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Background: The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC., Methods: A total of 381 NPC patients who were divided into high- and low-risk groups according to progression-free survival were retrospectively included. Deeplab v3 and U-Net were trained to build segmentation models for the automatic segmentation of the tumor and suspicious lymph nodes. Five datasets were constructed by expanding 5, 10, 20, 40, and 60 pixels outward from the edge of the automatically segmented region. Inception-Resnet-V2, ECA-ResNet50t, EfficientNet-B3, and EfficientNet-B0 were trained with the original, segmented, and the five new constructed datasets to establish the classification models. The receiver operating characteristic curve was used to evaluate the performance of each model., Results: The Dice coefficients of Deeplab v3 and U-Net were 0.741(95%CI:0.722-0.760) and 0.737(95%CI:0.720-0.754), respectively. The average areas under the curve (aAUCs) of deep learning models for classification trained with the original and segmented images and with images expanded by 5, 10, 20, 40, and 60 pixels were 0.717 ± 0.043, 0.739 ± 0.016, 0.760 ± 0.010, 0.768 ± 0.018, 0.802 ± 0.013, 0.782 ± 0.039, and 0.753 ± 0.014, respectively. The models trained with the images expanded by 20 pixels obtained the best performance., Conclusions: The peritumoral region NPC contains information related to prognosis, and the incorporation of this region could improve the performance of deep learning models for prognosis prediction., (© 2023. The Author(s).)

Abstract: Nasopharyngeal carcinoma (NC) is an epithelial tumor with distinctive prevalence in East and Southeast Asia. Most patients with NC only experience regional metastasis. In cases of distant metastasis, prognosis is grim. Leptomeningeal spread of the disease is a rare occurrence, which is sparsely described in the literature. We present a case of a 33-year-old man with a history of high-grade NC with leptomeningeal metastasis. Initial MRI lumbar spine demonstrated subtle findings consistent with leptomeningeal disease. These findings are easily discernible on 18 F-FDG PET/CT., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

Object: The pterygopalatine fossa (PPF) is a covert neurovascular pathway in the skull base and connects with numerous intracranial and extracranial spaces. The aim of this study was to explore the magnetic resonance imaging (MRI) features of PPF invasion in patients with nasopharyngeal carcinoma (NPC)., Material and Methods: The medical records of 88 patients with stage T3 or T4 NPC were retrospectively analyzed. The 3-Dimensional (3D) volumetric images of MRI were reconstructed for the tiny connecting conduits of the invaded PPFs in the NPC patients. The infiltration incidence of conduits and connected further structures were calculated., Results: Forty-six PPFs from 37 patients were invaded by NPC. The proportions of stage T4 NPC and intracranial extension were higher in patients with PPF invasion than that without PPF invasion (P < 0.05). Each connecting conduit of the PPF had corresponding optimal reconstructed orientation based on 3D volumetric MRI images. The first three most common infiltrated conduits were palatovaginal canal, vidian canal and sphenopalatine foramen, which were adjacent to the nasopharynx. Among the conduits connecting with further structures, the most common infiltrated conduit was pterygomaxillary fissure, followed by foramen rotundum and inferior orbital fissure. Furthermore, The NPC lesions involved stage T4 structures via the conduits from 19.6% of the invaded PPFs., Conclusions: The application of high-quality reconstruction images based on 3D sequence of MRI in NPC patients proved to be feasible and beneficial for the manifestation of the invaded PPFs and connecting conduits., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To compare PET/CT, MRI and ultrasonography in detecting recurrence of nasopharyngeal carcinoma and identify their benefit in staging, contouring and overall survival (OS)., Methods: Cohort A included 1453 patients with or without histopathology-confirmed local recurrence, while cohort B consisted of 316 patients with 606 histopathology-confirmed lymph nodes to compare the sensitivities and specificities of PET/CT, MRI and ultrasonography using McNemar test. Cohorts C and D consisted of 273 patients from cohort A and 267 patients from cohort B, respectively, to compare the distribution of PET/CT-based and MRI-based rT-stage and rN-stage and the accuracy of rN-stage using McNemar test. Cohort E included 30 random patients from cohort A to evaluate the changes in contouring with or without PET/CT by related-samples T test or Wilcoxon rank test. The OS of 61 rT3-4N0M0 patients staged by PET/CT plus MRI (cohort F) and 67 MRI-staged rT3-4N0M0 patients (cohort G) who underwent similar salvage treatment were compared by log-rank test and Cox regression., Results: PET/CT had similar specificity to MRI but higher sensitivity (93.9% vs. 79.3%, P < 0.001) in detecting local recurrence. PET/CT, MRI and ultrasonography had comparable specificities, but PET/CT had greater sensitivity than MRI (90.9% vs. 67.6%, P < 0.001) and similar sensitivity to ultrasonography in diagnosing lymph nodes. According to PET/CT, more patients were staged rT3-4 (82.8% vs. 68.1%, P < 0.001) or rN + (89.9% vs. 69.3%, P < 0.001), and the rN-stage was more accurate (90.6% vs. 73.8%, P < 0.001). Accordingly, the contours of local recurrence were more precise (median Dice similarity coefficient 0.41 vs. 0.62, P < 0.001) when aided by PET/CT plus MRI. Patients staged by PET/CT plus MRI had a higher 3-year OS than patients staged by MRI alone (85.5% vs. 60.4%, P = 0.006; adjusted HR = 0.34, P = 0.005)., Conclusion: PET/CT more accurately detected and staged recurrence of nasopharyngeal carcinoma and accordingly complemented MRI, providing benefit in contouring and OS., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To investigate the prognostic performance of multi-level computed tomography (CT)-dose fusion dosiomics at the image-, matrix-, and feature-levels from the gross tumor volume (GTV) at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients., Methods: Two hundred and nineteen NPC patients (175 vs. 44 for training vs. internal validation) were used to train prediction model, and 32 NPC patients were used for external validation. We first extracted CT and dose information from intratumoral nasopharynx (GTV&#95;nx) and lymph node (GTV&#95;nd) regions. Then, the corresponding peritumoral regions (RING&#95;3 mm and RING&#95;5 mm) were also considered. Thus, the individual and combination of intratumoral and peritumoral regions were as follows: GTV&#95;nx, GTV&#95;nd, RING&#95;3 mm&#95;nx, RING&#95;3 mm&#95;nd, RING&#95;5 mm&#95;nx, RING&#95;5 mm&#95;nd, GTV&#95;nxnd, RING&#95;3 mm&#95;nxnd, RING&#95;5 mm&#95;nxnd, GTV + RING&#95;3 mm&#95;nxnd, and GTV + RING&#95;5 mm&#95;nxnd. For each region, 11 models were built by combining five clinical parameters and 127 features from: (1) dose images alone; (2-7) fused dose and CT images via wavelet-based fusion using CT weights of 0.2, 0.4, 0.6, and 0.8, gradient transfer fusion, and guided-filtering-based fusion (GFF); (8) fused matrices (sumMat); (9-10) fused features derived via feature averaging (avgFea) and feature concatenation (conFea); and finally, (11) CT images alone. The concordance index (C-index) and Kaplan-Meier curves with log-rank test were used to assess model performance., Results: The fusion models' performance was better than single CT/dose model on both internal and external validation. Models that combined the information from both GTV&#95;nx and GTV&#95;nd regions outperformed the single region model. For internal validation, GTV + RING&#95;3 mm&#95;nxnd GFF model achieved the highest C-index both in recurrence-free survival (RFS) and metastasis-free survival (MFS) predictions (RFS: 0.822; MFS: 0.786). The highest C-index in external validation set was achieved by RING&#95;3 mm&#95;nxnd model (RFS: 0.762; MFS: 0.719). The GTV + RING&#95;3 mm&#95;nxnd GFF model is able to significantly separate patients into high-risk and low-risk groups compared to dose-only or CT-only models., Conclusion: Fusion dosiomics model combining the primary tumor, the involved lymph node, and 3 mm peritumoral information outperformed single-modality models for different outcome predictions, which is helpful for clinical decision-making and the development of personalized treatment., (© 2022 American Association of Physicists in Medicine.)

Background: Magnetic resonance imaging (MRI) is commonly used for the diagnosis of nasopharyngeal carcinoma (NPC) and occipital clivus (OC) invasion, but a proportion of lesions may be missed using non-enhanced MRI. The purpose of this study is to investigate the diagnostic performance of synthetic magnetic resonance imaging (SyMRI) in differentiating NPC from nasopharyngeal hyperplasia (NPH), as well as evaluating OC invasion., Methods: Fifty-nine patients with NPC and 48 volunteers who underwent SyMRI examination were prospectively enrolled. Eighteen first-order features were extracted from VOIs (primary tumours, benign mucosa, and OC). Statistical comparisons were conducted between groups using the independent-samples t-test and the Mann-Whitney U test to select significant parameters. Multiple diagnostic models were then constructed using multivariate logistic analysis. The diagnostic performance of the models was calculated by receiver operating characteristics (ROC) curve analysis and compared using the DeLong test. Bootstrap and 5-folds cross-validation were applied to avoid overfitting., Results: The T1, T2 and PD map-derived models had excellent diagnostic performance in the discrimination between NPC and NPH in volunteers, with area under the curves (AUCs) of 0.975, 0.972 and 0.986, respectively. Besides, SyMRI models also showed excellent performance in distinguishing OC invasion from non-invasion (AUC: 0.913-0.997). Notably, the T1 map-derived model showed the highest diagnostic performance with an AUC, sensitivity, specificity, and accuracy of 0.997, 96.9%, 97.9% and 97.5%, respectively. By using 5-folds cross-validation, the bias-corrected AUCs were 0.965-0.984 in discriminating NPC from NPH and 0.889-0.975 in discriminating OC invasion from OC non-invasion., Conclusions: SyMRI combined with first-order parameters showed excellent performance in differentiating NPC from NPH, as well as discriminating OC invasion from non-invasion., (© 2023. The Author(s).)

Background: There is no standardized registration frame of cone beam CT (CBCT) in intensity modulated radiotherapy (IMRT) for nasopharyngeal carcinoma (NPC). The overall registration frame that covers the whole head and neck is the most commonly used CBCT registration frame for NPC patients in IMRT., Objective: To compare the set-up errors using different registration frames of CBCT for NPC to assess the set-up errors for different region of the commonly used clinical overall registration frame., Methods: 294 CBCT images of 59 NPC patients were collected. Four registration frames were used for matching. The set-up errors were obtained using an automatic matching algorithm and then compared. The expansion margin from the clinical target volume (CTV) to the planned target volume (PTV) in the four groups was also calculated., Results: The average range of the isocenter translation and rotation errors of four registration frames are 0.89∼2.41 mm and 0.49∼1.53°, respectively, which results in a significant difference in the set-up errors (p < 0.05). The set-up errors obtained from the overall frame are smaller than those obtained from the head, upper neck, and lower neck frames. The margin ranges of the overall, head, upper neck, and lower neck frames in three translation directions are 1.49∼2.39 mm, 1.92∼2.45 mm, 1.86∼3.54 mm and 3.02∼4.78 mm, respectively. The expansion margins calculated from the overall frame are not enough, especially for the lower neck., Conclusion: Set-up errors of the neck are underestimated by the overall registration frame. Thus, it is important to improve the position immobilization of the neck, especially the lower neck. The margin of the target volume of the head and neck region should be expanded separately if circumstances permit.

Importance: Accurate pre-treatment prediction of distant metastasis in patients with Nasopharyngeal Carcinoma (NPC) enables the implementation of appropriate treatment strategies for high-risk individuals., Purpose: To develop and assess a Convolutional Neural Network (CNN) model using pre-therapy Magnetic Resonance (MR) imaging to predict distant metastasis in NPC patients., Methods: We retrospectively reviewed data of 441 pathologically diagnosed NPC patients who underwent complete radiotherapy and chemotherapy at Renmin Hospital of Wuhan University (Hubei, China) between February 2012 and March 2018. Using Adobe Photoshop, an experienced radiologist segmented MR images with rectangular regions of interest. To develop an accurate model according to the primary tumour, Cervical Metastatic Lymph Node (CMLN), the largest area of invasion of the primary tumour, and image segmentation methods, we constructed intratumoural and intra-peritumoural datasets that were used for training and test of the transfer learning models. Each model's precision was assessed according to its receiver operating characteristic curve and accuracy. Generated high-risk-related Grad-Cams demonstrated how the model captured the image features and further verified its reliability., Results: Among the four models, all intra-peritumoural datasets performed better than the corresponding intratumoural datasets, with the CMLN intra-peritumoural dataset exhibiting the best performance (average area under the curves (AUCs) = 0.88). There was no significant difference between average AUCs of the Max and NPC tumour datasets. AUCs of the eight datasets for the four models were higher than those of the Tumour-Node-Metastasis staging system (AUC=0.67). In most datasets, the xception model had higher AUCs than other models. The efficientnet-b0 and xception models efficiently extracted high-risk features., Conclusion: The CNN model predicted distant metastasis in NPC patients with high accuracy. Compared to the primary tumour, the CMLN better predicted distant metastasis. In addition to intratumoural data, peritumoural information can facilitate the prediction of distant metastasis. With a larger sample size, datasets of the largest areas of tumour invasion may achieve meaningful accuracy. Among the models, xception had the best overall performance., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background/purpose: Accurate staging is the first step for optimal treatment selection in patients with nasopharyngeal carcinoma (NPC). In this propensity-score-matched, population-based cohort study, we investigated the survival effects of pretreatment 8-fluorodeoxyglucose positron emission tomography-computed tomography ( 18 FDG-PET-CT) on patients with NPC., Methods: We included patients with stage I-IVA NPC receiving radiotherapy or concurrent chemoradiotherapy and categorized them into two 1:1 propensity score-matched groups according to whether or not they underwent pretreatment 18 FDG-PET-CT and compared their outcomes., Results: Of the 10,756 patients, propensity score matching yielded 4366 patients in each group. According to multivariable Cox regression analyses, the most prominent correlation between pretreatment 18 FDG-PET-CT and all-cause death was observed in patients with stage II NPC (adjusted hazard ratio [aHR], 0.77; 95% confidence interval [CI], 0.60-0.90; P = .0433), followed by patients with stage III NPC (aHR, 0.81; 95% CI, 0.69-0.94; P = .0071) and patients with stage IVA NPC (aHR, 0.88; 95% CI, 0.79-0.97; P = .0091). This association was not significant in patients with stage I NPC (aHR, 1.20; 95% CI, 0.75-1.93; P = .4426)., Conclusion: Pretreatment 18 FDG-PET-CT is associated with longer survival in patients with clinical stage II-IVA NPC but not in stage I NPC., Competing Interests: Declaration of competing interest The authors have no potential conflicts of interest to declare. The data sets supporting the study conclusions are included in the manuscript., (Copyright © 2022 Formosan Medical Association. Published by Elsevier B.V. All rights reserved.)

Background and Purpose: The skull base osteomyelitis sometimes can be difficult to distinguish from nasopharyngeal cancer. This study aimed to investigate the differences between skull base osteomyelitis and nasopharyngeal cancer using dynamic contrast-enhanced MR imaging and normalized ADC values., Materials and Methods: This study included 8 and 12 patients with skull base osteomyelitis and nasopharyngeal cancer, respectively, who underwent dynamic contrast-enhanced MR imaging and DWI before primary treatment. Quantitative dynamic contrast-enhanced MR imaging parameters and ADC values of the ROIs were analyzed. Normalized ADC parameters were calculated by dividing the ROIs of the lesion by that of the spinal cord., Results: The rate transfer constant between extravascular extracellular space and blood plasma per minute (Kep) was significantly lower in patients with skull base osteomyelitis than in those with nasopharyngeal cancer (median, 0.43 versus 0.57; P = .04). The optimal cutoff value of Kep was 0.48 (area under the curve, 0.78; 95% CI, 0.55-1). The normalized mean ADC was significantly higher in patients with skull base osteomyelitis than in those with nasopharyngeal cancer (median, 1.90 versus 0.87; P  < .001). The cutoff value of normalized mean ADC was 1.55 (area under the curve, 0.96; 95% CI, 0.87-1). The area under the curve of the combination of dynamic contrast-enhanced MR imaging parameters (Kep and extravascular extracellular space volume per unit tissue volume) was 0.89 (95% CI, 0.73-1), and the area under the curve of the combination of dynamic contrast-enhanced MR imaging parameters and normalized mean ADC value was 0.98 (95% CI, 0.93-1)., Conclusions: Quantitative dynamic contrast-enhanced MR imaging parameters and normalized ADC values may be useful in differentiating skull base osteomyelitis and nasopharyngeal cancer. The combination of dynamic contrast-enhanced MR imaging parameters and normalized ADC values outperformed each measure in isolation., (© 2023 by American Journal of Neuroradiology.)

Background: Reliable predictors are urgently needed to identify stage II nasopharyngeal carcinoma (NPC) patients who could benefit from concurrent chemoradiotherapy (CCRT). We aimed to develop a nomogram integrating MRI-identified multidimensional features of lymph nodes to predict survival and assist the decision-making of CCRT for stage II NPC., Patients and Methods: This retrospective study enrolled 242 stage II NPC patients treated from January 2007 to December 2017. Overall survival (OS) was the primary endpoint. Performance of nomogram was evaluated using calibration curves, Harrell Concordance Index (C-index), area under the curve (AUC) and decision curves analysis (DCA) and was compared with TNM staging. According to the individualized nomogram score, patients were classified into two risk cohorts and therapeutic efficacy of CCRT were evaluated in each cohort., Results: Three independent prognostic factors for OS: age, number and location of positive lymph nodes were included into the final nomogram. T stage was also incorporated due to its importance in clinical decision-making. Calibration plots demonstrated a good match between the predicted and our observed OS rates. C-index for nomogram was 0.726 compared with 0.537 for TNM staging ( p < 0.001). DCAs confirmed the superior clinical utility of nomograms compared with TNM staging. CCRT compared to intensity-modulated radiotherapy (IMRT) delivered OS benefit to patients in the high-risk group (5-year: 89.9% vs . 72.1%; 10-year: 72.5% vs . 34.2%, p = 0.011), but not in the low-risk group., Conclusions: This lymph node features-based nomogram demonstrated excellent discrimination and predictive accuracy for stage II patients and could identify patients who can benefit from CCRT., (© 2022 Yang Liu, Jianghu Zhang, Jingbo Wang, Runye Wu, Xiaodong Huang, Kai Wang, Yuan Qu, Xuesong Chen, Yexiong Li, Ye Zhang, Junlin Yi, published by Sciendo.)

Purpose: This study aimed to explore the value of [ 68 Ga]Ga-labelled fibroblast activation protein inhibitor ([ 68 Ga]Ga-FAPI) positron emission tomography/computed tomography (PET/CT) in the initial staging of patients with newly diagnosed nasopharyngeal carcinoma (NPC), compared with 2-deoxy-2[ 18 F]fluoro-D-glucose ([ 18 F]F-FDG) PET/CT., Materials and Methods: Forty-seven treatment-naïve patients with newly diagnosed NPC underwent magnetic resonance imaging (MRI), [ 68 Ga]Ga-DOTA-FAPI-04 PET/CT and [ 18 F]F-FDG PET/CT within 1 week. The diagnostic efficiency of all imaging modalities for evaluating primary tumour extension was compared from the two aspects of soft tissue and bony structure involvement. The accuracy of two PET/CT methods for diagnosing cervical lymph node (CLN) metastases was compared, and MRI served as the standard reference. T and N stages were assessed by MRI, [ 68 Ga]Ga-FAPI PET/CT and [ 18 F]F-FDG PET/CT. Immunohistochemical (IHC) staining for FAP was conducted in 22 of the patients., Results: [ 68 Ga]Ga-FAPI PET/CT outperformed [ 18 F]F-FDG PET/CT in the assessment of primary tumour invasion in the cavernous sinus (10 vs. 1, p < 0.001) and bony structures (207 vs. 177, p < 0.001). Compared with MRI, [ 68 Ga]Ga-FAPI PET/CT upgraded and underestimated T stage in 13 and 2 patients, while [ 18 F]F-FDG PET/CT upgraded and underestimated T stage in 5 and 13 patients. However, [ 68 Ga]Ga-FAPI PET/CT was inferior to [ 18 F]F-FDG PET/CT in diagnosing positive CLNs based on the analyses of patients, neck sides, neck levels and individual nodes. [ 68 Ga]Ga-FAPI PET/CT changed therapeutic schedules in 8 patients because of stage group changes. The presence of FAP with high quantity and intensity in cancer-associated fibroblasts (CAFs) was confirmed in all tumour specimens., Conclusion: [ 68 Ga]Ga-FAPI PET/CT outperformed [ 18 F]F-FDG PET/CT in detecting the cavernous sinus and bony structure involvement of primary NPC tumours, suggesting its value in improving T staging and therapeutic regimen selection. However, the performance of [ 68 Ga]Ga-FAPI PET/CT is less promising for N staging because it detected fewer positive CLNs than [ 18 F]F-FDG PET/CT., (© 2022. The Author(s), under exclusive licence to World Molecular Imaging Society.)

Purpose: To develop an advanced deep convolutional neural network (DCNN) architecture to generate synthetic CT (SCT) images from MR images for intensity-modulated proton therapy (IMPT) treatment planning of nasopharyngeal cancer (NPC) patients., Methods: T1-weighted MR images and paired CT (PCT) images were obtained from 206 NPC patients. For each patient, deformable image registration was performed between MR and PCT images to create an MR-CT image pair. Thirty pairs were randomly chosen as the independent test set and the remaining 176 pairs (14 for validation and 162 for training) were used to build two conditional generative adversarial networks (GANs): 1) GAN 3D : using a 3D U-net enhanced with residual connections and attentional mechanism as the generator and 2) GAN 2D : using a 2D U-net as the generator. For each test patient, SCT images were generated using the generators with the MR images as input and were compared with respect to the corresponding PCT image. A clinical IMPT plan was created and optimized on the PCT image. The dose was recalculated on the SCT images and compared with the one calculated on the PCT image., Results: The mean absolute errors (MAEs) between the PCT and SCT, within the body, were (64.89 ± 5.31) HU and (64.31 ± 4.61) HU for the GAN 2D and GAN 3D . Within the high-density bone (HU > 600), the GAN 3D achieved a smaller MAE compared with the GAN 2D ( p  < 0.001). Within the body, the absolute point dose deviation was reduced from (0.58 ± 1.61) Gy for the GAN 2D to (0.47 ± 0.94) Gy for the GAN 3D . The (3 mm/3%) gamma passing rates were above 97.32% for all SCT images., Conclusions: The SCT images generated using GANs achieved clinical acceptable dosimetric accuracy for IMPT of NPC patients. Using advanced DCNN architecture design, such as residual connections and attention mechanism, SCT image quality was further improved and resulted in a small dosimetric improvement.

Purpose: Accurate identification of nodal status enables adequate neck irradiation for nasopharyngeal carcinoma (NPC). However, most conventional techniques are unable to pick up occult metastases, leading to underestimation of tumor extensions. Here we investigate the clinical significance of carbonic anhydrase IX (CAIX) in human NPC samples, and develop a CAIX-targeted imaging strategy to identify occult lymph node metastases (LNMs) and extranodal extension (ENE) in animal studies., Methods: A total of 211 NPC samples are performed CAIX staining, and clinical outcomes are analyzed. The metastatic murine models are generated by foot pad injection of NPC cells, and a CAIX-targeted imaging agent (CAIX-800) is intravenously administered. We adopt fluorescence molecular tomography and ultrasonography (US)-guided spectroscopic photoacoustic (sPA) imaging to perform in vivo studies. Histological and immunohistochemical characterization are carried out via node-by-node analysis., Results: For clinical samples, 90.1% (91/101) primary tumors, 73.3% (66/90) metastases, and 100% (20/20) local recurrences are CAIX positive. In metastases group, 84.7% (61/72) nodal metastases and 22.2% (4/18) organ metastases are CAIX positive. CAIX expression in primary tumors is significantly associated with NPC stage and prognosis. For animal studies, CAIX-800-based fluorescence imaging achieves 81.3% sensitivity and 93.8% specificity in detecting occult LNMs in vivo, with a minimum detectable diameter of 1.7 mm. Coupled with CAIX-800, US-guided sPA imaging could not only detect subcapsular deposits of metastatic cancer cells 2 weeks earlier than conventional techniques, but also successfully track pathological ENE., Conclusion: CAIX remarkably expresses in human NPCs and stratifies patient prognosis. In preclinical studies, CAIX-800-based imaging successfully identifies occult LNMs and tracks early stage of pathological ENE. This attractive method shows potential in clinic, allowing medical workers to longitudinally monitor nodal status and helping to reduce unnecessary nodal biopsy for patients with NPC. The schematic diagram for the study. CAIX, carbonic anhydrase IX; NPC, nasopharyngeal carcinoma; US, ultrasonography; sPA, spectroscopic photoacoustic., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: To investigate the potential value of the pretreatment MRI-based radiomic model in predicting the overall survival (OS) of nasopharyngeal carcinoma (NPC) patients with local residual tumors after intensity-modulated radiotherapy (IMRT)., Methods: A total of 218 consecutive nonmetastatic NPC patients with local residual tumors after IMRT [training cohort (n = 173) and validation cohort (n = 45)] were retrospectively included in this study. Clinical and MRI data were obtained. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features from pretreatment MRI. The clinical, radiomic, and combined models for predicting OS were constructed. The models' performances were evaluated using Harrell's concordance index (C-index), calibration curve, and decision curve analysis., Results: The C-index of the radiomic model was higher than that of the clinical model, with the C-index of 0.788 (95% CI 0.724-0.852) versus 0.672 (95% CI 0.599-0.745) in the training cohort and 0.753 (95% CI 0.604-0.902) versus 0.634 (95% CI 0.593-0.675) in the validation cohort. Calibration curves showed good agreement between the radiomic model-predicted probability of 2- and 3-year OS and the actual observed probability in the training and validation groups. Decision curve analysis showed that the radiomic model had higher clinical usefulness than the clinical model. The discrimination of the combined model improved significantly in the training cohort (P < 0.01) but not in the validation cohort, with the C-index of 0.834 and 0.734, respectively. The radiomic model divided patients into high- and low-risk groups with a significant difference in OS in both the training and validation cohorts., Conclusions: Pretreatment MRI-based radiomic model may improve OS prediction in NPC patients with local residual tumors after IMRT and may assist in clinical decision-making., (© 2022. The Author(s).)

Objective: The present study aimed to assess the prognostic interest of metabolic and anatomic parameters derived from 2-deoxy-2-[ 18 F]fluoro-D-glucose positron emission tomography/computed tomography ([ 18 F]FDG PET/CT) and head and neck magnetic resonance imaging (HN-MRI) for better management of nasopharyngeal carcinoma (NPC)., Methods: In this study, pre-treatment [ 18 F]FDG PET/CT and HN-MRI parameters of NPC patients diagnosed between January 2017 and December 2018, were prospectively investigated. Correlation between those parameters and 4-year patient's survival outcomes was evaluated using Kaplan-Meier and Cox-regression analyses., Results: Our results revealed a significant association between pre-treatment nodal-maximum standardized uptake value (N-SUV max) and N categories (p = 0.01), between pre-treatment node-to-tumor SUV ratio (NTR) and both tumor size (p = 0.01) and N categories (p = 0.009), as well as between metabolic tumor volume (MTV) and both tumor size and NPC overall stage (p < 0.000). In multivariate analyses, pre-treatment N-SUV max, NTR and MTV were significant independent predictors of overall survival, distant metastasis-free survival, and progression-free survival (PFS) (p < 0.05). N-SUV max and MTV were also found to be significant independent predictors of loco-regional recurrence-free survival (p < 0.05), whereas HN-MRI detection of skull-base bone invasion was an independent factor associated with worse PFS in NPC (p = 0.03)., Conclusions: The present study highlights N-SUV max, NTR and MTV derived from [ 18 F]FDG PET/CT, and skull-base bone invasion defined by HN-MRI, as promising metabolic and anatomic prognosis biomarkers for NPC., (© 2022. The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.)

This work aimed to explore the application value of computed tomography (CT)-based radiomics in predicting changes in tumor regression during radiotherapy for nasopharyngeal carcinoma. In this work, 144 patients with nasopharyngeal carcinoma who underwent concurrent chemoradiotherapy (CCRT) in our hospital from January 2015 to December 2021 were selected. The patients were divided into a radiosensitive group (79 cases) and an insensitive group (65 cases) according to the tumor volume shrinkage during radiotherapy. The 3D Slicer 4.10.2 software was used to delineate the tumor region of interest (ROI), and a total of 1223 radiomics features were extracted using the radiomics module under the software. After between-group and within-group consistency tests, one-way ANOVA, and LASSO dimensionality reduction, three omics features were finally selected for the establishment of predictive models. At the same time, the age, gender, tumor T stage and N stage, hemoglobin, and albumin of the patients were collected to establish a clinical prediction model. The results showed that compared with logistic regression, decision tree, random forest, and AdaBoost models, the SVM model based on CT radiomics features had the best performance in predicting tumor regression changes during tumor radiotherapy (training group area under the receiver operating characteristic curve (AUC): 0.840 (95% confidence interval (CI): 0.764-0.916); validation group: AUC: 0.810 (95% CI: 0.676-0.944)). Compared with the supported vector machine (SVM) prediction model based on clinical features, the SVM model based on radiomics features had better performance in predicting the change of retraction during tumor radiotherapy (training group: omics feature SVM model AUC: 0.84, clinical feature SVM model: 0.78; validation group: omics feature SVM model AUC: 0.8, clinical feature SVM model: 0.58, P  = 0.044). Based on the radiomics characteristics and clinical characteristics of patients, a nomo prediction map was established, and the calibration curve shows good consistency, which can be visualized to assist clinical judgment. In this work, the prediction model composed of CT-based radiomic features combined with clinical features can accurately predict withdrawal changes during tumor radiotherapy, ensuring the accuracy of treatment planning, and minimizing the number of CT scans during radiotherapy., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yu Yang et al.)

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e.g., local lymph node metastasis and adjacent tissue invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation in advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background information. In addition, we also introduce a cascaded survival network to capture the prognostic information existing out of primary tumors and further leverage the global tumor information (e.g., tumor size, shape, and locations) derived from the segmentation backbone. Our experiments with two clinical datasets demonstrate that our DeepMTS can consistently outperform traditional radiomics-based survival prediction models and existing deep survival models.

Purpose: This study systematically compared the images from readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI [RS-DWI]) and simultaneous multislice accelerated RESOLVE-DWI (SMS-RS-DWI) in patients with nasopharyngeal carcinoma (NPC) in qualitative and quantitative aspects., Method: Forty-four patients with NPC were included. The RS-DWI and prototypic SMS-RS-DWI sequences were performed on all patients. Images were qualitatively evaluated by 4 independent radiologists using a 5-point Likert scale. For quantitative evaluation, the maximum and minimum diameters and the maximum tumor areas were determined for both DWI sequences and compared with the T2-weighted imaging (T2WI) to evaluate image distortions. The apparent diffusion coefficient was measured in the slice with the maximum tumor profile., Results: The SMS-RS-DWI was superior to RS-DWI with respect to overall image quality (3.40 ± 0.53 vs 2.71 ± 0.48, P < 0.0001) and tumor edge sharpness (3.29 ± 0.65 vs 2.64 ± 0.47, P < 0.0001). Susceptibility artifacts were significantly less severe in SMS-RS-DWI than in RS-DWI (0.85 ± 0.57 vs 1.36 ± 0.57, P < 0.0001). There was no significant overestimation or underestimation of the tumor geometry using the SMS-RS-DWI or RS-DWI compared with T2WI. The quantitative analysis showed a slightly higher agreement for SMS-RS-DWI with T2WI than RS-DWI for maximum diameter, minimum diameter, and maximum tumor area. The apparent diffusion coefficient values showed no significant differences between the 2 DWI techniques ( P > 0.05)., Conclusions: At 3 T, SMS-RS-DWI is a useful technique for diagnosing NPC. It substantially improves different aspects of image quality by providing higher spatial resolution and fewer susceptibility artifacts with more extensive anatomic coverage compared with RS-DWI., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The staging of nasopharyngeal carcinoma (NPC) is of great value in treatment and prognosis. We explored whether a positron emission tomography/ magnetic resonance imaging (PET/MRI) based comprehensive model of radiomics features and semiquantitative parameters was useful for clinical evaluation of NPC staging., Materials and Methods: A total of 100 NPC patients diagnosed with non-keratinized undifferentiated carcinoma were divided into early-stage group (I-II) and advanced-stage group (III-IV) and divided into the training set (n = 70) and the testing set (n = 30). Radiomics features (n = 396 × 2) of the primary site of NPC were extracted from MRI and PET images, respectively. Three major semiquantitative parameters of primary sites including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) in all NPC patients were measured. After feature selection, three diagnostic models including the radiomics model, the metabolic parameter model, and the combined model were established using logistic regression model. Finally, internal validation was performed, and a nomogram for NPC comprehensive diagnosis has been made., Results: The radiomics model and metabolic parameter model showed an area under the curve (AUC) of 0.83 and 0.80 in the testing set, respectively. The combined model based on radiomics and semiquantitative parameters showed an AUC of 0.90 in the testing set, with the best performance among the three models., Conclusion: The combined model based on PET/MRI radiomics and semiquantitative parameters is of great value in the evaluation of clinical stage (early-stage group and advanced-stage group) of NPC., (© 2022. The Author(s).)

Background: To evaluate the parameters derived from arterial spin labeling (ASL) and multi-b-value diffusion-weighted imaging (DWI) for differentiating retropharyngeal lymph nodes (RLNs) in patients with nasopharyngeal carcinoma (NPC)., Methods: This prospective study included 50 newly diagnosed NPC and 23 healthy control (HC) participants. RLNs of NPC were diagnosed according to the follow-up MRI after radiotherapy. Parameters derived from ASL and multi-b-value DWI, and RLNs axial size on pre-treatment MRI among groups were compared. Receiver operating characteristic curve (ROC) was used to analyze the diagnostic efficiency., Results: A total of 133 RLNs were collected and divided into a metastatic group (n = 71) and two non-metastatic groups (n = 62, including 29 nodes from NPC and 33 nodes from HC). The axial size, blood flow (BF), and apparent diffusion coefficient (ADC) of RLNs were significantly different between the metastasis and the non-metastasis group. For NPC patients with a short axis < 5 mm or < 6 mm, or long axis < 7 mm, if BF > 54 mL/min/100 g or ADC ≤ 0.95 × 10 -3  mm 2 /s, the RLNs were still considered metastatic. Compared with the index alone, a combination of size and functional parameters could improve the accuracy significantly, except the long axis combined with ADC; especially, combined size with BF exhibited better performance with an accuracy of 91.00-92.00%., Conclusions: ASL and multi-b-value DWI could help determine the N stage of NPC, while the BF combination with RLNs size may significantly improve the diagnostic efficiency., (© 2022. The Author(s).)

Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models.

Background: Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient., Purpose: To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model., Study Type: Retrospective., Population: A total of 286 LANPC patients were assigned to training (N = 200, 43.8 ± 10.9 years, 152 male) and testing (N = 86, 43.5 ± 11.3 years, 57 male) cohorts., Field Strength/sequence: T 2 -weighted imaging, contrast enhanced-T 1 -weighted imaging using fast spin echo sequences at 1.5 T scanner., Assessment: Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Model clinic ), radiomics features (Model radiomics ), and clinical factors + radiomics signatures using logistic (Model combined ), and BPNN (Model BPNN ) methods were established, and model performances were compared., Statistical Tests: Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance., Results: Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR = 2.883; 95% CI, 1.364-6.745), and T stage (OR = 1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. Model BPNN demonstrated the highest performance among Model combined , Model radiomics , and Model clinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695)., Conclusion: A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC., Evidence Level: 3 TECHNICAL EFFICACY: Stage 2., (© 2021 International Society for Magnetic Resonance in Medicine.)

Nasopharyngeal carcinoma is frequently an undifferentiated squamous cell carcinoma, which is primarily treated with radiotherapy with or without chemotherapy; however, for residual or recurrent cancer, salvage surgery is the treatment of choice for resectable disease because it reduces the significant morbidity associated with re-irradiation. This review highlights the current surgical approach, mapping the extent of disease with an emphasis on magnetic resonance imaging (MRI) and integrated positron-emission tomography (PET)/computed tomography (CT), imaging features that determine operability for nasopharyngectomy and neck dissection, and need for a succinct radiological report and multidisciplinary team discussion, which are of paramount importance for successful surgical planning., (Copyright © 2022 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Aim: To establish a spectral computed tomography (CT)-based nomogram for predicting the response to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC)., Materials and Methods: Fifty-four patients with NPC who underwent spectral CT examination before ICT were enrolled prospectively. Patients were assigned to response and non-response groups according to response evaluation. The predictive indicators were spectral CT parameters of venous phase, including iodine concentration (IC), normalised IC (NIC), slope of the spectral attenuation curve in Hounsfield units (λHU), effective atomic number (Eff-Z), and water concentration. Multivariate logistic regression was used to construct a predictive model. The receiver operating characteristic (ROC) and calibration curves were used to evaluate the robustness of model, while the bootstrap method was used for internal validation. The Hosmer-Lemeshow test was used to test the goodness of fit of the discriminant model., Results: Multivariate logistic regression analysis showed that NIC, λHU, and Eff-Z were the potential predictors, and the three indicators were further used to establish a predictive model. The nomogram was evaluated to have good predictive performance, the area under the ROC curve was 0.909 (95% confidence interval [CI]: 0.799-0.970), and the model was well calibrated (χ 2  = 8.149, p=0.419)., Conclusions: The spectral CT nomogram has potential clinical value in predicting response to ICT in NPC and may help guide individualised treatment decisions., (Copyright © 2022 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Objective: To shorten acquisition time of readout segmentation of long variable echo trains (RESOLVE)-based diffusion kurtosis imaging (DKI) via Readout Partial Fourier (RPF) and b-value combinations., Methods: The RESOLVE-based DKI images of 38 patients with nasopharyngeal carcinoma (NPC) were prospectively enrolled. For RESOLVE-based DKI images with 5/8 RPF and without RPF, objective and subjective evaluations of image quality were performed. A total of nine groups with different b-value combinations were simulated, and the influence of different b-value combinations for RESOLVE-RPF-based DKI sequences was assessed using the intraclass correlation coefficient (ICC)., Results: The mean values of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in DKI images without RPF were higher than those with 5/8 RPF (252.9 ± 77.7 vs 247.3 ± 85.5 and 5.8 ± 2.8 vs 5.4 ± 2.3, respectively), but not significantly ( p = 0.460 and p = 0.180, respectively). In comparing the ICCs between nine groups of different b-value combinations in RESOLVE-RPF-based DKI, group (200, 800, 2000 s/mm 2 ), group (200, 400, 800, 2000 s/mm 2 ) and group (200, 800, 1500, 2000 s/mm 2 ) were not significantly different ( p > 0.001) and showed excellent agreement (0.81-1.00) with that of group (200, 400, 800, 1500, 2000 s/mm 2 ). Using b-value optimization and RPF technology, the group with RPF (200, 400, 800, 2000 s/mm 2 ) showed a 56% reduced scanning compared with the group without RPF (200, 400, 800, 1500, 2000 s/mm 2 ; 3 min 46 s vs 8 min 31 s, respectively)., Conclusion: DKI with RPF did not significantly affect image quality, but both RPF and different b-value combinations can affect the scanning time. The combination of RPF and b-value optimization can ensure the stability of DKI parameters and reduce the scanning time by 56%., Advances in Knowledge: This work is to optimize scan parameters, e.g. RPF and b-value combinations, to reduce acquisition time for RESOLVE-based DKI in NPC. To our knowledge, the effect of RESOLVE-RPF and b-value combinations on DKI has not been reported.

Purpose: Endoscopic biopsy is recommended for diagnosis of nasopharyngeal carcinoma (NPC). A proportion of lesions are hidden from endoscopic view but detected with magnetic resonance imaging (MRI). This systematic review and meta-analysis investigated the diagnostic performance of MRI for detection of NPC., Methods: An electronic search of twelve databases and registries was performed. Studies were included if they compared the diagnostic accuracy of MRI to a reference standard (histopathology) in patients suspected of having NPC. The primary outcome was accuracy for detection of NPC. Random-effects models were used to pool outcomes for sensitivity, specificity, and positive and negative likelihood ratio (LR). Bias and applicability were assessed using the modified QUADAS-2 tool., Results: Nine studies were included involving 1736 patients of whom 337 were diagnosed with NPC. MRI demonstrated a pooled sensitivity of 98.1% (95% CI 95.2-99.3%), specificity of 91.7% (95% CI 88.3-94.2%), negative LR of 0.02 (95% CI 0.01-0.05), and positive LR of 11.9 (95% CI 8.35-16.81) for detection of NPC. Most studies were performed in regions where NPC is endemic, and there was a risk of selection bias due to inclusion of retrospective studies and one case-control study. There was limited reporting of study randomization strategy., Conclusion: This study demonstrates that MRI has a high pooled sensitivity, specificity, and negative predictive value for detection of NPC. MRI may be useful for lesion detection prior to endoscopic biopsy and aid the decision to avoid biopsy in patients with a low post-test probability of disease., (© 2022. The Author(s).)

Purpose: We aimed to validate the accuracy and clinical value of a novel semisupervised learning framework for gross tumor volume (GTV) delineation in nasopharyngeal carcinoma., Methods and Materials: Two hundred fifty-eight patients with magnetic resonance imaging data sets were divided into training (n = 180), validation (n = 20), and testing (n = 58) cohorts. Ground truth contours of nasopharynx GTV (GTVnx) and node GTV (GTVnd) were manually delineated by 2 experienced radiation oncologists. Twenty percent (n = 36) labeled and 80% (n = 144) unlabeled images were used to train the model, producing model-generated contours for patients from the testing cohort. Nine experienced experts were invited to revise model-generated GTV in 20 randomly selected patients from the testing cohort. Six junior oncologists were asked to delineate GTV in 12 randomly selected patients from the testing cohort without and with the assistance of the model, and revision degrees were compared under these 2 modes. The Dice similarity coefficient (DSC) was used to quantify the accuracy of the model., Results: The model-generated contours showed a high accuracy compared with ground truth contours, with an average DSC score of 0.83 and 0.80 for GTVnx and GTVnd, respectively. There was no significant difference in DSC score between T1-2 and T3-4 patients (0.81 vs 0.83; P = .223), or between N1-2 and N3 patients (0.80 vs 0.79; P = .807). The mean revision degree was lower than 10% in 19 (95%) patients for GTVnx and in 16 (80%) patients for GTVnd. With assistance of the model, the mean revision degree for GTVnx and GTVnd by junior oncologists was reduced from 25.63% to 7.75% and from 21.38% to 14.44%, respectively. Meanwhile, the delineating efficiency was improved by over 60%., Conclusions: The proposed semisupervised learning-based model showed a high accuracy for delineating GTV of nasopharyngeal carcinoma. It was clinically applicable and could assist junior oncologists to improve GTV contouring accuracy and save contouring time., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Magnetic resonance imaging (MRI) parameters that reflect the tumor microenvironment of nasopharyngeal carcinoma (NPC) may predict treatment response and facilitate treatment planning. This study aimed to evaluate the diffusion-weighted imaging and dynamic contrast-enhanced MRI (DCE-MRI) values for predicting the treatment outcomes in NPC patients., Methods: Eighty-three patients with NPC underwent pretreatment MRI simulation with diffusion-weighted imaging and dynamic contrast-enhanced MRI. Average values of the apparent diffusion coefficient (ADC), Ktrans, Kep, Ve, Vp, and tumor volume of the primary tumors were measured. Other potential clinical characteristics (age, sex, staging, pathology, pretreatment Epstein-Barr virus level, and treatment type) were analyzed. Patients underwent follow-up imaging 6 months after treatment initiation. Treatment responses were assigned according to the Response Evaluation Criteria in Solid Tumors guideline (version 1.1)., Results: Fifty-one patients showed complete response (CR), whereas 32 patients did not (non-CR). Univariable logistic regression with variables dichotomized by optimal cutoff values showed that ADC ≥1.45 × 10 -3 mm 2 /s, Vp ≥0.14, tumor volume of ≥14.05 mL, high stage (stages III and IV), and Epstein-Barr virus level of ≥2300 copies/mL were predictors of non-CR ( P = 0.008, 0.05, 0.01, 0.009, and 0.04, respectively). The final multivariable model, consisting of a combination of ADC ≥1.45 × 10 -3 mm 2 /s, Vp ≥0.14, and high stage, could predict non-CR with a good discrimination ability (area under the receiver operating characteristic curve, 0.76 [95% confidence interval, 0.66-0.87]; sensitivity, 62.50%; specificity, 80.39%; and accuracy 73.49%)., Conclusions: A multivariable prediction model using a combination of ADC ≥1.45 × 10 -3 mm 2 /s, Vp ≥0.14, and high stage can be effective for treatment response prediction in NPC patients., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose survivability is greatly improved if early diagnosis and timely treatment are provided. Accurate segmentation of both the primary NPC tumors and metastatic lymph nodes (MLNs) is crucial for patient staging and radiotherapy scheduling. However, existing studies mainly focus on the segmentation of primary tumors, eliding the recognition of MLNs, and thus fail to comprehensively provide a landscape for tumor identification. There are three main challenges in segmenting primary NPC tumors and MLNs: variable location, variable size, and irregular boundary. To address these challenges, we propose an automatic segmentation network, named by NPCNet, to achieve segmentation of primary NPC tumors and MLNs simultaneously. Specifically, we design three modules, including position enhancement module (PEM), scale enhancement module (SEM), and boundary enhancement module (BEM), to address the above challenges. First, the PEM enhances the feature representations of the most suspicious regions. Subsequently, the SEM captures multiscale context information and target context information. Finally, the BEM rectifies the unreliable predictions in the segmentation mask. To that end, extensive experiments are conducted on our dataset of 9124 samples collected from 754 patients. Empirical results demonstrate that each module realizes its designed functionalities and is complementary to the others. By incorporating the three proposed modules together, our model achieves state-of-the-art performance compared with nine popular models.

Purpose: How to discriminate different risks of recurrent nasopharyngeal carcinoma (rNPC) patients and guide individual treatment has become of great importance. This study aimed to explore the associations between deep learning signatures and biological functions as well as survival in (rNPC) patients., Methods: A total of 420 rNPC patients with PET/CT imaging and follow-up of overall survival (OS) were retrospectively enrolled. All patients were randomly divided into a training set (n = 269) and test set (n = 151) with a 6:4 ratio. We constructed multi-modality deep learning signatures from PET and CT images with a light-weighted deep convolutional neural network EfficienetNet-lite0 and survival loss DeepSurvLoss. An integrated nomogram was constructed incorporating clinical factors and deep learning signatures from PET/CT. Clinical nomogram and single-modality deep learning nomograms were also built for comparison. Furthermore, the association between biological functions and survival risks generated from an integrated nomogram was analyzed by RNA sequencing (RNA-seq)., Results: The C-index of the integrated nomogram incorporating age, rT-stage, and deep learning PET/CT signature was 0.741 (95% CI: 0.688-0.794) in the training set and 0.732 (95% CI: 0.679-0.785) in the test set. The nomogram stratified patients into two groups with high risk and low risk in both the training set and test set with hazard ratios (HR) of 4.56 (95% CI: 2.80-7.42, p < 0.001) and 4.05 (95% CI: 2.21-7.43, p < 0.001), respectively. The C-index of the integrated nomogram was significantly higher than the clinical nomogram and single-modality nomograms. When stratified by sex, N-stage, or EBV DNA, risk prediction of our integrated nomogram was valid in all patient subgroups. Further subgroup analysis showed that patients with a low-risk could benefit from surgery and re-irradiation, while there was no difference in survival rates between patients treated by chemotherapy in the high-risk and low-risk groups. RNA sequencing (RNA-seq) of data further explored the mechanism of high- and low-risk patients from the genetic and molecular level., Conclusion: Our study demonstrated that PET/CT-based deep learning signatures showed satisfactory prognostic predictive performance in rNPC patients. The nomogram incorporating deep learning signatures successfully divided patients into different risks and had great potential to guide individual treatment: patients with a low-risk were supposed to be treated with surgery and re-irradiation, while for high-risk patients, the application of palliative chemotherapy may be sufficient., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: To validate tumor volume-based imaging markers for predicting local recurrence-free survival (LRFS) in locoregionally advanced nasopharyngeal carcinoma patients, who underwent induction chemotherapy followed by definitive intensity-modulated radiotherapy., Methods: We enrolled 145 patients with stage III-IVA nasopharyngeal carcinoma in this retrospective study. Pre-treatment tumor volume (V pre ) and late-course volume (LCV) were measured based on the MRIs scanned before treatment and during the first 3 days in the sixth week of radiotherapy, respectively. The volume regression rate (VRR) was calculated according to V pre and LCV. Receiver operating characteristic (ROC) curves were used to identify the cut-off best separating patient subgroups in assessing the prognostic value of V pre, LCV and VRR. The Kaplan-Meier method was used for survival analysis. Prognostic analyses were performed using univariate and multivariate COX proportional hazard models., Results: The LCV was 5.3 ± 0.5 (range 0-42.1) cm 3 ; The VRR was 60.4 ± 2.2% (range 2.9-100.0). The median follow-up period was 36 months (range 6-98 months). The cut-off value of LCV determined by the ROC was 6.8 cm 3 for LRFS prediction (sensitivity 68.8%; specificity 79.8%). The combination of LCV and VRR for LRFS prediction (AUC = 0.79, P < 0.001, 95% CI 0.67-0.90), LCV (AUC = 0.74, P = 0.002, 95% CI 0.60-0.88) and V pre (AUC = 0.71, P = 0.007, 95% CI 0.56-0.85) are better than T category (AUC = 0.64, P = 0.062, 95% CI 0.50-0.79) alone. Patients with LCV ≤ 6.8 cm 3 had significantly longer LRFS (P < 0.001), disease-free survival (DFS, P < 0.001) and overall survival (OS, P = 0.005) than those with LCV > 6.8 cm 3 . Multivariate Cox regression showed LCV was the only independent prognostic factor for local control (HR = 7.80, 95% CI 2.69-22.6, P < 0.001)., Conclusions: LCV is a promising prognostic factor for local control and chemoradiosensitivity in patients with locoregionally advanced NPC. The LCV, and the combination of LCV with VRR are more robust predictors for patient survival than T category., (© 2022. The Author(s).)

Purpose: We aimed to predict the prognosis of advanced nasopharyngeal carcinoma (stage Ⅲ-Ⅳa) using Pre- and Post-treatment MR images based on deep learning (DL)., Methods: A total of 206 patients with primary nasopharyngeal carcinoma who were diagnosed and treated at the Renmin Hospital of Wuhan University between June 2012 and January 2018 were retrospectively selected. A rectangular region of interest (ROI), which included the tumor area, surrounding tissues and organs, was delineated on each Pre- and Post-treatment MR image. Two Inception-Resnet-V2 based transfer learning models, named Pre-model and Post-model, were trained with the Pre-treatment images and the Post-treatment images, respectively. In addition, an ensemble learning model based on the Pre-model and Post-models was established. The three established models were evaluated by receiver operating characteristic curve (ROC), confusion matrix, and Harrell's concordance indices (C-index). High-risk-related gradient-weighted class activation mapping (Grad-CAM) images were developed according to the DL models., Results: The Pre-model, Post-model, and ensemble model displayed a C-index of 0.717 (95% CI: 0.639 to 0.795), 0.811 (95% CI: 0.745-0.877), 0.830 (95% CI: 0.767-0.893), and AUC of 0.741 (95% CI: 0.584-0.900), 0.806 (95% CI: 0.670-0.942), and 0.842 (95% CI: 0.718-0.967) for the test cohort, respectively. In comparison with the models, the performance of Post-model was better than the performance of Pre-model, which indicated the importance of Post-treatment images for prognosis prediction. All three DL models performed better than the TNM staging system (0.723, 95% CI: 0.567-0.879). The captured features presented on Grad-CAM images suggested that the areas around the tumor and lymph nodes were related to the prognosis of the tumor., Conclusions: The three established DL models based on Pre- and Post-treatment MR images have a better performance than TNM staging. Post-treatment MR images are of great significance for prognosis prediction and could contribute to clinical decision-making., Competing Interests: Declaration of Competing Interest All authors declared that there were no competing interests., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Nasopharyngeal carcinoma (NPC) is a common malignant tumor of the head and neck with a high incidence rate worldwide, especially in southern China. Phototheranostics in combination with nanoparticles is an integrated strategy for enabling simultaneous diagnosis, real-time monitoring, and administration of precision therapy for nasopharyngeal carcinoma (NPC). It has shown great potential in the field of cancer diagnosis and treatment owing to its unique noninvasive advantages. Many Chinese and international research teams have applied nano-targeted drugs to optical diagnosis and treatment technology to conduct multimodal imaging and collaborative treatment of NPC, which has become a hot research topic. In this review, we aimed to introduce the recent developments in phototheranostics of NPC based on a nanoplatform. This study aimed to elaborate on the applications of nanoplatform-based optical imaging strategies and treatment modalities, including fluorescence imaging, photoacoustic imaging, Raman spectroscopy imaging, photodynamic therapy, and photothermal therapy. This study is expected to provide a scientific basis for further research and development of NPC diagnosis and treatment., Competing Interests: The authors declare that there are no conflicts of interest related to this article., (© 2022 Lin et al.)

The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image super-resolution reconstruction algorithm optimized convolutional neural network (OPCNN) algorithm in nasopharyngeal carcinoma (NPC) lesion diagnosis. A total of 54 patients with NPC were selected as research objects. Based on the traditional CNN structure, OPCNN was proposed. Besides, MRI processed by the traditional CNN model and the U-net network model was introduced to be analyzed and compared with its algorithm. The used assessment parameters included volume transfer constant ( K trans ), rate constant ( K ep ), volume fraction ( V e ), and apparent diffusion coefficient (ADC). The results showed that the values of Dice coefficient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the OPCNN algorithm were significantly higher than those of the traditional CNN model and the U-net network model. Meanwhile, the difference was statistically significant ( P  < 0.05). K trans , K ep , and V e in tumor lesions were significantly higher than those in the healthy side, while the ADC was significantly lower than that in the healthy side ( P  < 0.05). The sensitivity, specificity, and accuracy of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) in the diagnosis of nasopharyngeal carcinoma staging were slightly higher than those in T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). The diagnostic sensitivity of DCE-MRI was more than 85%, its diagnostic specificity was more than 75%, and its diagnostic accuracy was more than 90%. The AUC area of NPC diagnosed by combination of the three was significantly different from that diagnosed by single T2WI, DWI, and DCE-MRI ( P  < 0.05). The diagnostic accuracy of MRI based on the OPCNN algorithm for nasopharyngeal carcinoma (93.2%) was significantly higher than that of single MRI (76.4%). In summary, the OPCNN algorithm proposed in this study could improve the quality of MRI images, and the effect was better than the traditional deep learning model, which had the value of clinical promotion. The application value of DCE-MRI in the diagnosis of pathogenic lesions of nasopharyngeal carcinoma was better than conventional MRI. The combined application of T2WI, DWI, and DCE-MRI in the screening of nasopharyngeal carcinoma lesions could greatly improve the diagnostic accuracy of nasopharyngeal carcinoma., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Ruijie Huang et al.)

Purposes: To systematically review and perform meta-analysis to evaluate the prognostic value of cervical nodal necrosis (CNN) on the staging computed tomography/magnetic resonance imaging (MRI) of nasopharyngeal carcinoma (NPC) in era of intensity-modulated radiotherapy., Methods: Literature search through PubMed, EMBASE, and Cochrane Library was conducted. The hazard ratios (HRs) with 95% confidence intervals (CIs) of CNN for distant metastasis-free survival (DMFS), disease free survival (DFS) and overall survival (OS) were extracted from the eligible studies and meta-analysis was performed to evaluate the pooled HRs with 95%CI., Results: Nine studies, which investigated the prognostic values of 6 CNN patterns on MRI were included. Six/9 studies were eligible for meta-analysis, which investigated the CNN presence/absence in any nodal group among 4359 patients. The pooled unadjusted HRs showed that the CNN presence predicted poor DMFS (HR =1.89, 95%CI =1.72-2.08), DFS (HR =1.57, 95%CI =1.08-2.26), and OS (HR =1.87, 95%CI =1.69-2.06). The pooled adjusted HRs also showed the consistent results for DMFS (HR =1.34, 95%CI =1.17-1.54), DFS (HR =1.30, 95%CI =1.08-1.56), and OS (HR =1.61, 95%CI =1.27-2.04). Results shown in the other studies analysing different CNN patterns indicated the high grade of CNN predicted poor outcome, but meta-analysis was unable to perform because of the heterogeneity of the analysed CNN patterns., Conclusion: The CNN observed on the staging MRI is a negative factor for NPC outcome, suggesting that the inclusion of CNN is important in the future survival analysis. However, whether and how should CNN be included in the staging system warrant further evaluation., (© 2022. The Author(s).)

Background: To rethink the clinical significance of standardized uptake values (SUVs) of nasopharyngeal carcinoma (NPC) on 18 F-fluorodeoxyglucose ( 18 F-FDG) positron-emission tomography (PET)., Methods: We retrospectively reviewed 369 NPC patients who underwent pretreatment 18 F-FDG PET. The predictive value of the SUVmax of the primary tumor (SUVmax-t) and regional lymph nodes (SUVmax-n) was evaluated using probability density functions. Receiver operating characteristic curves were used to determine optimal cutoffs for the SUVmax-n/SUVmax-t ratio (NTR). Kaplan-Meier and Cox regression analyses were used to assess survival., Results: The optimal SUVmax-t and SUVmax-n cutoffs were 7.5 and 6.9, respectively. High SUVmax-t and SUVmax-n were related to local and regional recurrence, respectively. Patients with low SUVmax had better 3-year overall survival (OS). To avoid cross-sensitization of cutoff points, we stratified patients with high SUVmax into the low and high NTR groups. The 3-year distant metastasis-free survival (DMFS; 92.3 vs. 80.6%, P = 0.009), progression-free survival (PFS; 84.0 vs. 67.7%, P = 0.011), and OS (95.9 vs. 89.2%, P = 0.002) significantly differed between the high vs. low NTR groups for patients with high SUVmax. Multivariable analysis showed that NTR was an independent prognostic factor for DMFS (hazard ratio [HR]: 2.037, 95% CI: 1.039-3.992, P = 0.038), PFS (HR: 1.636, 95% CI: 1.021-2.621, P = 0.041), and OS (HR: 2.543, 95% CI: 1.214-5.325, P = 0.013)., Conclusion: High SUVmax was associated with NPC recurrence. NTR is a potential prognosticator for DMFS, suggesting that heterogeneity in the pretreatment 18 F-FDG uptake between the primary tumor and lymph nodes is associated with high invasion and metastatic potential., (© 2022. The Author(s).)

Background: The purpose of this study was to explore the prognostic value of imaging features and related models in nasopharyngeal carcinoma (NPC) patients that received neoadjuvant chemotherapy., Materials and Methods: We systematically reviewed the data of 110 NPC patients who received radiotherapy and neoadjuvant chemotherapy. The patients were randomly divided into the training cohort (n = 88) and the verification cohort (n = 22). The imaging data collected in this study were screened via Pyramidics and used to construct prediction models based on histology and clinical nomographs. The models' accuracy was evaluated via calibration curves and the consistency index (C-index). In addition, we also explored the correlation between radiomics expression patterns, quantitative histological characteristics, and clinical data and then constructed a model to predict the prognosis of NPC., Results: The models that integrated radiomics contours with all the clinical data were superior to those based on the clinical data alone (C-index 0.746 vs. C-index 0.814, respectively) and the calibration curves showed good consistency. The heat map showed that the radiomics expression pattern and selected histological characteristics were correlated with the clinical stage, T stage, and N stage (p < 0.05), and no radiomics feature was associated with lactate dehydrogenase expression, lymphocyte count, or mononuclear cell count., Conclusion: MRI-based radiomics can significantly improve the efficacy of traditional TNM staging and clinical data in predicting the progression-free survival (PFS) of patients with advanced NPC, which may provide an opportunity for precision medicine., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets., Competing Interests: Declaration of Competing Interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2022. Published by Elsevier B.V.)

Abstract: A 73-year-old man with newly diagnosed nasopharyngeal carcinoma included in a clinical trial of 68Ga-FAPI PET/CT study in tumors (ChiCTR2100044131). Increased tracer uptake was noted in nasopharyngeal carcinoma and cervical lymph nodes metastases. In addition, avascular necrosis of right femoral head also revealed increased FAPI activity., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives/hypothesis: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasopharyngoscopy images., Study Design: Retrospective study., Methods: A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S-DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance. To verify the effectiveness of combining the above-mentioned two modal images for prediction, we compared the proposed S-DCNN with two baseline models, namely DCNN-1 (only considering WLI images) and DCNN-2 (only considering NBI images)., Results: In the threefold cross-validation, an overall accuracy and area under the curve of the three DCNNs achieved 94.9% (95% confidence interval [CI] 93.3%-96.5%) and 0.986 (95% CI 0.982-0.992), 87.0% (95% CI 84.2%-89.7%) and 0.930 (95% CI 0.906-0.961), and 92.8% (95% CI 90.4%-95.3%) and 0.971 (95% CI 0.953-0.992), respectively. The accuracy of S-DCNN is significantly improved compared with DCNN-1 (P-value <.001) and DCNN-2 (P-value = .008)., Conclusion: Using the deep-learning technology to automatically diagnose NPC under nasopharyngoscopy can provide valuable reference for NPC screening. Superior performance can be obtained by simultaneously utilizing the multimodal features of NBI image and WLI image of the same patient., Level of Evidence: 3 Laryngoscope, 132:999-1007, 2022., (© 2021 The American Laryngological, Rhinological and Otological Society, Inc.)

Purpose: Our study aims to compare the diagnostic value of 18 F-NaF positron emission tomography-computed tomography (PET/CT), 18 F-NaF PET, and planar 99m Tc-MDP bone scintigraphy for detection of bone metastases in patients with newly diagnosed nasopharyngeal carcinoma (NPC)., Methods: Our study retrospectively analyzed 58 patients with pathologically proven NPC. They all underwent both 18 F-NaF PET/CT and planar 99m Tc-MDP bone scintigraphy within a 7-day interval. Bone metastases were confirmed by follow-up using PET/CT, contrast-enhanced computed tomography (CT), and magnetic resonance imaging (MRI). These three examinations were compared using per-patient-based analysis and per-lesion-based analysis., Results: 19 patients (32.7%) were classified as having bone metastatic disease in their final diagnosis. The patient-based diagnostic performances (sensitivity, specificity, and overall accuracy) were as follows: 18 F-NaF PET/CT (100%, 92.3%, and 94.8%), 18 F-NaF PET (100%, 53.8%, and 69.0%), and planar 99m Tc-MDP bone scintigraphy (78.9%, 74.4%, and 75.9%). The overall accuracy of 18 F-NaF PET/CT was significantly more favorable compared to 18 F-NaF PET ( p =0.002) and to planar 99m Tc-MDP bone scintigraphy ( p =0.044). The lesion-based diagnostic performances (sensitivity, specificity, and overall accuracy) were as follows: 18 F-NaF PET/CT (98.5%, 93.9%, and 96.6%), 18 F-NaF PET (98.5%, 57.1%, and 81.1%), and planar 99m Tc-MDP bone scintigraphy (69.9%, 85.7%, and 76.4%)., Conclusion: 18 F-NaF PET/CT outperforms 18 F-NaF PET or planar 99m Tc-MDP bone scintigraphy in detecting bone metastases with newly diagnosed NPC on a patient-based and lesion-based analysis., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Dong Wang et al.)

Background and Objectives: Administration of contrast is not desirable for all cases in clinical setting, and no consensus in sequence selection for deep learning model development has been achieved, thus we aim to explore whether contrast-enhanced magnetic resonance imaging (ceMRI) can be substituted in the identification and segmentation of nasopharyngeal carcinoma (NPC) with the aid of deep learning models in a large-scale cohort., Methods: A total of 4478 eligible individuals were randomly split into training, validation and test sets, and self-constrained 3D DenseNet and V-Net models were developed using axial T1-weighted imaging (T1WI), T2WI or enhanced T1WI (T1WIC) images separately. The differential diagnostic performance between NPC and benign hyperplasia were compared among models using chi-square test. Segmentation evaluation metrics, including dice similarity coefficient (DSC) and average surface distance (ASD), were compared using paired student's t-test between T1WIC and T1WI or T2WI models or M&#95;T1/T2, a merged output of malignant region derived from T1WI and T2WI models., Results: All models exhibited similar satisfactory diagnostic performance in discriminating NPC from benign hyperplasia, all attaining overall accuracy over 99.00% in all T stages of NPC. And T1WIC model exhibited similar average DSC and ASD with those of M&#95;T1/T2 (DSC, 0.768±0.070 vs 0.764±0.070; ASD, 1.573±10.954 mm vs 1.626±10.975 mm 1.626±0.975 mm vs 1.573±0.954 mm, all p > 0.0167) in primary NPC using DenseNet, but yielded a significantly higher DSC and lower ASD than either T1WI model or T2WI model (DSC, 0.759±0.065 or 0.755±0.071; ASD, 1.661±0.898 mm or 1.722±1.133 mm, respectively, all p < 0.01) in the entire test set of NPC cohort. Moreover, the average DSCs and ASDs were not statistically significant between T1WIC model and M&#95;T1/T2 in both., Competing Interests: Declaration of Competing Interest None of the authors has any potential financial conflict of interest related to this manuscript., (Copyright © 2022 Elsevier B.V. All rights reserved.)

PET/CT scan has been used as a tool for the diagnosis and management of nasopharyngeal carcinoma (NPC). It has been proven to be highly valuable for the imaging and management of patients with NPC with strengths in N and M staging as well as treatment planning and is recommended to be incorporated into the standard clinical assessment. Novel quantitative techniques such as the use of radiomics may provide valuable prognostic information., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: We longitudinally evaluated the tumour growth and metabolic activity of three nasopharyngeal carcinoma (NPC) cell line models (C666-1, C17 and NPC43) and two xenograft models (Xeno76 and Xeno23) using a micropositron emission tomography and magnetic resonance (microPET/MR). With a better understanding of the interplay between tumour growth and metabolic characteristics of these NPC models, we aim to provide insights for the selection of appropriate NPC cell line/xenograft models to assist novel drug discovery and evaluation., Methods: Mice were imaged by 18 F-deoxyglucose ([ 18 F]FDG) microPET/MR twice a week for consecutive 3-7 weeks. [ 18 F]FDG uptake was quantified by standardized uptake value (SUV) and presented as SUVmean tumour-to-liver ratio (SUVRmean). Longitudinal tumour growth patterns and metabolic patterns were recorded. SUVRmean and histological characteristics were compared across the five NPC models. Cisplatin was administrated to one selected optimal tumour model, C17, to evaluate our imaging platform., Results: We found variable tumour growth and metabolic patterns across different NPC tumour types. C17 has an optimal growth rate and higher tumour metabolic activity compared with C666-1. C666-1 has a fast growth rate but is low in SUVRmean at endpoint due to necrosis as confirmed by H&E. NPC43 and Xeno76 have relatively slow growth rates and are low in SUVRmean, due to severe necrosis. Xeno23 has the slowest growth rate, and a relative high SUVRmean. Cisplatin showed the expected therapeutic effect in the C17 model in marked reduction of tumour size and metabolism., Conclusion: Our study establishes an imaging platform that characterizes the growth and metabolic patterns of different NPC models, and the platform is well able to demonstrate drug treatment outcome supporting its use in novel drug discovery and evaluation for NPC., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Abstract: Accurate clinical staging is crucial for managing nasopharyngeal carcinoma (NPC). However, false-positive finding of cervical lymph nodes in 18F-FDG PET/CT and MRI may lead to unnecessary chemotherapy for NPC. In this case, we report a patient with NPC who was incorrectly diagnosed with bilateral cervical lymph node metastases by both 18F-FDG PET/CT and MRI. Subsequent pathology results showed proliferating lymphoid cells with no signs of tumor metastasis. Interestingly, no abnormal 68Ga-FAPI uptake was observed in the nonmetastatic reactive lymph nodes. Therefore, 68Ga-FAPI PET/CT may be useful for assessment of pretreatment lymph node status in NPC., Competing Interests: Conflicts of interest and sources of funding: This report was funded by National Natural Science Foundation of China (grant number 82071961). The authors declare no conflicts of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)