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Purpose: To evaluate the impact of application acquisition and reconstruction with motion suppression (ARMS) technology on improving the image quality of diffusion-weighted Imaging (DWI) for nasopharyngeal carcinoma (NPC), compared to single-shot echo-planar imaging (SS-EPI)., Methods: A total of 90 patients with NPC underwent MR examination, including ARMS DWI and SS-EPI DWI sequences. Both DWI sequences were acquired with b-values 0 and 800 s/mm 2 . Two radiologists evaluated the visibility of the lesion, geometric distortion, and overall image quality of the two DWI sequences. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion degree, and apparent diffusion coefficient (ADC) values of the nasopharyngeal lesions were assessed and compared for two sequences. The Wilcoxon signed-rank test was used to compare the quantitative and qualitative parameters of the two sequences., Results: The lesion visibility, geometric distortion, and overall image quality scores were significantly higher in ARMS DWI (all P<0.001). Four small-sized lesions were not visible and four lesions were partially visible in the SS-EPI DWI sequence. Lesion detection rate of ARMS DWI is 100 %, while that of SS-EPI is 95.56 %, P<0.043. The mismatch distance between the fusion images of ARMS DWI and T2WI was smaller than that of SS-EPI DWI and T2WI (all P<0.001). The SNR and CNR of ARMS DWI were lower than that of SS-EPI DWI (114.48 ± 37.89 vs. 202.61 ± 78.84, P<0.001 and 1.81 ± 1.84 vs. 3.29 ± 3.71, P<0.003) while the ADC value was higher (839.19 ± 138.44 × 10 -6 mm 2 /s vs. 788.82 ± 110.96 × 10 -6 mm 2 /s, P<0.002)., Conclusion: ARMS DWI improves the image quality by reducing geometric distortion and magnetic susceptibility artifacts. ARMS DWI is superior to SS-EPI DWI for diagnosing small-sized nasopharyngeal lesions, although it has lower SNR and CNR., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: To establish nomograms integrating multiparametric MRI radiomics with clinical-radiological features to identify the responders and non-responders to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC)., Methods: We retrospectively analyzed the clinical and MRI data of 168 NPC patients between December 2015 and April 2022. We used 3D-Slicer to segment the regions of interest (ROIs) and the "Pyradiomic" package to extract radiomics features. We applied the least absolute shrinkage and selection operator regression to select radiomics features. We developed clinical-only, radiomics-only, and the combined clinical-radiomics nomograms using logistic regression analysis. The receiver operating characteristic curves, DeLong test, calibration, and decision curves were used to assess the discriminative performance of the models. The model was internally validated using 10-fold cross-validation., Results: A total of 14 optimal features were finally selected to develop a radiomic signature, with an AUC of 0.891 (95 % CI, 0.825-0.946) in the training cohort and 0.837 (95 % CI, 0.723-0.932) in the testing cohort. The nomogram based on the Rad-Score and clinical-radiological factors for evaluating tumor response to ICT yielded an AUC of 0.926 (95 % CI, 0.875-0.965) and 0.901 (95 % CI, 0.815-0.979) in the two cohorts, respectively. Decision curves demonstrated that the combined clinical-radiomics nomograms were clinically useful., Conclusion: Nomograms integrating multiparametric MRI-based radiomics and clinical-radiological features could non-invasively discriminate ICT responders from non-responders in NPC patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Purpose: To investigate the feasibility of synthetic MRI (syMRI) quantitative parameters and its combination with morphological features in discriminating stage T1 nasopharyngeal carcinoma (T1-NPC) and benign hyperplasia (BH)., Material and Methods: Eighty-eight patients with nasopharyngeal lesions (T1-NPC, n = 54; BH, n = 34) were retrospectively enrolled between October 2020 and May 2022. The syMRI quantitative parameters of nasopharyngeal lesions (T1, T2, PD, T1 SD , T2 SD , PD SD ) and longus capitis (T1, T2, PD) were measured, and T1 ratio , T2 ratio and PD ratio were calculated (lesion/longus capitis). The morphological features (lesion pattern, retention cyst, serrated protrusion, middle ear effusion, tumor volume, and retropharyngeal lymph node) were compared. Statistical analyses were performed using the independent sample t test, Chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test., Results: The T1, T2, PD, T1 SD , T1 ratio , and T2 ratio values of T1-NPC were significantly lower than those of BH. The morphological features (lesion pattern, retention cyst, retropharyngeal lymph node) were significant difference between these two entities. T2 value has the highest AUC in all syMRI quantitative parameters, followed by T1, T1 ratio , PD, T2 ratio and T1 SD . Combined syMRI quantitative parameters (T2, PD, T1 ratio ) can further improve the diagnosis efficiency. Combined syMRI parameters and morphological feature (T2, PD, lesion pattern, retropharyngeal lymph node) has the excellent diagnostic efficiency, with AUC, sensitivity, specificity, and accuracy of 0.979, 96.30%, 97.06%, 96.77%., Conclusions: Synthetic MRI was helpful in distinguishing T1-NPC from BH, and combined syMRI quantitative parameters and morphological features has the optimal diagnostic performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objectives: Accuracy in the detection of recurrent nasopharyngeal carcinoma (NPC) on follow-up magnetic resonance (MR) scans needs to be improved., Material and Methods: A total of 5 035 follow-up MR scans from 5 035 survivors with treated NPC between April 2007 and July 2020 were retrospectively collected from three cancer centers for developing and evaluating the deep learning (DL) model MODERN (MR-based Deep learning model for dEtecting Recurrent Nasopharyngeal carcinoma). In a reader study with 220 scans, the accuracy of two radiologists in detecting recurrence on scans with vs without MODERN was evaluated. The performance was measured using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy with a 95% confidence interval (CI)., Results: MODERN exhibited sound performance in the validation cohort (internal: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 1: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 2: ROC-AUC, 0.85, 95% CI, 0.82-0.88). In a reader study, MODERN alone achieved reliable accuracy compared to that of radiologists (MODERN: 84.1%, 95% CI, 79.3%-88.9%; competent: 80.9%, 95% CI, 75.7%-86.1%, P < 0.001; expert: 85.9%, 95% CI, 81.3%-90.5%, P < 0.001). The accuracy of radiologists was boosted by the MODERN score (competent with MODERN score: 84.6%, 95% CI, 79.8%-89.3%, P < 0.001; expert with MODERN score: 87.7%, 95% CI, 83.4%-92.1%, P < 0.001)., Conclusion: We developed a DL model for recurrence detection with reliable performance. Computer-human collaboration has the potential to refine the workflow in interpreting surveillant MR scans among patients with treated NPC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: MRI based radiomics has the potential to better define tumor biology compared to qualitative MRI assessment and support decisions in patients affected by nasopharyngeal carcinoma. Aim of this review was to systematically evaluate the methodological quality of studies using MRI- radiomics for nasopharyngeal cancer patient evaluation., Methods: A systematic search was performed in PUBMED, WEB OF SCIENCE and SCOPUS using "MRI, magnetic resonance imaging, radiomic, texture analysis, nasopharyngeal carcinoma, nasopharyngeal cancer" in all possible combinations. The methodological quality of study included ( = 24) was evaluated according to the RQS (Radiomic quality score). Subgroup, for journal type (imaging/clinical) and biomarker (prognostic/predictive), and correlation, between RQS and journal Impact Factor, analyses were performed. Mann-Whitney U test and Spearman's correlation were performed. P value < .05 were defined as statistically significant., Results: Overall, no studies reported a phantom study or a test re-test for assessing stability in image, biological correlation or open science data. Only 8% of them included external validation. Almost half of articles (45 %) performed multivariable analysis with non-radiomics features. Only 1 study was prospective (4%). The mean RQS was 7.5 ± 5.4. No significant differences were detected between articles published in clinical/imaging journal and between studies with a predictive or prognostic biomarker. No significant correlation was found between total RQS and Impact Factor of the year of publication (p always > 0.05)., Conclusions: Radiomic articles in nasopharyngeal cancer are mostly of low methodological quality. The greatest limitations are the lack of external validation, biological correlates, prospective design and open science., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objectives: To determine the optimal kiloelectron volt of noise-optimized virtual monoenergetic images [VMI (+)] for visualization of nasopharyngeal carcinoma (NPC) and nasopharyngeal lymphoma (NPL), and to explore the clinical value of quantitative parameters derived from dual-energy computed tomography (DECT) for distinguishing the two entities., Materials and Methods: Eighty patients including 51 with NPC and 29 with NPL were enrolled. The VMIs (+) at 40-80 keV with an interval of 10 keV were reconstructed by contrast enhanced images. The overall image quality and demarcation of lesion margins, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed in VMIs (+) and polyenergetic images (PEI). Normalized iodine concentration (NIC), slope of the spectral Hounsfield unit curve (λ HU ) and effective atomic number (Z eff ) were calculated. Diagnostic performance was assessed by receiver operating characteristic (ROC) curve., Results: The 40 keV VMI (+) yielded highest overall image quality scores, demarcation of lesion margins scores, SNR and CNR. The values of NIC, λ HU and Z eff in NPL were higher than those in NPC (P <  0.001). Multivariate logistic regression model combining NIC, λ HU and Z eff showed the best performance for distinguishing NPC from NPL (AUC: 0.947, sensitivity: 93.1 % and specificity: 92.2 %)., Conclusion: VMI (+) reconstruction at 40 keV was optimal for visualizing NPC and NPL. Quantitative parameters derived from DECT were helpful for differentiating NPC from NPL., (Copyright © 2021. Published by Elsevier B.V.)

Purpose: To evaluate whether pre-treatment intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can predict treatment outcome after 2 years in patients with nasopharyngeal carcinoma (NPC)., Method: One hundred and sixty-one patients with newly diagnosed NPC underwent pre-treatment IVIM-DWI. Univariate Cox regression analysis was performed to evaluate the correlation of the mean values of the pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction and apparent diffusion coefficient with local relapse-free survival (LRFS), regional relapse-free survival (RRFS), distant metastases-free survival (DMFS) and disease-free survival (DFS). Significant diffusion parameters, together with staging, age, gender and treatment as confounding factors, were added into a multivariate model. The area under the curves (AUCs) of significant parameters for disease relapse were compared using the Delong test., Results: Disease relapse occurred in 30 % of the patients at a median follow-up time of 52.1 months. The multivariate analysis showed that high D and T-staging were correlated with poor LRFS (p = 0.042 and 0.020, respectively) and poor DFS (p = 0.023 and 0.001, respectively); low D* and high T-staging with poor RRFS (p = 0.020 and 0.033, respectively); and high N-staging with poor DMFS (p = 0.006). D with the optimal threshold of ≥0.68 × 10 -3  mm 2 /s and T-staging showed similar AUCs (AUC = 0.614 and 0.651, respectively; p = 0.493) for predicting disease relapse., Conclusion: High D and low D* were predictors of poor locoregional outcome but none of the diffusion parameters predicted DMFS in NPC., Competing Interests: Declaration of Competing Interest The authors of this manuscript declare no financial or other conflicts of interest with the publication of this work., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: To explore the value of dual-energy CT for the differentiation between stage T1 nasopharyngeal carcinoma (NPC T1 ) and lymphoid hyperplasia (LH)., Method: Patients with histopathological proven nasopharyngeal lesions (stage T1 NPCs, n = 30; LHs, n = 47) who underwent dual-energy CT were enrolled in this retrospective study. Quantitative parameters derived from dual-energy CT were measured. Statistical analyses were performed using the independent sample t-test, Wilcoxon rank sum test, and receiver operating characteristic curve (ROC) analysis., Results: There was significantly higher iodine concentration (IC), normalized iodine concentration (NIC, to internal jugular vein) in NPC T1 compared with LH (p < 0.001). The effective atomic number (Z eff ) was significantly higher in NPC T1 than that in LH (p <  0.001). The virtual monochromatic images (VMIs) at 50 keV-110 keV (20 keV-interval) of NPC T1 were all significantly higher than those of LH (all p <0.001). The slope (k) value of spectral attenuation curve was also significantly higher in NPC T1 than LH (p <  0.001). There was no significant difference in virtual noncontrast (VNC) and 130 keV-190 keV (20 keV-interval) between the NPC T1 and LH. For discriminating NPC T1 from LH, the area under curve (AUC) using 70 keV was the highest in all single parameter (AUC, 0.92; sensitivity, 80.00 %; specificity, 91.49 %). Combined multiple parameters (IC, NIC, Z eff , 50 keV, 70 keV, 90 keV, slope (k)) by performing multivariate logistic regression model significantly improve the diagnostic capability in differentiating these two entities, with AUC, sensitivity, and specificity values of 0.99, 93.33 %, 97.87 %, respectively., Conclusions: Dual-energy CT can be helpful for the differentiation between NPC T1 and LH lesions., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020. Published by Elsevier B.V.)

Objectives: To investigate the use of texture analysis to quantitatively distinguish nasopharyngeal carcinoma (NPC) from normal adenoid on CT., Methods: In this IRB-approved, retrospective study, nasopharyngeal tissues in 13 patients with NPC and 13 control patients were manually contoured, segmented, and imported to an in-house developed texture analysis program, which extracted 41 texture features. Basic descriptive statistics were performed to evaluate for differences in texture parameters between NPC and controls., Results: Statistically significant differences between NPC and controls were seen in 32 of 41 texture features. These significant differences were present in 11 of 12 histogram features, 4 of 5 gray-level co-occurrence matrix features, 7 of 11 gray-level run length features, 4 of 4 gray-level gradient matrix features, and 6 of 9 Laws features., Conclusion: Significant differences in many texture features were seen between NPC and normal adenoids. CT texture analysis may aid in differentiating NPC from normal adenoid tissue., (Copyright © 2018 Elsevier B.V. All rights reserved.)