.3% (95% CI: 21.5-40.7%, I2 = 98.8%) and 6.9% (95% CI: 3.9-11.7%, I2 = 97.7%), respectively. Significantly higher proportions of visualization score C were found in studies that exclusively enrolled cirrhosis patients and a study in which the disease etiology was non-alcoholic fatty liver disease (NAFLD) (p < 0.05). In addition, the pooled proportion of visualization score C was higher in studies with a mean or median body mass index >25 kg/m2 (10.7%, 95% CI: 4.3-24.3%). In conclusion, a substantial proportion of surveillance US examinations exhibited moderate to severe limitations on visualization. There was a tendency toward higher proportions of US LI-RADS visualization score C in patients with cirrhosis, NAFLD and obesity.

Glioma grading using multiparametric MRI: head-to-head comparison among dynamic susceptibility contrast, dynamic contrast-enhancement, diffusion-weighted images, and MR spectroscopy.

PURPOSE: To assess the diagnostic accuracy of dynamic susceptibility contrast, dynamic contrast-enhancement, MR spectroscopy (MRS), and diffusion-weighted imaging for differentiating high-grade (HGGs) from low-grade gliomas (LGGs). METHODS: Seventy-two patients (16 LGGs, 56 HGGs) with pathologically confirmed gliomas were retrospectively included. From three-dimensionally segmented tumor, histogram analyses of relative cerebral blood volume (rCBV), volume transfer constant (Ktrans), and apparent diffusion coefficient (ADC) were performed. Choline-to-creatinine ratio (Cho/Cr) was calculated using MRS. Logistic regression analyses were performed to differentiate HGGs (grade ≥ 3) from LGGs (grade ≤ 2). Areas under the receiver operating characteristics curves (AUC) were plotted. Subgroup analysis was performed between IDH-wildtype glioblastomas and IDH-mutant astrocytomas. Pairwise Spearman's correlation coefficients (ρ) were computed. RESULTS: HGGs had higher 95th percentile rCBV, Ktrans and Cho/Cr (P < 0.01) than LGGs. AUC of 95th percentiles of rCBV and Ktrans were 0.79 (95% CI, 0.67-0.91) and 0.74 (95% CI, 0.59-0.88), respectively. AUC of 5th percentile of ADC was 0.63 (95% CI, 0.48-0.79), and that of Cho/Cr was 0.67 (95% CI, 0.52-0.81). IDH-wildtype glioblastomas and IDH-mutant astrocytomas showed significantly different 95th percentile rCBV (P = 0.04) and Ktrans (P < 0.01), with Ktrans showing the highest AUC (0.73, 95% CI 0.57-0.89) in IDH status prediction. Moderate correlations were observed between 95th percentile rCBV and Ktrans (ρ = 0.47), Cho/Cr (ρ = 0.40), and 5th percentile ADC (ρ = -0.36) (all P < 0.01). CONCLUSIONS: The 95th percentile rCBV may be most helpful in discriminating HGGs from LGGs. The 95th percentile Ktrans may aid predicting IDH status of diffuse gliomas.

Correlation between radiologic depth of invasion and pathologic depth of invasion in oral cavity squamous cell carcinoma: A systematic review and meta-analysis.

OBJECTIVES: To comprehensively assess the correlation between radiologic depth of invasion (rDOI) and pathologic depth of invasion (pDOI) in oral cavity squamous cell carcinoma (OSCC) by meta-analysis. MATERIALS AND METHODS: PubMed and Embase databases were searched to find pertinent articles reporting rDOI of OSCC. Studies evaluating the correlations and mean differences (MDs) between rDOI and pDOI were included. The rDOI was measured based on ultrasound (US), computed tomography (CT), or magnetic resonance imaging (MRI). The correlation coefficients and MDs between rDOI and pDOI were meta-analytically pooled. Between-study heterogeneity was assessed using Higgins' inconsistency index (I2). Subgroup analysis was performed based on imaging modality. RESULTS: Twenty-three studies with 1787 patients were included. The pooled correlation coefficient and MD were 0.86 (95 % confidence interval [CI], 0.82-0.90; I2 = 66.9 %) and 1.84 mm (95 % CI, 1.02-2.65 mm; I2 = 88.2 %), respectively. In subgroup analysis, MRI showed the largest MD (n = 12, 2.61 mm), followed by US (n = 2, -0.41 mm) and CT (n = 2, 0.12 mm). US showed the highest correlation coefficient (n = 3, 0.91), followed by MRI (n = 12, 0.85) and CT (n = 3, 0.82). CONCLUSION: rDOI measured by US, CT, and MRI demonstrated excellent correlations with pDOI.

MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI. METHODS: This retrospective study included T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) images from 185 clinical scans: 135 for DNN training, 11 for DNN validation, 20 for qualitative evaluation, and 19 for quantitative evaluation. Additionally, 18 vessel wall imaging (VWI) data were included to evaluate generalization. In each scan of the DNN training set, two noise-independent images were generated from the k-space data, resulting in an input-label pair. 2.5D U-net architecture was utilized for the DNN model. Qualitative evaluation between conventional MP-RAGE and DNN-based MP-RAGE was performed by two radiologists in image quality, fine structure delineation, and lesion conspicuity. Quantitative evaluation was performed with full sampled data as a reference by measuring quantitative error metrics and volumetry at 7 different simulated noise levels. DNN application on VWI was evaluated by two radiologists in image quality. RESULTS: Our DNN-based MP-RAGE outperformed conventional MP-RAGE in all image quality parameters (average scores = 3.7 vs. 4.9, p < 0.001). In the quantitative evaluation, DNN showed better error metrics (p < 0.001) and comparable (p > 0.09) or better (p < 0.02) volumetry results than conventional MP-RAGE. DNN application to VWI also revealed improved image quality (3.5 vs. 4.6, p < 0.001). CONCLUSION: The proposed DNN model successfully denoises 3D MR image and improves its image quality by using routine clinical scans only. KEY POINTS: • Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI.

Diagnosis of nasal bone fractures on plain radiographs via convolutional neural networks.

This study aimed to assess the performance of deep learning (DL) algorithms in the diagnosis of nasal bone fractures on radiographs and compare it with that of experienced radiologists. In this retrospective study, 6713 patients whose nasal radiographs were examined for suspected nasal bone fractures between January 2009 and October 2020 were assessed. Our dataset was randomly split into training (n = 4325), validation (n = 481), and internal test (n = 1250) sets; a separate external dataset (n = 102) was used. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the DL algorithm and the two radiologists were compared. The AUCs of the DL algorithm for the internal and external test sets were 0.85 (95% CI, 0.83-0.86) and 0.86 (95% CI, 0.78-0.93), respectively, and those of the two radiologists for the external test set were 0.80 (95% CI, 0.73-0.87) and 0.75 (95% CI, 0.68-0.82). The DL algorithm therefore significantly exceeded radiologist 2 (P = 0.021) but did not significantly differ from radiologist 1 (P = 0.142). The sensitivity and specificity of the DL algorithm were 83.1% (95% CI, 71.2-93.2%) and 83.7% (95% CI, 69.8-93.0%), respectively. Our DL algorithm performs comparably to experienced radiologists in diagnosing nasal bone fractures on radiographs.

Image Quality of High-Resolution 3-Dimensional Neck MRI Using CAIPIRINHA-VIBE and GRASP-VIBE: An Intraindividual Comparative Study.

OBJECTIVES: Acquiring high-quality magnetic resonance imaging (MRI) of the head and neck region is often challenging due to motion and susceptibility artifacts. This study aimed to compare image quality of 2 high-resolution three-dimensional (3D) MRI sequences of the neck, controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), and golden-angle radial sparse parallel imaging (GRASP)-VIBE. MATERIALS AND METHODS: One hundred seventy-three patients indicated for contrast-enhanced neck MRI examination were scanned using 3 T scanners and both CAIPIRINHA-VIBE and GRASP-VIBE with nearly isotropic 3D acquisitions (<1 mm in-plane resolution with analogous acquisition times). Patients' MRI scans were independently rated by 2 radiologists using a 5-grade Likert scale for overall image quality, artifact level, mucosal and lesion conspicuity, and fat suppression degree at separate anatomical regions. Interobserver agreement was calculated using the Cohen κ coefficient. The quality ratings of both sequences were compared using the Mann-Whitney U test. Nonuniformity and contrast-to-noise ratio values were measured in all subjects. Separate MRI scans were performed twice for each sequence in a phantom and healthy volunteer without contrast injection to calculate the signal-to-noise ratio (SNR). RESULTS: The scores of overall image quality, overall artifact level, motion artifact level, and conspicuity of the nasopharynx, oropharynx, oral cavity, hypopharynx, and larynx were all significantly higher in GRASP-VIBE than in CAIPIRINHA-VIBE (all P 's < 0.001). Moderate to substantial interobserver agreement was observed in overall image quality (GRASP-VIBE κ = 0.43; CAIPIRINHA-VIBE κ = 0.59) and motion artifact level (GRASP-VIBE κ = 0.51; CAIPIRINHA-VIBE κ = 0.65). Lesion conspicuity was significantly higher in GRASP-VIBE than in CAIPIRINHA-VIBE ( P = 0.005). The degree of fat suppression was weaker in the lower neck regions in GRASP-VIBE (3.90 ± 0.72) than in CAIPIRINHA-VIBE (4.97 ± 0.21) ( P < 0.001). The contrast-to-noise ratio at hypopharyngeal level was significantly higher in GRASP-VIBE (6.28 ± 4.77) than in CAIPIRINHA-VIBE (3.14 ± 9.95) ( P < 0.001). In the phantom study, the SNR of GRASP-VIBE was 12 times greater than that of CAIPIRINHA-VIBE. The in vivo SNR of the volunteer MRI scan was 13.6 in CAIPIRINHA-VIBE and 20.7 in GRASP-VIBE. CONCLUSIONS: Both sequences rendered excellent images for head and neck MRI scans. GRASP-VIBE provided better image quality, as well as mucosal and lesion conspicuities, with less motion artifacts, whereas CAIPIRINHA-VIBE provided better fat suppression in the lower neck regions.

Clinical Implications of Focal Mineral Deposition in the Globus Pallidus on CT and Quantitative Susceptibility Mapping of MRI.

OBJECTIVE: To assess focal mineral deposition in the globus pallidus (GP) by CT and quantitative susceptibility mapping (QSM) of MRI scans and evaluate its clinical significance, particularly cerebrovascular degeneration. MATERIALS AND METHODS: This study included 105 patients (66.1 ± 13.7 years; 40 male and 65 female) who underwent both CT and MRI with available QSM data between January 2017 and December 2019. The presence of focal mineral deposition in the GP on QSM (GPQSM) and CT (GPCT) was assessed visually using a three-point scale. Cerebrovascular risk factors and small vessel disease (SVD) imaging markers were also assessed. The clinical and radiological findings were compared between the different grades of GPQSM and GPCT. The relationship between GP grades and cerebrovascular risk factors and SVD imaging markers was assessed using univariable and multivariable linear regression analyses. RESULTS: GPCT and GPQSM were significantly associated (p < 0.001) but were not identical. Higher GPCT and GPQSM grades showed smaller gray matter (p = 0.030 and p = 0.025, respectively) and white matter (p = 0.013 and p = 0.019, respectively) volumes, as well as larger GP volumes (p < 0.001 for both). Among SVD markers, white matter hyperintensity was significantly associated with GPCT (p = 0.006) and brain atrophy was significantly associated with GPQSM (p = 0.032) in at univariable analysis. In multivariable analysis, the normalized volume of the GP was independently positively associated with GPCT (p < 0.001) and GPQSM (p = 0.002), while the normalized volume of the GM was independently negatively associated with GPCT (p = 0.040) and GPQSM (p = 0.035). CONCLUSION: Focal mineral deposition in the GP on CT and QSM might be a potential imaging marker of cerebral vascular degeneration. Both were associated with increased GP volume.

Deep convolutional neural network for classification of thyroid nodules on ultrasound: Comparison of the diagnostic performance with that of radiologists.

PURPOSE: This study aimed to train and validate deep learning (DL) models for differentiating malignant from benign thyroid nodules on US images and compare their performance with that of radiologists. METHODS: Images of thyroid nodules in patients who underwent US-guided fine-needle aspiration biopsy at our institution between January 2010 and March 2020 were retrospectively reviewed. Four radiologists independently classified the images. Images of thyroid nodules were trained using three different image classification DL models (VGG16, VGG19, and ResNet). The diagnostic performances of the DL models were calculated for the internal and external datasets and compared with the diagnoses of the four radiologists. Pairwise comparisons of the AUCs between the radiologists and DL models were made using bootstrap-based tests. RESULTS: In total, 15,409 images from 7,321 patients (mean age, 60 ± 13 years; malignant nodules, 20.7%) were randomly grouped into training (n = 12,327) and validation (n = 3,082) sets. Independent internal (n = 432; 197 patients) and external (n = 168; 59 patients) test sets were also acquired. The DL models demonstrated a higher diagnostic performance than the radiologists in the internal test set (AUC, 0.83 - 0.86 vs. 0.71 - 0.76, P < 0.05), but not in the external test set. The VGG16 model demonstrated the highest diagnostic performance in internal (AUC, 0.86; sensitivity, 91.8%; specificity, 73.2%) and external (AUC: 0.83; sensitivity: 78.6%; specificity: 76.8%) test sets. However, no statistical differences were found in the AUCs among the DL models. CONCLUSIONS: The DL models demonstrated comparable diagnostic performance to radiologists in distinguishing benign from malignant thyroid nodules on US images and may play a potential role in augmenting radiologists' diagnosis of thyroid nodules.

Magnetic Resonance Imaging-Based Radiomics for the Prediction of Progression-Free Survival in Patients with Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.

Advanced non-metastatic nasopharyngeal carcinoma (NPC) has variable treatment outcomes. However, there are no prognostic biomarkers for identifying high-risk patients with NPC. The aim of this systematic review and meta-analysis was to comprehensively assess the prognostic value of magnetic resonance imaging (MRI)-based radiomics for untreated NPC. The PubMed-Medline and EMBASE databases were searched for relevant articles published up to 12 August 2021. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was used to determine the qualities of the selected studies. Random-effects modeling was used to calculate the pooled estimates of Harrell's concordance index (C-index) for progression-free survival (PFS). Between-study heterogeneity was evaluated using Higgins' inconsistency index (I2). Among the studies reported in the 57 articles screened, 10 with 3458 patients were eligible for qualitative and quantitative data syntheses. The mean adherence rate to the TRIPOD checklist was 68.6 ± 7.1%. The pooled estimate of the C-index was 0.762 (95% confidence interval, 0.687-0.837). Substantial between-study heterogeneity was observed (I2 = 89.2%). Overall, MRI-based radiomics shows good prognostic performance in predicting the PFS of patients with untreated NPC. However, more consistent and robust study protocols are necessary to validate the prognostic role of radiomics for NPC.

Low-Dose Three-Dimensional Rotational Angiography for Evaluating Intracranial Aneurysms: Analysis of Image Quality and Radiation Dose.

OBJECTIVE: This study aimed to evaluate the image quality and dose reduction of low-dose three-dimensional (3D) rotational angiography (RA) for evaluating intracranial aneurysms. MATERIALS AND METHODS: We retrospectively evaluated the clinical data and 3D RA datasets obtained from 146 prospectively registered patients (male:female, 46:100; median age, 58 years; range, 19-81 years). The subjective image quality of 79 examinations obtained from a conventional method and 67 examinations obtained from a low-dose (5-seconds and 0.10-µGy/frame) method was assessed by two neurointerventionists using a 3-point scale for four evaluation criteria. The total image quality score was then obtained as the average of the four scores. The image quality scores were compared between the two methods using a noninferiority statistical testing, with a margin of -0.2 (i.e., score of low-dose group - score of conventional group). For the evaluation of dose reduction, dose-area product (DAP) and air kerma (AK) were analyzed and compared between the two groups. RESULTS: The mean total image quality score ± standard deviation of the 3D RA was 2.97 ± 0.17 by reader 1 and 2.95 ± 0.20 by reader 2 for conventional group and 2.92 ± 0.30 and 2.95 ± 0.22, respectively, for low-dose group. The image quality of the 3D RA in the low-dose group was not inferior to that of the conventional group according to the total image quality score as well as individual scores for the four criteria in both readers. The mean DAP and AK per rotation were 5.87 Gy-cm² and 0.56 Gy, respectively, in the conventional group, and 1.32 Gy-cm² (p < 0.001) and 0.17 Gy (p < 0.001), respectively, in the low-dose group. CONCLUSION: Low-dose 3D RA was not inferior in image quality and reduced the radiation dose by 70%-77% compared to the conventional 3D RA in evaluating intracranial aneurysms.

Preoperative assessment of cervical lymph node metastases in patients with papillary thyroid carcinoma: Incremental diagnostic value of dual-energy CT combined with ultrasound.

PURPOSE: To determine whether dual-energy CT (DECT) has incremental diagnostic value when combined with ultrasound (US) in the diagnosis of metastatic cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC). METHODS: This was a single-center retrospective cohort study of patients diagnosed with PTC between October 2019 and August 2020. US features of LNs to include hyperechogenicity, round shape, microcalcification, cystic component, and homogeneous/peripheral vascularity were considered suggestive of metastasis. The HU of arterial phase (HUarterial) and DECT-derived CT images [contrast media (CM) and areas under the 100 keV monoenergetic curve (AUC100keV)] were measured. Effective atomic numbers (Zeff), iodine concentration (mg/mL), and slope of the HU curve (λHU) were also obtained. The values for metastatic and benign LNs were compared using Student's t-test with false-discovery correction. Logistic regression with areas under the receiver operating characteristic curves (AUCs) were performed for predicting metastatic LNs. RESULTS: A total of 102 patients were included (49 metastatic and 53 benign LNs; mean age, 46±15 years). Metastatic LNs showed significantly higher values for HUarterial, CM, Zeff, λHU, AUC100keV, and iodine concentration (all, P = 0.001). In logistic regression, the HUarterial demonstrated the highest AUC (0.824; 95% confidence interval [CI], 0.751-0.897), followed by CM HU (0.762; 95% CI, 0.679-0.846). Combination of DECT parameters with US features improved the AUC from 0.890 to 0.941. CONCLUSION: Compared to US features alone, combination with DECT-derived quantitative parameters improved diagnostic performance in predicting metastatic cervical LNs in patients with PTC.

Diffusion-weighted MRI for predicting treatment response in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis.

Early prediction of treatment response in nasopharyngeal carcinoma is clinically relevant for optimizing treatment strategies. This meta-analysis was performed to evaluate whether apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can predict treatment response of patients with nasopharyngeal carcinoma. A systematic search of PubMed-MEDLINE and Embase was performed to identify relevant original articles until July 22, 2021. We included studies which performed DWI for predicting locoregional treatment response in nasopharyngeal carcinoma treated with neoadjuvant chemotherapy, definitive chemoradiation, or radiation therapy. Hazard ratios were meta-analytically pooled using a random-effects model for the pooled estimates of overall survival, local relapse-free survival, distant metastasis-free survival and their 95% CIs. ADC showed a pooled sensitivity of 87% (95% CI 72-94%) and specificity of 70% (95% CI 56-80%) for predicting treatment response. Significant between-study heterogeneity was observed for both pooled sensitivity (I2 = 68.5%) and specificity (I2 = 92.2%) (P < 0.01). The pooled hazard ratios of low pretreatment ADC for assessing overall survival, local relapse-free survival, and distant metastasis-free survival were 1.42 (95% CI 1.09-1.85), 2.31 (95% CI 1.42-3.74), and 1.35 (95% CI 1.05-1.74), respectively. In patients with nasopharyngeal carcinoma, pretreatment ADC demonstrated good predictive performance for treatment response.

Adverse effects of hypertension, supine hypertension, and perivascular space on cognition and motor function in PD.

Dilated perivascular space (dPVS) has recently been reported as a biomarker for cognitive impairment in Parkinson's disease (PD). However, comprehensive interrelationships between various clinical risk factors, dPVS, white-matter hyperintensities (WMH), cognition, and motor function in PD have not been studied yet. The purpose of this study was to test whether dPVS might mediate the effect of clinical risk factors on WMH, cognition, and motor symptoms in PD patients. A total of 154 PD patients were assessed for vascular risk factors (hypertension, diabetes mellitus, and dyslipidemia), autonomic dysfunction (orthostatic hypotension and supine hypertension [SH]), APOE ε4 genotype, rapid eye movement sleep-behavior disorder, motor symptoms, and cognition status. The degree of dPVS was evaluated in the basal ganglia (BG) and white matter using a 5-point visual scale. Periventricular, deep, and total WMH severity was also assessed. Path analysis was performed to evaluate the associations of these clinical factors and imaging markers with cognitive status and motor symptoms. Hypertension and SH were significantly associated with more severe BGdPVS, which was further associated with higher total WMH, consequently leading to lower cognitive status. More severe BGdPVS was also associated with worse motor symptoms, but without mediation of total WMH. Similar associations were seen when using periventricular WMH as a variable, but not when using deep WMH as a variable. In conclusion, BGdPVS mediates the effect of hypertension and SH on cognitive impairment via total and periventricular WMH, while being directly associated with more severe motor symptoms.

Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics.

OBJECTIVES: To assess the additive prognostic value of MR-based radiomics in predicting progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC) METHODS: Patients newly diagnosed with non-metastatic NPC between June 2006 and October 2019 were retrospectively included and randomly grouped into training and test cohorts (7:3 ratio). Radiomic features (n=213) were extracted from T2-weighted and contrast-enhanced T1-weighted MRI. The patients were staged according to the 8th edition of American Joint Committee on Cancer Staging Manual. The least absolute shrinkage and selection operator was used to select the relevant radiomic features. Univariate and multivariate Cox proportional hazards analyses were conducted for PFS, yielding three different survival models (clinical, stage, and radiomic). The integrated time-dependent area under the curve (iAUC) for PFS was calculated and compared among different combinations of survival models, and the analysis of variance was used to compare the survival models. The prognostic performance of all models was validated using a test set with integrated Brier scores. RESULTS: This study included 81 patients (training cohort=57; test cohort=24), and the mean PFS was 57.5 ± 43.6 months. In the training cohort, the prognostic performances of survival models improved significantly with the addition of radiomics to the clinical (iAUC, 0.72-0.80; p=0.04), stage (iAUC, 0.70-0.79; p=0.001), and combined models (iAUC, 0.76-0.81; p<0.001). In the test cohort, the radiomics and combined survival models were robustly validated for their ability to predict PFS. CONCLUSION: Integration of MR-based radiomic features with clinical and stage variables improved the prediction PFS in patients diagnosed with NPC.

Prognostic value of MRI-measured tumor thickness in patients with tongue squamous cell carcinoma.

This study aimed to assess the prognostic value of MRI-measured tumor thickness (MRI-TT) in patients with tongue squamous cell carcinoma (SCC). This single-center retrospective cohort study included 133 pathologically confirmed tongue SCC patients between January 2009 and October 2019. MRI measurements of tongue SCC were based on axial and coronal T2-weighted (T2WI) and contrast-enhanced T1-weighted (CE-T1WI) images. Two radiologists independently measured MRI-TT. Intraclass correlation coefficients (ICC) were calculated for inter-rater agreements. Spearman's rank correlation between MRI-TT and pathologic depth of invasion (pDOI) was assessed. Cox proportional hazards analyses on recurrence-free (RFS) and overall survival (OS) were performed for MRI-TT and pDOI. Kaplan-Meier survival curves were plotted with log-rank tests. The intra- and inter-rater agreements of MRI-TT were excellent (ICC: 0.829-0.897, all P < 0.001). The correlation between MRI-TT and pDOI was good (Spearman's correlation coefficients: 0.72-0.76, P < 0.001). MRI-TT were significantly greater than pDOI in all axial and coronal T2WI and CE-T1WI (P < 0.001). In multivariate Cox proportional hazards analysis, MRI-TT measured on axial CE-T1WI yielded a significant prognostic value for OS (hazards ratio 2.77; P = 0.034). MRI-TT demonstrated excellent intra- and inter-rater agreements as well as high correlation with pDOI. MRI-TT may serve as a prognostic predictor in patients with tongue SCC.