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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.

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 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.

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.

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