Search

Rapid advances in nanotechnology are paving the way for innovative breakthroughs in overcoming the current limitations in the clinical treatment of cancer and other prevalent diseases plaguing mankind. Magnetic nanoparticles composed of iron oxide (Fe 3 O 4 ) are a novel class of nanoparticles that are receiving increasing attention in the field of cancer therapy. To address the inherent limitations, bare Fe 3 O 4 can be functionalized, polymerized, assembled, or combined with other functional materials to produce a range of smart nanoplatforms suitable for tumor therapy. In this paper, we present a unique multifunctional therapeutic nanoplatform centered on aldehyde-oxidized sodium alginate-stabilized iron oxide nanoparticles (NPs) designed for T 2 -weighted magnetic resonance (MR) imaging. Sodium alginate oxide and ferric oxide nanoparticles were prepared respectively, and the two particles were mixed in a certain molar ratio to form a complex, which was coupled to target polypeptide GE11 by Schiff base reaction, and finally supported by cisplatin through coordination complexation. The prepared magnetic nanoparticles (hereinafter referred to as GE11-CDDP-ASA@Fe 3 O 4 ) have an average diameter of 152.9 nm, and have good colloidal stability and cytocompatibility. The distinctive structure and composition of GE11-CDDP-ASA@Fe 3 O 4 contribute to its excellent MRI imaging performance, positioning it as a nano platform suitable for enhancing the efficacy of combination therapy in tumor treatment. This is of great significance for translational nanomedicine applications., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2024. Published by Elsevier B.V.)

Mouse orthotopic xenograft tumor models are commonly employed in studies investigating the mechanisms underlying the development and progression of tumors and their preclinical treatment. However, the unavailability of mature and visualized orthotopic xenograft models of nasopharyngeal carcinoma limits the development of treatment strategies for this cancer. The aim of this study was to provide a simple and reliable method for building an orthotopic xenograft model of nasopharyngeal carcinoma. Human nasopharyngeal carcinoma (C666-1-luc) cells, stably expressing the firefly luciferase gene, were injected subcutaneously into the right axilla of BALB/C nude mice. Four weeks later, the resulting subcutaneous tumors were cut into small blocks and grafted into the nasopharynx of immunodeficient BALB/C nude mice to induce tumor formation. Tumor growth was monitored by bioluminescence imaging and small animal magnetic resonance imaging (MRI). The expression of histological and immunological antigens associated with orthotopic xenograft nasopharyngeal carcinoma was analyzed by tissue section analysis and immunohistochemistry (IHC). A visualized orthotopic xenograft nasopharyngeal carcinoma model was successfully developed in this study. Luminescence signal detection, micro-MRI, and hematoxylin and eosin staining revealed the successful growth of tumors in the nasopharynx of the nude mice. Moreover, IHC analysis detected cytokeratin (CK), CK5/6, P40, and P63 expression in the orthotopic tumors, consistent with the reported expression of these antigens in human nasopharyngeal tumors. This study established a reproducible, visual, and less lethal orthotopic xenograft model of nasopharyngeal carcinoma, providing a platform for preclinical research.

Objective: This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM)., Methods: A retrospective multi-center study was conducted, which included 389 NPC patients who received NACT from three institutions. Radiomics features were extracted from magnetic resonance imaging scans, while pathomics features were derived from histopathological images. A total of 2,667 radiomics features and 254 pathomics features were initially extracted. Feature selection involved intra-class correlation coefficient evaluation, Mann-Whitney U test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. The PSO-SVM model was constructed and validated using 10-fold cross-validation on the training set and further evaluated using an external validation set. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curves, and decision curve analysis., Results: Eight significant predictive features (five radiomics and three pathomics) were identified. The PSO-SVM radiopathomics model achieved superior performance compared to models based solely on radiomics or pathomics features. The AUCs for the PSO-SVM radiopathomics model were 0.917 (95% CI: 0.887-0.948) in internal validation and 0.814 (95% CI: 0.742-0.887) in external validation. Calibration curves demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis showed that the PSO-SVM radiopathomics model provided higher clinical net benefit over a wider range of risk thresholds compared to other models., Conclusion: The PSO-SVM radiopathomics model effectively integrates radiomics and pathomics features, offering enhanced predictive accuracy and clinical utility for assessing NACT efficacy in NPC. The multi-center approach and robust validation underscore its potential for personalized treatment planning, supporting improved clinical decision-making for NPC patients., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Ethics Committee of the Affiliated Hospital of Southwest Medical University, Qingyang People’s Hospital and Xinzhou People’s Hospital (KY2023041; LC-202427; XZRY-2024030) and in accordance with the Declaration of Helsinki. Informed consent was obtained from all the patients. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Automating the segmentation of nasopharyngeal carcinoma (NPC) is crucial for therapeutic procedures but presents challenges given the hurdles in amassing extensively annotated datasets. Although previous studies have applied self-supervised learning to capitalize on unlabeled data to improve segmentation performance, these methods often overlooked the benefits of dual-sequence magnetic resonance imaging (MRI). In the present study, we incorporated self-supervised learning with a saliency transformation module using unlabeled dual-sequence MRI for accurate NPC segmentation. 44 labeled and 72 unlabeled patients were collected to develop and evaluate our network. Impressively, our network achieved a mean Dice similarity coefficient (DSC) of 0.77, which is consistent with a previous study that relied on a training set of 4,100 annotated cases. The results further revealed that our approach required minimal adjustments, primarily < 20% tweak in the DSC, to meet clinical standards. By enhancing the automatic segmentation of NPC, our method alleviates the annotation burden on oncologists, curbs subjectivity, and ensures reliable NPC delineation., 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 Elsevier Ltd. All rights reserved.)

This study investigates the utility of 18 F-sodium fluoride ( 18 F-NaF) positron emission tomography/computed tomography (PET/CT) in assessing skull-base bone invasion (SBBI) and predicting treatment response in advanced nasopharyngeal carcinoma (NPC). A retrospective analysis was conducted on 142 patients with newly diagnosed advanced NPC who underwent 18 F-NaF PET/CT for initial staging from December 2020 to December 2023. 18 F-NaF PET/CT scans were analyzed for uptake values at the skull-base bone, and these were correlated with treatment outcomes of primary tumor using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. Statistical analyses involved Mann-Whitney U tests for group comparisons and logistic regression for evaluating risk factors. Higher 18 F-NaF uptake at the skull-base bone was significantly associated with advanced T stages (p < 0.0001) and the presence of bone metastases (p = 0.01). Patients exhibiting complete response (CR) to treatment had significantly lower skull-base 18 F-NaF uptake compared to those with non-CR (p < 0.001). Receiver operating characteristic (ROC) analysis identified an SUV max  > 10.0 and SUV mean  > 5.2 as predictive of non-CR, with AUC values of 0.77 and 0.76, respectively. Univariate and multivariable analysis confirmed SUV max as a significant predictor of treatment response (OR = 7.03, 95% CI: 1.97-25.13, p < 0.05). Elevated 18 F-NaF uptake at the skull-base bone is predictive of poorer treatment outcomes, highlighting its potential as a prognostic biomarker in advanced NPC. This study demonstrates that 18 F-NaF PET/CT is a valuable imaging modality for evaluating SBBI in NPC, offering metabolic information that complements the anatomical findings from MRI., Competing Interests: Competing interests: The authors declare no competing interests. Ethical approval and consent to participate: This retrospective study received ethical clearance and the requirement to obtain informed consent was waived by the institutional review boards of Affiliated Hospital of Guilin Medical University (ID: 2023QTLL-16). The study protocol adheres to the tenets of the 1964 Declaration of Helsinki., (© 2024. The Author(s).)

Accurate T-staging of nasopharyngeal carcinoma (NPC) holds paramount importance in guiding treatment decisions and prognosticating outcomes for distinct risk groups. Regrettably, the landscape of deep learning-based techniques for T-staging in NPC remains sparse, and existing methodologies often exhibit suboptimal performance due to their neglect of crucial domain-specific knowledge pertinent to primary tumor diagnosis. To address these issues, we propose a new cross-domain mutual-assistance learning framework for fully automated diagnosis of primary tumor using H&N MR images. Specifically, we tackle primary tumor diagnosis task with the convolutional neural network consisting of a 3D cross-domain knowledge perception network (CKP net) for excavated cross-domain-invariant features emphasizing tumor intensity variations and internal tumor heterogeneity, and a multi-domain mutual-information sharing fusion network (M2SF net), comprising a dual-pathway domain-specific representation module and a mutual information fusion module, for intelligently gauging and amalgamating multi-domain, multi-scale T-stage diagnosis-oriented features. The proposed 3D cross-domain mutual-assistance learning framework not only embraces task-specific multi-domain diagnostic knowledge but also automates the entire process of primary tumor diagnosis. We evaluate our model on an internal and an external MR images dataset in a three-fold cross-validation paradigm. Exhaustive experimental results demonstrate that our method outperforms the other algorithms, and obtains promising performance for tumor segmentation and T-staging. These findings underscore its potential for clinical application, offering valuable assistance to clinicians in treatment decision-making and prognostication for various risk groups.

Purpose: This study assesses the reliability of deep learning models based on planar whole-body bone scintigraphy for diagnosing Skull base invasion (SBI) in nasopharyngeal carcinoma (NPC) patients., Methods: In this multicenter study, a deep learning model was developed using data from one center with a 7:3 allocation to training and internal test sets, to diagnose SBI in patients newly diagnosed with NPC using planar whole-body bone scintigraphy. Patients were diagnosed based on a composite reference standard incorporating radiologic and follow-up data. Ten different convolutional neural network (CNN) models were applied to both whole-image and partial-image input modes to determine the optimal model for each analysis. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis (DCA), and compared with expert assessments by two nuclear medicine physicians., Results: The best-performing model using partial-body input achieved AUCs of 0.80 (95% CI: 0.73, 0.86) in the internal test set, 0.84 (95% CI: 0.77, 0.91) in the external cohort, and 0.78 (95% CI: 0.73, 0.83) in the treatment test cohort. Calibration curves and DCA confirmed the models' excellent discrimination, calibration, and potential clinical utility across internal and external datasets. The AUCs of both nuclear medicine physicians were lower than those of the best-performing deep learning model in external test set (AUC: 0.75 vs. 0.77 vs. 0.84)., Conclusion: Deep learning models utilizing partial-body input from planar whole-body bone scintigraphy demonstrate high discriminatory power for diagnosing SBI in NPC patients, surpassing experienced nuclear medicine physicians., (© 2024. The Author(s).)

The deep learning (DL)-based prediction of accurate lymph node (LN) clinical target volumes (CTVs) for nasopharyngeal carcinoma (NPC) radiotherapy (RT) remains challenging. One of the main reasons is the variability of contours despite standardization processes by expert guidelines in combination with scarce data sharing in the community. Therefore, we retrospectively generated a 262-subjects dataset from four centers to develop the DL models for LN CTVs delineation. This dataset included 440 computed tomography images from different scanning phases, disease stages and treatment strategies. Three clinical expert boards, each comprising two experts (totalling six experts), manually delineated six basic LN CTVs on separate cohorts as the ground truth according to LN involvement and clinical requirements. Several state-of-the-art segmentation algorithms were evaluated on this benchmark, showing promising results for LN CTV segmentation. In conclusion, this work built a multicenter LN CTV segmentation dataset, which may be the first dataset for automatic LN CTV delineation development and evaluation, serving as a benchmark for future research., (© 2024. The Author(s).)

Objective: This study aims to use the maximum standardised uptake value (SUV max ) of 18F-fluorodeoxyglucose positron emission tomography to improve the cost effectiveness of nimotuzumab (NTZ) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC)., Methods: Two hundred and forty-eigh patients with LA-NPC, who met the inclusion criteria, were recruited from January 2012 to June 2019. The survival differences and independent factors were assessed using the Kaplan-Meier method and by Cox proportional hazards regression analysis. A cost effectiveness analysis was performed., Results: The optimal cut-off value for SUV max was 12.92. Multivariable analysis indicated a prognostic significance of overall survival (OS) for the NTZ treatment (p = 0.023) and SUV max (p = 0.014). The exploratory subgroup survival analysis revealed that LA-NPC patients with SUV max > 12.92 treated with concurrent chemoradiotherapy (CCRT) and NTZ had a significantly improved 3-year OS compared to patients treated with CCRT alone (96.2 vs 73.2%, p = 0.047). Furthermore, the treatment cost for NTZ was $6,317.61. This incurred an additional cost of $274.68 for every 1% increase in OS., Conclusions: For patients with LA-NPC with SUV max > 12.92, the addition of NTZ to CCRT can improve OS and is cost effective., (Copyright © 2024 Società Italiana di Otorinolaringoiatria e Chirurgia Cervico-Facciale, Rome, Italy.)

Background: Treatment response evaluated by tumour size change is an important indicator for outcome prediction. Advanced nasopharyngeal carcinoma (adNPC) grows irregularly, and so the unidimensional measurement may not be accurately applied to adNPC for outcome prediction. This study aimed to evaluate values of unidimensional and volumetric measurements for treatment response to induction chemotherapy (IC) for outcome prediction in adNPC and compared the values with that of RECIST 1.1 guideline., Materials and Methods: Pre-treatment and post-IC magnetic resonance images (MRIs) from 124 patients with stage III-IVA NPC were retrospectively reviewed. Sums of the maximum unidimensional diameters (D) and volumes of the targeted tumours (primary tumour and two largest metastatic lymph nodes) on the pre- (D pre and V pre ) and post-IC MRIs (D post-IC and V post-IC ) and percentage changes in D (Δ D%) and V (ΔV%) between two scans were calculated and correlated with disease-free survival (DFS), locoregional recurrence-free survival (LRRFS), and distant metastases-free survival (DMFS) using Cox regression analysis. Area under the curves (AUCs) of independent measurements and RECIST groups (RECIST response and non-response groups) for predicting disease recurrence, locoregional recurrence, and distant metastases, respectively, were calculated and compared using the DeLong test., Results: Univariable analysis showed correlations between high D post-IC with poor DFS and DMFS (P < 0.05), but not with LRRFS (P = 0.07); high V post-IC and low ΔV% (less decrease in volume on post-IC) with poor DFS, LRRFS, and DMFS (P < 0.05); and no correlations between D pre , ΔD%, and V pre and the outcomes (P > 0.05). Multivariable analysis showed that ΔV% was the only independent measurement for outcomes (P < 0.05). Compared with RECIST groups, ΔV% of 47.9% (median value) showed a higher AUC for disease recurrence (0.682 versus 0.526, P < 0.01) and for locoregional recurrence (0.782 versus 0.585, P < 0.01), but not for distant metastases (0.593 versus 0.518, P = 0.26)., Conclusions: Volumetric measurement to evaluate treatment response to IC outperformed unidimensional measurement and RECIST guideline in outcome prediction in adNPC., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Recurrences are frequent in nasopharyngeal carcinoma (NPC) despite high remission rates with treatment, leading to considerable morbidity. This study aimed to develop a prediction model for NPC survival by harnessing both pre- and post-treatment magnetic resonance imaging (MRI) radiomics in conjunction with clinical data, focusing on 3-year progression-free survival (PFS) as the primary outcome. Our comprehensive approach involved retrospective clinical and MRI data collection of 276 eligible NPC patients from three independent hospitals (180 in the training cohort, 46 in the validation cohort, and 50 in the external cohort) who underwent MRI scans twice, once within 2 months prior to treatment and once within 10 months after treatment. From the contrast-enhanced T1-weighted images before and after treatment, 3404 radiomics features were extracted. These features were not only derived from the primary lesion but also from the adjacent lymph nodes surrounding the tumor. We conducted appropriate feature selection pipelines, followed by Cox proportional hazards models for survival analysis. Model evaluation was performed using receiver operating characteristic (ROC) analysis, the Kaplan-Meier method, and nomogram construction. Our study unveiled several crucial predictors of NPC survival, notably highlighting the synergistic combination of pre- and post-treatment data in both clinical and radiomics assessments. Our prediction model demonstrated robust performance, with an accuracy of AUCs of 0.66 (95% CI: 0.536-0.779) in the training cohort, 0.717 (95% CI: 0.536-0.883) in the testing cohort, and 0.827 (95% CI: 0.684-0.948) in validation cohort in prognosticating patient outcomes. Our study presented a novel and effective prediction model for NPC survival, leveraging both pre- and post-treatment clinical data in conjunction with MRI features. Its constructed nomogram provides potentially significant implications for NPC research, offering clinicians a valuable tool for individualized treatment planning and patient counseling., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: This study aims to develop a novel prediction model and risk stratification system that could accurately predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC)., Methods: Herein, we included 106 individuals diagnosed with NPC, who underwent 18 F-FDG PET/CT scanning before treatment. They were divided into training (n = 76) and validation (n = 30) sets. The prediction model was constructed based on multivariate Cox regression analysis results and its predictive performance was evaluated. Risk factor stratification was performed based on the nomogram scores of each case, and Kaplan-Meier curves were used to evaluate the model's discriminative ability for high- and low-risk groups., Results: Multivariate Cox regression analysis showed that N stage, M stage, SUV max , MTV, HI, and SIRI were independent factors affecting the prognosis of patients with NPC. In the training set, the model considerably outperformed the TNM stage in predicting PFS (AUCs of 0.931 vs. 0.841, 0.892 vs. 0.785, and 0.892 vs. 0.804 at 1-3 years, respectively). The calibration plots showed good agreement between actual observations and model predictions. The DCA curves further justified the effectiveness of the model in clinical practice. Between high- and low-risk group, 3-year PFS rates were significantly different (high- vs. low-risk group: 62.8% vs. 9.8%, p < 0.001). Adjuvant chemotherapy was also effective for prolonging survival in high-risk patients (p = 0.009)., Conclusion: Herein, a novel prediction model was successfully developed and validated to improve the accuracy of prognostic prediction for patients with NPC, with the aim of facilitating personalized treatment., (© 2024. The Author(s), under exclusive licence to The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.)

Purpose: To construct an integrative radiopathomics model for predicting progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC) patients., Methods: 357 NPC patients who underwent pretreatment MRI and pathological whole-slide imaging (WSI) were included in this study and randomly divided into two groups: a training set (n = 250) and validation set (n = 107). Radiomic features extracted from MRI were selected using the minimum redundancy maximum relevance and least absolute shrinkage and selection operator methods. The pathomics signature based on WSI was constructed using a deep learning architecture, the Swin Transformer. The radiopathomics model was constructed by incorporating three feature sets: the radiomics signature, pathomics signature, and independent clinical factors. The prognostic efficacy of the model was assessed using the concordance index (C-index). Kaplan-Meier curves for the stratified risk groups were tested by the log-rank test., Results: The radiopathomics model exhibited superior predictive performance with C-indexes of 0.791 (95% confidence interval [CI]: 0.724-0.871) in the training set and 0.785 (95% CI: 0.716-0.875) in the validation set compared to any single-modality model (radiomics: 0.619, 95% CI: 0.553-0.706; pathomics: 0.732, 95% CI: 0.662-0.802; clinical model: 0.655, 95% CI: 0.581-0.728) (all, P < 0.05). The radiopathomics model effectively stratified patients into high- and low-risk groups in both the training and validation sets (P < 0.001)., Conclusion: The developed radiopathomics model demonstrated its reliability in predicting PFS for NPC patients. It effectively stratified individual patients into distinct risk groups, providing valuable insights for prognostic assessment., (© 2024. The Author(s).)

Background: Radiomics has been widely used in the study of tumours, which has predictive and prognostic value in nasopharyngeal carcinoma (NPC). Therefore, we collected relevant literature to explore the role of current radiomics in predicting the prognosis of NPC., Methods: We performed a systematic literature review and meta-analysis in accordance with the preferred reporting items in the systematic evaluation and meta-analysis guidelines. We included papers on radiomics published before May 5, 2024, to evaluate the predictive ability of radiomics for the prognosis of NPC. The methodological quality of the included articles was evaluated using the radiomics quality score. The area under the curve (AUC), combined sensitivity and combined specificity were used to evaluate the ability of radiomics models to predict the prognosis of NPC., Results: A total of 20 studies met the inclusion criteria for the current systematic review, and 13 papers were included in the meta-analysis. The radiomics quality score ranged from 7 to 20 (maximum score: 36). The diagnostic test forest plots showed that the diagnostic OR of radiology was 11.04 (95% CI: 5.11-23.87), while the ORs for sensitivity and 1-specificity were 0.75 (95% CI: 0.73-0.78) and 0.74 (95% CI: 0.72-0.76), respectively. It cannot be determined whether the combined model was superior to the radiomics model for predicting the prognosis of NPC. It is unclear whether the fact that the radiomics model was composed of features extracted from MRI is due to CT. The AUC of PFS was larger than that of disease-free survival (P < .05). The overall AUC value is 0.8265., Conclusion: This study summarized all the studies that examined the predictive value of radiomics for NPC prognosis. Based on the summarized AUC values, as well as sensitivity and 1-specificity, it can be concluded that radiomics has good performance in predicting the prognosis of NPC. Radiomics models have certain advantages in predicting the effectiveness of PFS compared to predicting disease-free survival. It cannot be determined whether the combination model is superior to the radiomics model in predicting NPC prognosis, nor can it be determined whether imaging methods have differences in predictive ability. The findings confirmed and provided further evidence supporting the effectiveness of radiomics for the prediction of cancer prognosis., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Photothermal therapy (PTT) guided by photoacoustic imaging (PAI) using nanoplatforms has emerged as a promising strategy for cancer treatment due to its efficiency and accuracy. This study aimed to develop and synthesize novel second near-infrared region (NIR-II) absorption-conjugated polymer acceptor acrylate-substituted thiadiazoloquinoxaline-diketopyrrolopyrrole polymers (PATQ-DPP) designed specifically as photothermal and imaging contrast agents for nasopharyngeal carcinoma (NPC)., Methods: The PATQ-DPP nanoparticles were synthesized and characterized for their optical properties, including low optical band gaps. Their potential as PTT agents and imaging contrast agents for NPC was evaluated both in vitro and in vivo. The accumulation of nanoparticles at tumor sites was assessed post-injection, and the efficacy of PTT under near-infrared laser irradiation was investigated in a mouse model of NPC., Results: Experimental results indicated that the PATQ-DPP nanoparticles exhibited significant photoacoustic contrast enhancement and favorable PTT performance. Safety and non-toxicity evaluations confirmed the biocompatibility of these nanoparticles. In vivo studies showed that PATQ-DPP nanoparticles effectively accumulated at NPC tumor sites and demonstrated excellent tumor growth inhibition upon exposure to near-infrared laser irradiation. Notably, complete elimination of nasopharyngeal tumors was observed within 18 days following PTT., Discussion: The findings suggest that PATQ-DPP nanoparticles are a promising theranostic agent for NIR-II PAI and PTT of tumors. This innovative approach utilizing PATQ-DPP nanoparticles provides a powerful tool for the early diagnosis and precise treatment of NPC, offering a new avenue in the management of this challenging malignancy., Competing Interests: The authors declare no conflicts of interest in this work., (© 2024 Li et al.)

Background: Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor-node-metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck-lymph node radiotherapy in patients with NPC., Methods: Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression., Results: DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73-0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively., Conclusion: Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool., (© 2024. The Author(s).)

Objectives: Radiotherapy is the primary treatment for nasopharyngeal carcinoma, but it frequently leads to radiotherapy-induced temporal lobe injury (RTLI). Magnetic resonance imaging (MRI) is the main diagnostic method for RTLI after radiotherapy for nasopharyngeal carcinoma, but it is prone to missed diagnoses. This study aims to investigate the causes of missed diagnoses of RTLI in nasopharyngeal carcinoma patients undergoing MRI after radiotherapy., Methods: Clinical and MRI data from nasopharyngeal carcinoma patients diagnosed and treated with radiotherapy at Xiangya Hospital of Central South University, from January 2010 to April 2021, were collected. Two radiologists reviewed all head and neck MRIs (including nasopharyngeal and brain MRIs) before and after radiotherapy of identify cases of late delayed response-type RTLI for the first time. If the original diagnosis of the initial RTLI in nasopharyngeal carcinoma patients did not report temporal lobe lesions, it was defined as a missed diagnosis. The first diagnosis of RTLI cases was divided into a missed diagnosis group and a non-missed diagnosis group. Clinical and imaging data were compared between the 2 groups, and multivariate logistic regression analysis was used to identify independent risk factors for MRI missed diagnoses of initial RTLI., Results: A total of 187 nasopharyngeal carcinoma with post-radiotherapy RTLI were included. The original diagnostic reports missed 120 cases and accurately diagnosed 67 cases, with an initial RTLI diagnosis accuracy rate of 35.8% and a missed diagnosis rate of 64.2%. There were statistically significant differences between the missed diagnosis group and the non-missed diagnosis group in terms of lesion size, location, presence of contralateral temporal lobe lesions, white matter high signal, cystic degeneration, hemorrhage, fluid attenuated inversion recovery (FLAIR), and examination site (all P <0.05). Multivariate logistic regression analysis showed that lesions ≤25 mm, non-enhancing lesions, lesions without cystic degeneration or hemorrhage, lesions located only in the medial temporal lobe, and MRI examination only of the nasopharynx were independent risk factors for missed MRI diagnosis of initial RTLI (all P <0.05)., Conclusions: The missed diagnosis of initial RTLI on MRI is mainly related to lesion size and location, imaging characteristics, and MRI examination site.

Deep learning techniques have been applied to medical image segmentation and demonstrated expert-level performance. Due to the poor generalization abilities of the models in the deployment in different centres, common solutions, such as transfer learning and domain adaptation techniques, have been proposed to mitigate this issue. However, these solutions necessitate retraining the models with target domain data and annotations, which limits their deployment in clinical settings in unseen domains. We evaluated the performance of domain generalization methods on the task of MRI segmentation of nasopharyngeal carcinoma (NPC) by collecting a new dataset of 321 patients with manually annotated MRIs from two hospitals. We transformed the modalities of MRI, including T1WI, T2WI and CE-T1WI, from the spatial domain to the frequency domain using Fourier transform. To address the bottleneck of domain generalization in MRI segmentation of NPC, we propose a meta-learning approach based on frequency domain feature mixing. We evaluated the performance of MFNet against existing techniques for generalizing NPC segmentation in terms of Dice and MIoU. Our method evidently outperforms the baseline in handling the generalization of NPC segmentation. The MF-Net clearly demonstrates its effectiveness for generalizing NPC MRI segmentation to unseen domains (Dice = 67.59%, MIoU = 75.74% T1W1). MFNet enhances the model's generalization capabilities by incorporating mixed-feature meta-learning. Our approach offers a novel perspective to tackle the domain generalization problem in the field of medical imaging by effectively exploiting the unique characteristics of medical images., (© 2024 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Objective: Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) are complementary in staging of nasopharyngeal carcinoma (NPC). The combination of MRI and functional imaging from PET in PET/MR is promising in NPC management. Diagnostic performance of PET/CT and PET/MR was compared in 46 patients with histologically confirmed NPC under different disease scenarios, including primary non-metastatic cases, primary metastatic cases, recurrence and/or metastasis after treatment, and post-treatment follow-up cases., Subjects and Methods: Forty-six patients underwent both PET/CT and PET/MR in the same day. Primary tumor extension into risk-stratified anatomic structures, retropharyngeal and cervical lymph node metastasis, distant metastasis and post-treatment follow-up results, were compared., Results: For high-risk structures, PET/MR detected two more sides of tensor/levator veli palatine muscle involvement, one more case of clivus involvement, and ruled out 12 false-positive sides of prevertebral muscle involvement by PET/CT. For medium-risk structures, PET/MR detected four more sides of medial pterygoid muscle involvement. For low-risk structures, abnormal signal on massa lateralis atlantis was detected by PET/MR. PET/MR detected 14 more positive retropharyngeal lymph nodes and more liver micrometastases than PET/CT. Overall, PET/MR changed two patients' T staging., Conclusion: Positron emission tomography/MR outperforms PET/CT in delineating muscle, skull-base bone, and nodal involvement, and identifying liver micrometastases, may serve as a single-step staging modality for NPC.

Background: [18 F]-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) has the ability to detect local and/or regional recurrence as well as distant metastasis. We aimed to evaluate the prognosis value of PET/CT in locoregional recurrent nasopharyngeal (lrNPC)., Methods: A total of 451 eligible patients diagnosed with recurrent I-IVA (rI-IVA) NPC between April 2009 and December 2015 were retrospectively included in this study. The differences in overall survival (OS) of lrNPC patients with and without PET/CT were compared in the I-II, III-IVA, r0-II, and rIII-IVA cohorts, which were grouped by initial staging and recurrent staging (according to MRI)., Results: In the III-IVA and rIII-IVA NPC patients, with PET/CT exhibited significantly higher OS rates in the univariate analysis (P = 0.045; P = 0.009; respectively). Multivariate analysis revealed that with PET/CT was an independent predictor of OS in the rIII-IVA cohort (hazard ratio [HR] = 0.476; 95% confidence interval [CI]: 0.267 to 0.847; P = 0.012). In the rIII-IVA NPC, patients receiving PET/CT sacns before salvage surgery had a better prognosis compared with MRI alone (P = 0.036). The recurrent stage (based on PET/CT) was an independent predictor of OS. (r0-II versus [vs]. rIII-IVA; HR = 0.376; 95% CI: 0.150 to 0.938; P = 0.036)., Conclusion: The present study showed that with PET/CT could improve overall survival for rIII-IVA NPC patients. PET/CT appears to be an effective method for assessing rTNM staging., (© 2024. The Author(s).)

Background: To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients., Methods: The cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors., Results: The NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901-0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686-0.905)., Conclusions: A predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management., (© 2024. The Author(s).)

Nasopharyngeal carcinoma is a malignant tumor that occurs in the epithelium and mucosal glands of the nasopharynx, and its pathological type is mostly poorly differentiated squamous cell carcinoma. Since the nasopharynx is located deep in the head and neck, early diagnosis and timely treatment are critical to patient survival. However, nasopharyngeal carcinoma tumors are small in size and vary widely in shape, and it is also a challenge for experienced doctors to delineate tumor contours. In addition, due to the special location of nasopharyngeal carcinoma, complex treatments such as radiotherapy or surgical resection are often required, so accurate pathological diagnosis is also very important for the selection of treatment options. However, the current deep learning segmentation model faces the problems of inaccurate segmentation and unstable segmentation process, which are mainly limited by the accuracy of data sets, fuzzy boundaries, and complex lines. In order to solve these two challenges, this article proposes a hybrid model WET-UNet based on the UNet network as a powerful alternative for nasopharyngeal cancer image segmentation. On the one hand, wavelet transform is integrated into UNet to enhance the lesion boundary information by using low-frequency components to adjust the encoder at low frequencies and optimize the subsequent computational process of the Transformer to improve the accuracy and robustness of image segmentation. On the other hand, the attention mechanism retains the most valuable pixels in the image for us, captures the remote dependencies, and enables the network to learn more representative features to improve the recognition ability of the model. Comparative experiments show that our network structure outperforms other models for nasopharyngeal cancer image segmentation, and we demonstrate the effectiveness of adding two modules to help tumor segmentation. The total data set of this article is 5000, and the ratio of training and verification is 8:2. In the experiment, accuracy = 85.2% and precision = 84.9% can show that our proposed model has good performance in nasopharyngeal cancer image segmentation., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Extensive research has been conducted on radiolabeled fibroblast activation protein (FAP) inhibitors (FAPIs) and p-Cl-Phe-cyclo(d-Cys-Tyr-d-4-amino-Phe(carbamoyl)-Lys-Thr-Cys)d-Tyr-NH 2 (LM3) peptides for imaging of FAP and somatostatin receptor 2 (SSTR2)-positive tumors. In this study, we designed and synthesized a FAPI-LM3 heterobivalent molecule radiolabeled with 68 Ga and evaluated its effectiveness in both tumor xenografts and patients with nasopharyngeal carcinoma (NPC). Methods: The synthesis of FAPI-LM3 was based on the structures of FAPI-46 and LM3. After radiolabeling with 68 Ga, its dual-receptor-binding affinity was evaluated in vitro and in vivo. Preclinical studies, including small-animal PET and biodistribution evaluation, were conducted on HT-1080-FAP and HT-1080-SSTR2 tumor xenografts. The feasibility of 68 Ga-FAPI-LM3 PET/CT in a clinical setting was evaluated in patients with NPC, and the results were compared with those of 18 F-FDG. Results: 68 Ga-FAPI-LM3 showed high affinity for both FAP and SSTR2. The tumor uptake of 68 Ga-FAPI-LM3 was significantly higher than that of 68 Ga-FAPI-46 and 68 Ga-DOTA-LM3 in HT-1080-FAP-plus-HT-1080-SSTR2 tumor xenografts. In a clinical study involving 6 NPC patients, 68 Ga-FAPI-LM3 PET/CT showed significantly higher uptake than did 18 F-FDG in primary and metastatic lesions, leading to enhanced lesion detectability and tumor delineation. Conclusion: 68 Ga-FAPI-LM3 exhibited FAPI and SSTR2 dual-receptor-targeting properties both in vitro and in vivo, resulting in improved tumor uptake and retention compared with that observed with monomeric 68 Ga-FAPI and 68 Ga-DOTA-LM3. This study highlights the clinical feasibility of 68 Ga-FAPI-LM3 PET/CT for NPC imaging., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: The study aims to harness the value of radiomics models combining intratumoral and peritumoral features obtained from pretreatment CT to predict treatment response as well as the survival of LA-NPC(locoregionally advanced nasopharyngeal carcinoma) patients receiving multiple types of induction chemotherapies, including immunotherapy and targeted therapy., Methods: 276 LA-NPC patients (221 in the training and 55 in the testing cohort) were retrospectively enrolled. Various statistical analyses and feature selection techniques were applied to identify the most relevant radiomics features. Multiple machine learning models were trained and compared to build signatures for the intratumoral and each peritumoral region, along with a clinical signature. The performance of each model was evaluated using different metrics. Subsequently, a nomogram model was constructed by combining the best-performing radiomics and clinical models., Results: In the testing cohort, the nomogram model exhibited an AUC of 0.816, outperforming the other models. The nomogram model's calibration curve showed good agreement between predicted and observed outcomes in both the training and testing sets. When predicting survival, the model's concordance index (C-index) was 0.888 in the training cohort and 0.899 in the testing cohort, indicating its robust predictive ability., Conclusion: In conclusion, the combined nomogram model, incorporating radiomics and clinical features, outperformed other models in predicting treatment response and survival outcomes for LA-NPC patients receiving induction chemotherapies. These findings highlight the potential clinical utility of the model, suggesting its value in individualized treatment planning and decision-making., (© 2024. The Author(s).)

Objective: This study was developed to explore the prognostic relevance of radiologic extranodal extension (rENE) in lymph node-positive nasopharyngeal carcinoma (NPC) patients. Materials and methods: A retrospective review of data from 249 eligible patients with NPC was performed, with magnetic resonance imaging scans being used for rENE grading. The prognostic value of rENE was assessed through univariate and multivariate analyses. Results: Log-rank tests revealed significant differences between patients with and without rENE in terms of overall survival, progression-free survival (PFS) and distant metastasis-free survival (DMFS). G2 and G3 patients tended to exhibit worse PFS and DMFS relative to G0/G1 patients ( p < 0.05). Long-term chemotherapy cycles were associated with significant improvements in the PFS and DMFS of G2 and G3 patients. Conclusion: These results suggest that higher rENE grades (G2/G3) are independently associated with worse survival outcomes among NPC patients, with more aggressive treatment strategies potentially affording greater prognostic benefits to these individuals.

Nasopharyngeal Carcinoma (NC) refers to the malignant tumor that occurs at the top and side walls of the nasopharyngeal cavity. The NC incidence rate always dominates the first among the malignant tumors of the ear, nose and throat, and mainly occurs in Asia. NC cases are mainly concentrated in southern provinces in China, with about 4 million existing NC. With the pollution of environment and pickled diet, and the increase of life pressure, the domestic NC incidence rate has reached 4.5-6.5/100000 and is increasing year by year. It was reported that the known main causes of NC include hereditary factor, genetic mutations, and EB virus infection, common clinical symptoms of NC include nasal congestion, bloody mucus, etc. About 90% of NC is highly sensitive to radiotherapy which is regard as the preferred treatment method; However, for NC with lower differentiation, larger volume, and recurrence after treatment, surgical resection and local protons and heavy ions therapy are also indispensable means. According to reports, the subtle heterogeneity and diversity exists in some NC, with about 80% of NC undergone radiotherapy and about 25% experienced recurrence and death within five years after radiotherapy in China. Therefore, screening the NC population with suspected recurrence after concurrent chemoradiotherapy may improve survival rates in current clinical decision-making., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: To investigate the diagnostic value of conventional white light endoscopy (WLE), narrow band imaging (NBI) endoscopy, and Lugol's iodine staining under WLE (endoscopic iodine staining) in the screening and early diagnosis of nasopharyngeal carcinoma., Methods: Patients with nasopharyngeal lesions requiring biopsy attending the Department of Otolaryngology Head and Neck Surgery in our hospital between January 2021 and April 2023 were included in this study. Before biopsy, all subjects underwent conventional WLE, NBI endoscopy, and endoscopic iodine staining. On WLE, according to nasopharyngeal lesion morphology and color, patients were diagnosed with nasopharyngeal carcinoma ( +) or chronic hyperplastic nasopharyngitis (-). On NBI endoscopy, according to nasopharyngeal lesion vascular morphology, patients with type V manifestations (nasopharyngeal carcinoma) were categorized as NBI ( +) and patients with type I-IV manifestations (chronic hyperplastic nasopharyngitis) were categorized as NBI (-). Endoscopic iodine staining (1.6% Lugol's iodine solution) was positive ( +) if the mucosal surface was brown with no white patches, or negative (-) if there was no or light brown staining of the mucosal surface. Patients were divided into 2 groups based on histopathological diagnosis: nasopharyngeal carcinoma or chronic hyperplastic nasopharyngitis. Endoscopic diagnoses were compared with histopathological findings. The diagnostic performance of WLE, NBI endoscopy and endoscopic iodine staining for nasopharyngeal carcinoma were determined., Results: This study included 159 patients. On histopathology, 29 patients were diagnosed with nasopharyngeal carcinoma, and 130 patients were diagnosed with chronic hyperplastic nasopharyngitis. There were no significant differences in the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC) of conventional WLE, NBI endoscopy or endoscopic iodine staining for differentiating nasopharyngeal carcinoma and chronic hyperplastic nasopharyngitis. The diagnostic performance of the combination of conventional WLE, NBI endoscopy and endoscopic iodine staining was significantly improved compared to any procedure alone., Conclusions: Conventional WLE, NBI endoscopy or endoscopic iodine staining had good diagnostic performance for differentiating nasopharyngeal carcinoma and chronic hyperplastic nasopharyngitis. In particular, NBI endoscopy and endoscopic iodine staining alone or combined had clinical utility for identifying patients with nasopharyngeal lesions that are eligible for a watch-and-wait strategy., (© 2023. The Author(s).)

Background: Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality metrics (IQMs). To evaluate the similarity between synthetic CT (sCT) and real CT (rCT) comprehensively, we comprehensively evaluated IQMs and radiomic features for the first time., Methods: This study enrolled 127 patients with nasopharyngeal carcinoma who underwent CT and MRI scans. Supervised-learning (Unet) and unsupervised-learning (CycleGAN) methods were applied to build MRI-to-CT synthesis models. The regions of interest (ROIs) included nasopharynx gross tumor volume (GTVnx), brainstem, parotid glands, and temporal lobes. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), root mean square error (RMSE), and structural similarity (SSIM) were used to evaluate image quality. Additionally, 837 radiomic features were extracted for each ROI, and the correlation was evaluated using the concordance correlation coefficient (CCC)., Results: The MAE, RMSE, SSIM, and PSNR of the body were 91.99, 187.12, 0.97, and 51.15 for Unet and 108.30, 211.63, 0.96, and 49.84 for CycleGAN. For the metrics, Unet was superior to CycleGAN (P < 0.05). For the radiomic features, the percentage of four levels (i.e., excellent, good, moderate, and poor, respectively) were as follows: GTVnx, 8.5%, 14.6%, 26.5%, and 50.4% for Unet and 12.3%, 25%, 38.4%, and 24.4% for CycleGAN; other ROIs, 5.44% ± 3.27%, 5.56% ± 2.92%, 21.38% ± 6.91%, and 67.58% ± 8.96% for Unet and 5.16% ± 1.69%, 3.5% ± 1.52%, 12.68% ± 7.51%, and 78.62% ± 8.57% for CycleGAN., Conclusions: Unet-sCT was superior to CycleGAN-sCT for the IQMs. However, neither exhibited absolute superiority in radiomic features, and both were far less similar to rCT. Therefore, further work is required to improve the radiomic similarity for MRI-to-CT synthesis., Trial Registration: This study was a retrospective study, so it was free from registration., (© 2023. The Author(s).)

To explore the prognostic significance of PET/CT-based radiomics signatures and clinical features for local recurrence-free survival (LRFS) in nasopharyngeal carcinoma (NPC). We retrospectively reviewed 726 patients who underwent pretreatment PET/CT at our center. Least absolute shrinkage and selection operator (LASSO) regression and the Cox proportional hazards model were applied to construct Rad-score, which represented the radiomics features of PET-CT images. Univariate and multivariate analyses were used to establish a nomogram model. The concordance index (C-index) and calibration curve were used to evaluate the predictive accuracy and discriminative ability. Receiver operating characteristic analysis was performed to stratify the local recurrence risk of patients. The nomogram was validated by evaluating its discrimination ability and calibration in the validation cohort. A total of eight features were selected to construct Rad-score. A radiomics-clinical nomogram was built after the selection of univariate and multivariable Cox regression analyses, including the Rad-score and maximum standardized uptake value (SUVmax). The C-index was 0.71 (0.67-0.74) in the training cohort and 0.70 (0.64-0.76) in the validation cohort. The nomogram also performed far better than the 8th T-staging system with an area under the receiver operating characteristic curve (AUC) of 0.75 vs. 0.60 for 2 years and 0.71 vs. 0.60 for 3 years. The calibration curves show that the nomogram indicated accurate predictions. Decision curve analysis (DCA) revealed significantly better net benefits with this nomogram model. The log-rank test results revealed a distinct difference in prognosis between the two risk groups. The PET/CT-based radiomics nomogram showed good performance in predicting LRFS and showed potential to identify patients at high-risk of developing NPC., (© 2023. Springer Nature Limited.)

Background: Dose adjuvant chemotherapy (AC) should be offered in nasopharyngeal carcinoma (NPC) patients? Different guidelines provided the different recommendations., Methods: In this retrospective study, a total of 140 patients were enrolled and followed for 3 years, with 24 clinical features being collected. The imaging features on the enhanced-MRI sequence were extracted by using PyRadiomics platform. The pearson correlation coefficient and the random forest was used to filter the features associated with recurrence or metastasis. A clinical-radiomics model (CRM) was constructed by the Cox multivariable analysis in training cohort, and was validated in validation cohort. All patients were divided into high- and low-risk groups through the median Rad-score of the model. The Kaplan-Meier survival curves were used to compare the 3-year recurrence or metastasis free rate (RMFR) of patients with or without AC in high- and low-groups., Results: In total, 960 imaging features were extracted. A CRM was constructed from nine features (seven imaging features and two clinical factors). In the training cohort, the area under curve (AUC) of CRM for 3-year RMFR was 0.872 (P <0.001), and the sensitivity and specificity were 0.935 and 0.672, respectively; In the validation cohort, the AUC was 0.864 (P <0.001), and the sensitivity and specificity were 1.00 and 0.75, respectively. Kaplan-Meier curve showed that the 3-year RMFR and 3-year cancer specific survival (CSS) rate in the high-risk group were significantly lower than those in the low-risk group (P <0.001). In the high-risk group, patients who received AC had greater 3-year RMFR than those who did not receive AC (78.6% vs. 48.1%) (p = 0.03)., Conclusion: Considering increasing RMFR, a prediction model for NPC based on two clinical factors and seven imaging features suggested the AC needs to be added to patients in the high-risk group and not in the low-risk group., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The three-dimensional chemical exchange saturation transfer (3D CEST) technique is a novel and promising magnetic resonance sequence; however, its application in nasopharyngeal carcinoma (NPC) lacks sufficient evaluation. This study aimed to assess the feasibility of the 3D CEST technique in predicting the short-term treatment outcomes for chemoradiotherapy (CRT) in NPC patients., Methods: Forty NPC patients and fourteen healthy volunteers were enrolled and underwent the pre-treatment 3D CEST magnetic resonance imaging and diffusion-weighted imaging (DWI). The reliability of 3D CEST was assessed in healthy volunteers by calculating the intra- and inter-observer correlation coefficient (ICC) for amide proton transfer weighted-signal intensity (APTw-SI) and magnetization transfer ratio (MTR) values. NPC patients were divided into residual and non-residual groups based on short-term treatment outcomes after CRT. Whole-tumor regions of interest (ROIs) were manually drawn to measure APTw-SI, MTR and apparent diffusion coefficient (ADC) values. Multivariate analysis and the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of clinical characteristics, APTw-SI, MTR, ADC values, and combined models in predicting short-term treatment outcomes in NPC patients., Results: For the healthy volunteer group, all APTw-SI and MTR values exhibited good to excellent intra- and inter-observer agreements (0.736-0.910, 0.895-0.981, all P > 0.05). For NPC patients, MTR values showed a significant difference between the non-residual and residual groups (31.24 ± 5.21% vs. 34.74 ± 1.54%, P = 0.003) while no significant differences were observed for APTw-SI and ADC values (P > 0.05). Moreover, the diagnostic power of MTR value was superior to APTw-SI (AUC: 0.818 vs. 0.521, P = 0.017) and comparable to ADC values (AUC: 0.818 vs. 0.649, P > 0.05) in predicting short-term treatment outcomes for NPC patients. The prediction performance did not improve even when combining MTR values with APTw-SI and/or ADC values (P > 0.05)., Conclusions: The pre-treatment MTR value acquired through 3D CEST demonstrated superior predictive performance for short-term treatment outcomes compared to APTw-SI and ADC values in NPC patients after CRT., (© 2023. International Cancer Imaging Society (ICIS).)

Background: The use of the apparent transverse relaxation rate (R2 * ) in nasopharyngeal carcinoma (NPC) has not been previously reported in the literature. The aim of this study was to investigate the role of the R2 * value in evaluating response to concurrent chemoradiotherapy (CCRT) in patients with NPC., Methods: Forty-one patients with locoregionally advanced NPC confirmed by pathology were examined by blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) before and after CCRT, and conventional MRI was performed 3 months after the completion of CCRT. All patients were divided into a responding group (RG) and a nonresponding group (NRG), according to MRI findings 3 months after the end of treatment. The R2 * values before (R2* preT ) and after (R2 * postT ) CCRT and the ΔR2 * (ΔR2 * =R2 * postT - R2 * preT ) were calculated in the tumor., Results: Among the 41 patients, 26 were in the RG and 15 were in the NRG. There was no statistical difference in the R2 * preT between RG and NRG (P = 0.307); however, there were significant differences in R2 * postT and ΔR2 * (P < 0.001). The area under the curve of R2 * postT and ΔR2 * for predicting the therapeutic response of NPC was 0.897 and 0.954, respectively, with cutoff values of 40.95 and 5.50 Hz, respectively., Conclusion: The R2 * value can be used as a potential imaging indicator to evaluate the therapeutic response of locoregionally advanced NPC., (© 2023. The Author(s).)

Background: To establish and validate radiomic models combining intratumoral (Intra) and peritumoral (Peri) features obtained from pretreatment MRI for the prediction of treatment response of lymph node metastasis from nasopharyngeal cancer (NPC)., Methods: One hundred forty-five NPC patients (102 in the training and 43 in the validation set) were retrospectively enrolled. Radiomic features were extracted from Intra and Peri regions on the metastatic cervical lymph node, and selected with the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was applied to build radiomic models. Sensitivity, specificity, accuracy, and the area under the curve (AUC) of receiver operating characteristics were employed to evaluate the predictive power of each model., Results: The AUCs of the radiomic model of Intra, Peri, Intra + Peri, and Clinical-radiomic were 0.910, 0.887, 0.934, and 0.941, respectively, in the training set and 0.737, 0.794, 0.774, and 0.783, respectively, in the validation set. There were no significant differences in prediction performance among the radiomic models in the training and validation sets (all P > 0.05). The calibration curve of the radiomic model of Peri demonstrated good agreement between prediction and observation in the training and validation sets., Conclusions: The pretreatment MRI-based radiomics model may be useful in predicting the treatment response of metastatic lymph nodes of NPC. Besides, the generalization ability of the radiomic model of Peri was better than that of Intra and Intra + Peri., (© 2023. The Author(s).)

Positron emission tomography/computed tomography (PET/CT) with 18 F-fluorodeoxyglucose (FDG) is widely used for management of nasopharyngeal carcinoma (NPC). Combining the radiomic features of pre- and post-treatment FDG PET images may improve tumor characterization and prognostic predication. We investigated prognostic value of radiomic features from pre- and post-radiotherapy FDG PET images in patients with NPC. Quantitative radiomic features of primary tumors were extracted from the FDG PET images of 145 NPC patients and the delta values were also calculated. The study population was divided randomly into two groups, the training and test sets (7:3). A random survival forest (RSF) model was adopted to perform analyses of progression-free survival (PFS) and overall survival (OS). There were 37 (25.5%) cases of recurrence and 16 (11.0%) cases of death during a median follow-up period of 54.5 months. Both RSF models with clinical variables and radiomic PET features for PFS and OS showed comparable predictive performance to RSF models with clinical variables and conventional PET parameters. Tumoral radiomic features of pre- and post-treatment FDG PET and the corresponding delta values may predict PFS and OS in patients with NPC., (© 2023. The Author(s).)

Background: In this study, we propose the deep learning model-based framework to automatically delineate nasopharynx gross tumor volume (GTVnx) in MRI images., Methods: MRI images from 200 patients were collected for training-validation and testing set. Three popular deep learning models (FCN, U-Net, Deeplabv3) are proposed to automatically delineate GTVnx. FCN was the first and simplest fully convolutional model. U-Net was proposed specifically for medical image segmentation. In Deeplabv3, the proposed Atrous Spatial Pyramid Pooling (ASPP) block, and fully connected Conditional Random Field(CRF) may improve the detection of the small scattered distributed tumor parts due to its different scale of spatial pyramid layers. The three models are compared under same fair criteria, except the learning rate set for the U-Net. Two widely applied evaluation standards, mIoU and mPA, are employed for the detection result evaluation., Results: The extensive experiments show that the results of FCN and Deeplabv3 are promising as the benchmark of automatic nasopharyngeal cancer detection. Deeplabv3 performs best with the detection of mIoU 0.8529 ± 0.0017 and mPA 0.9103 ± 0.0039. FCN performs slightly worse in term of detection accuracy. However, both consume similar GPU memory and training time. U-Net performs obviously worst in both detection accuracy and memory consumption. Thus U-Net is not suggested for automatic GTVnx delineation., Conclusions: The proposed framework for automatic target delineation of GTVnx in nasopharynx bring us the desirable and promising results, which could not only be labor-saving, but also make the contour evaluation more objective. This preliminary results provide us with clear directions for further study., (© 2023. The Author(s).)

Background: To research the pathological and clinical staging uses of arterial spin labeling (ASL) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Materials and Methods: 64 newly diagnosed nasopharyngeal carcinoma (NPC) patients were enrolled from December 2020 to January 2022, and 3.0 T MRI (Discovery 750W, GE Healthcare, USA) were used for ASL and DCE-MRI scans. The DCE-MRI and ASL raw data were processed post-acquisition on the GE image processing workstation (GE Healthcare, ADW 4.7, USA). The volume transfer constant (Ktrans), blood flow (BF), and accompanying pseudo-color images were generated automatically. Draw the region of interest (ROIs), and the Ktrans and BF values for each ROI were recorded separately. Based on pathological information and the most recent AJCC staging criteria, patients were divided into low T stage groups = T 1-2 and high T stage groups = T 3-4 , low N stage groups = N 0-1 and high N stage groups = N 2-3 , and low AJCC stage group = stage I-II and high AJCC stage group = stage III-IV. The association between the Ktrans t and BF parameters and the T, N, and AJCC stages was compared using an independent sample t-test. Using a receiver operating characteristic (ROC) curve, the sensitivity, specificity, and AUC of Ktrans t , BF t , and their combined use in T and AJCC staging of NPC were investigated and assessed., Result: The tumor-BF (BF t ) (t = - 4.905, P < 0.001) and tumor-Ktrans (Ktrans t ) (t = - 3.113, P = 0.003) in the high T stage group were significantly higher than those in the low T stage group. The Ktrans t in the high N stage group was significantly higher than that in the low N stage group (t = - 2.071, P = 0.042). The BF t (t = - 3.949, P < 0.001) and Ktrans t (t = - 4.467, P < 0.001) in the high AJCC stage group were significantly higher than those in the low AJCC stage group. BF t was moderately positively correlated with the T stage (r = 0.529, P < 0.001) and AJCC stage (r = 0.445, P < 0.001). Ktrans t was moderately positively correlated with T staging (r = 0.368), N staging (r = 0.254), and AJCC staging (r = 0.411). There was also a positive correlation between BF and Ktrans in gross tumor volume (GTV) (r = 0.540, P < 0.001), parotid (r = 0.323, P < 0.009) and lateral pterygoid muscle (r = 0.445, P < 0.001). The sensitivity of the combined application of Ktrans t and BF t for AJCC staging increased from 76.5 and 78.4 to 86.3%, and the AUC value increased from 0.795 and 0.819 to 0.843, respectively., Conclusion: Combining Ktrans and BF measures may make it possible to identify the clinical stages in NPC patients., (© 2023. The Author(s).)

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).)

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/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.)

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.)

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.

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.

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).)

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.)