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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.)

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

Purpose: The aim of this study is to determine intratumoral habitat regions from multi-sequences magnetic resonance imaging (MRI) and to assess the value of those regions for prediction of patient response to neoadjuvant chemotherapy (NAC) in nasopharyngeal carcinoma (NPC)., Materials and Methods: Two hundred and ninety seven patients with NPC were enrolled. Multi-sequences MRI data were used to outline three-dimensional volumes of interest (VOI) of the whole tumor. The original imaging data were divided into two groups, which were resampled to an isotropic resolution of 1 × 1 × 1 mm 3 (group&#95;1mm) and 3 × 3 × 3 mm 3 (group&#95;3mm). Nineteen radiomics features were computed for each voxel of three sequences in group&#95;3mm, within the tumor region to extract local information. Then, k-means clustering was implemented to segment the whole tumor regions in two groups. After radiomics features were extracted and dimension reduction, habitat models were built using Multi-Layer Perceptron (MLP) algorithm., Results: Only T stage was included as the clinical model. The habitat 3mm model, which included 10 radiomics features, achieved AUCs of 0.752 and 0.724 in the training and validation cohorts, respectively. Given the slightly better outcome of habitat 3mm model, nomogram was developed in combination with habitat 3mm model and T stage with the AUC of 0.749 and 0.738 in the training and validation cohorts. The decision curve analysis provides further evidence of the nomogram's clinical practicality., Conclusions: A nomogram based on intratumoral habitat predicts the efficacy of NAC in NPC patients, offering the potential to improve both the treatment plan and patient outcomes., Competing Interests: Declarations. Conflict of interest: The authors have no relevant financial or non-financial interests to disclose. Ethical approval: This retrospective chart review study involving human participants was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The ethics committee of the Fujian Cancer Hospital approved this study. Informed consent: The need for informed consent was waived due to its retrospective status., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Purpose: Somatostatin Receptor 2 (SSTR2)-targeted radiopharmaceutical [ 68 Ga]Ga-DOTATATE has potential advantages in the diagnosis of nasopharyngeal carcinoma (NPC). This study introduces a novel long-lasting SSTR2 analogue, LNC1010, based on DOTATATE, a truncated Evans blue-binding moiety, and a polyethylene-glycol linker. We hypothesised that peptide receptor radionuclide therapy (PRRT) is more effective with [ 177 Lu]Lu-LNC1010 than with [ 177 Lu]Lu-DOTATATE in treating metastatic NPC., Methods: We assessed binding characteristics of LNC1010 in vitro using C666-1 NPC cells and in-vivo pharmacokinetics of [ 68 Ga]Ga/[ 177 Lu]Lu-LNC1010 in C666-1 NPC xenografts via PET and SPECT imaging, biodistribution studies, and PRRT, and compared them with [ 68 Ga]Ga/[ 177 Lu] Lu-labelled DOTATATE. Furthermore, a proof-of-concept approach for imaging and therapy was conducted in a patient with metastatic NPC., Results: LNC1010 exhibited strong uptake and specific affinity for SSTR2 in C666-1 NPC cells. PET and SPECT imaging demonstrated higher uptake and longer tumour retention of [ 68 Ga]Ga/[ 177 Lu]Lu-LNC1010 than [ 68 Ga]Ga/[ 177 Lu]Lu-DOTATATE in C666-1 NPC xenografts, indicating its suitability for PRRT applications in NPCs. Biodistribution studies confirmed the higher uptake and prolonged retention of [ 177 Lu]Lu-LNC1010 than [ 177 Lu]Lu-DOTATATE. In preclinical PRRT studies, [ 177 Lu]Lu-LNC1010 showed greater inhibition of tumour growth in C666-1 NPC xenografts than [ 177 Lu]Lu-DOTATATE. In a subsequent pilot clinical study, PRRT with [ 177 Lu]Lu-LNC1010 achieved favourable therapeutic and negligible side effects in a patient with metastatic NPC., Conclusion: [ 177 Lu]Lu-LNC1010 demonstrated increased tumour uptake and prolonged retention in SSTR2-positive NPCs, with superior anti-tumour efficacy to that of [ 177 Lu]Lu-DOTATATE in preclinical studies. These findings suggest that PRRT with [ 177 Lu]Lu-LNC1010 is a promising treatment for advanced NPC, extending the clinical scope of PRRT beyond neuroendocrine tumours., Competing Interests: Declarations. Ethical approval: All procedures involving human participants were carried out in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any experiments with animals. Consent to participate: Informed consent was obtained from all individual participants included in the study. Conflict of interest: X Chen is co-founder of the Yantai LNC Biotechnology. The other authors declare that they have no potential conflicts of interest., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To investigate the potential of virtual contrast-enhanced magnetic resonance imaging (VCE-MRI) for gross-tumor-volume (GTV) delineation of nasopharyngeal carcinoma (NPC) using multi-institutional data., Methods and Materials: This study retrospectively retrieved T1-weighted (T1w), T2-weighted (T2w) MRI, gadolinium-based contrast-enhanced MRI (CE-MRI), and planning computed tomography (CT) of 348 biopsy-proven NPC patients from 3 oncology centers. A multimodality-guided synergistic neural network (MMgSN-Net) was trained using 288 patients to leverage complementary features in T1w and T2w MRI for VCE-MRI synthesis, which was independently evaluated using 60 patients. Three board-certified radiation oncologists and 2 medical physicists participated in clinical evaluations in 3 aspects: image quality assessment of the synthetic VCE-MRI, VCE-MRI in assisting target volume delineation, and effectiveness of VCE-MRI-based contours in treatment planning. The image quality assessment includes distinguishability between VCE-MRI and CE-MRI, clarity of tumor-to-normal tissue interface, and veracity of contrast enhancement in tumor invasion risk areas. Primary tumor delineation and treatment planning were manually performed by radiation oncologists and medical physicists, respectively., Results: The mean accuracy to distinguish VCE-MRI from CE-MRI was 31.67%; no significant difference was observed in the clarity of tumor-to-normal tissue interface between VCE-MRI and CE-MRI; for the veracity of contrast enhancement in tumor invasion risk areas, an accuracy of 85.8% was obtained. The image quality assessment results suggest that the image quality of VCE-MRI is highly similar to real CE-MRI. The mean dosimetric difference of planning target volumes was less than 1 Gy., Conclusions: The VCE-MRI is highly promising to replace the use of gadolinium-based CE-MRI in tumor delineation of NPC patients., (Copyright © 2024 Elsevier Inc. 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).)

Background: Patients may undergo anatomical changes during radiotherapy, leading to an underdosing of the target or overdosing of the organs at risk (OARs)., Purpose: This study developed a deep-learning method to predict the tumor response of patients with nasopharyngeal carcinoma (NPC) during treatment. This method can predict the anatomical changes of a patient., Methods: The participants included 230 patients with NPC. The data included planning computed tomography (pCT) and routine cone-beam CT (CBCT) images. The CBCT image quality was improved to the CT level using an advanced method. A long short-term memory network-generative adversarial network (LSTM-GAN) is proposed, which can harness the forecasting ability of LSTM and the generation ability of GAN. Four models were trained to predict the anatomical changes that occurred in weeks 3-6 and named LSTM-GAN-week 3 to LSTM-GAN-week 6. The pCT and CBCT were used as input, and the tumor target volumes (TVs) and OARs were delineated on the predicted and real images (ground truth). Finally, the models were evaluated using contours and dosimetry parameters., Results: The proposed method predicted the anatomical changes, with a dice similarity coefficient above 0.94 and 0.90 for the TVs and surrounding OARs, respectively. The dosimetry parameters were close between the prediction and ground truth. The deviations in the prescription, minimum, and maximum doses of the tumor targets were below 0.5 Gy. For serial organs (brain stem and spinal cord), the deviations in the maximum dose were below 0.6 Gy. For parallel organs (bilateral parotid glands), the deviations in the mean dose were below 0.8 Gy., Conclusion: The proposed method can predict the tumor response to radiotherapy in the future such that adaptation can be scheduled on time. This study provides a proactive mechanism for planning adaptation, which can enable personalized treatment and save clinical time by anticipating and preparing for treatment strategy adjustments., (© 2024 American Association of Physicists in Medicine.)

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.

Objective: The early response to concurrent chemoradiotherapy in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) is closely correlated with prognosis. In this study, we aimed to predict early response using a combined model that combines sub-regional radiomics features from multi-sequence MRI with clinically relevant factors., Methods: A total of 104 patients with LA-NPC were randomly divided into training and test cohorts at a ratio of 3:1. Radiomic features were extracted from subregions within the tumor area using the K-means clustering method, and feature selection was performed using LASSO regression. Four models were established: a radiomics model, a clinical model, an Intratumor Heterogeneity (ITH) score-based model and a combined model that integrates the ITH score with clinical factors. The predictive performance of these models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA)., Results: Among the models, the combined model incorporating the ITH score and clinical factors exhibited the highest predictive performance in the test cohort (AUC=0.838). Additionally, the models based on ITH score showed superior prognostic value in both the training cohort (AUC=0.888) and the test cohort (AUC=0.833)., Conclusion: The combined model that integrates the ITH score with clinical factors exhibited superior performance in predicting early response following concurrent chemoradiotherapy in patients with LA-NPC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.)

Purpose: To establish and validate a delta-radiomics-based model for predicting progression-free survival (PFS) in patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) following induction chemotherapy (IC)., Methods and Materials: A total of 250 LA-NPC patients (training cohort: n = 145; validation cohort: n = 105) were enrolled. Radiomic features were extracted from MRI scans taken before and after IC, and changes in these features were calculated. Following feature selection, a delta-radiomics signature was constructed using LASSO-Cox regression analysis. A prognostic nomogram incorporating independent clinical indicators and the delta-radiomics signature was developed and assessed for calibration and discrimination. Risk stratification by the nomogram was evaluated using Kaplan-Meier methods., Results: The delta-radiomics signature, consisting of 12 features, was independently associated with prognosis. The nomogram, integrating the delta-radiomics signature and clinical factors demonstrated excellent calibration and discrimination. The model achieved a Harrell's concordance index (C-index) of 0.848 in the training cohort and 0.820 in the validation cohort. Risk stratification identified two groups with significantly different PFS rates. The three-year PFS for high-risk patients who received concurrent chemoradiotherapy (CCRT) or radiotherapy plus adjuvant chemotherapy (RT+AC) after IC was significantly higher than for those who received RT alone, reaching statistical significance. In contrast, for low-risk patients, the three-year PFS after IC was slightly higher for those who received CCRT or RT+AC compared to those who received RT alone; however, this difference did not reach statistical significance., Conclusions: Our delta MRI-based radiomics model could be useful for predicting PFS and may guide subsequent treatment decisions after IC in LA-NPC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objectives: This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC)., Materials and Methods: A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods., Results: The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients., Conclusion: The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT., Clinical Relevance Statement: The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment., Key Points: • The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

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: Three dimensional pulsed continuous arterial spin labeling (3D-pCASL) and incoherent movement within voxels (IVIM) imaging was combined to assess dynamic microscopic structure changes of the hippocampus and temporal lobe white matter (TLWM) of nasopharyngeal carcinoma (NPC) patients post intensity-modulated radiation therapy (IMRT)., Methods: Forty-six patients who were first diagnosed with NPC and underwent IMRT were prospectively enrolled. 3D-CASL and IVIM were performed pre-RT, within 1 week (1 W) post-RT, 3 months (3 M) post-RT, 6 months (6 M) post-RT, and 18 months (18 M) post-RT. Twenty-seven patients completed follow-ups for all time periods, and their data were analyzed. The cerebral flow (CBF) derived from ASL, and apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (F) derived from IVIM of hippocampus and TLWM were analyzed. The quantitative parameters were measured before RT as the baseline, and the corresponding parameter values and change rates at each time point post-RT were compared using the non-parametric Wilcoxon rank sum test., Results: At 1 W post-RT, CBF showed a significant increase and peaked in both the hippocampus and TLWM (p < 0.05) with change rate of 30.3 % and 24.1 %. In the hippocampus, both D and D* were significantly increased from pre-RT to 6 M post-RT with change rate of 6.66 % and 34.7 %, while D*-values remained significantly higher than pre-RT at 12 months post-RT with change rate of 41.2 %. In the TLWM, the F firstly increased and then decreased, and was significantly decreased from pre-RT to 6 M post-RT with change rate of 20.2 %., Conclusion: 3D-PCASL and IVIM can indirectly reflecting the developmental pattern and molecular mechanism of RT induced brain injury., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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.)

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).)

Purpose: Sarcopenia, characterized by loss of muscle mass index (SMI), serves as a diagnostic indicator for malnutrition and has been shown to influence cancer treatment outcomes. The objective of this study was to investigate the prognostic significance of sarcopenia on the locally advanced nasopharyngeal carcinoma (laNPC) patients., Patients and Methods: 545 patients with stage III-IVa NPC were included in this retrospective study. Sarcopenia was defined using the skeletal muscle index (SMI) determined at the C3 level based on baseline MRI. The log-rank test and the Cox proportional hazards model were used to compare overall survival (OS) and progression-free survival (PFS)., Results: The results of the multivariate analysis revealed that sarcopenia group (HR = 2.82, 95% CI 1.96-4.06, P < 0.01), T4 stage (HR = 1.64, 95% CI 1.24-2.15, P < 0.01), N3 stage (HR = 1.91, 95% CI 1.52-2.40, P < 0.01), comorbidities (HR = 2.08, 95% CI 1.45-2.97, P < 0.01), and any adverse event grade 3-4 (HR = 1.48, 95% CI 1.04-2.01, P = 0.03) were identified as independent risk factors that significantly impacted the OS. Additionally, sarcopenia group (HR = 2.40, 95% CI 1.73-3.33, P < 0.01), T4 stage (HR = 1.50, 95% CI 1.17-1.92, P < 0.01), N3 stage (HR = 1.80, 95% CI 1.46-2.22, P < 0.01), sarcopenia group (HR = 2.40, 95% CI 1.73-3.33, P < 0.01), and any adverse event grade 3-4 (HR = 1.45, 95% CI 1.04-2.01, P = 0.03) were found to have a significant impact on PFS., Conclusion: Sarcopenia was identified as a prognostic factor for patients with laNPC. Furthermore, T stage, N stage, comorbidities, and any adverse event grade 3-4 were identified as independent prognostic factors for laNPC., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Background and Purpose: To develop and validate a prognostic nomogram based on pretreatment 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (PET-CT)radiomics parameters and peripheral blood markers for risk stratification in patients with de novo metastatic nasopharyngeal carcinoma (dmNPC)., Materials and Methods: A total of 558 patients with dmNPC were retrospectively enrolled between 2011 and 2019. Eligible patients were randomly divided into training and validation cohorts (7:3 ratio). A Cox regression model was used to identify prognostic factors for overall survival (OS). The predictive accuracy and discriminative ability of the prognostic nomogram were determined using the concordance index (C-index) and calibration curve., Results: Independent factors derived from multivariable analysis of the training cohort to predict death were lactate dehydrogenase levels, pretreatment Epstein-Barr virus DNA, total lesion glycolysis of locoregional lesions, number of metastatic lesions, and age, all of which were assembled into a nomogram with (nomogram B) or without PET-CT parameters (nomogram A). The C-index of nomogram B for predicting death was 0.70, which was significantly higher than the C-index values for nomogram A. Patients were then stratified into low- and high-risk groups based on the scores calculated using nomogram B for OS. The median OS was significantly higher in the low-risk group than in the high-risk group (69.60 months [95 % CI: 58.50-108.66] vs. 21.40 months [95 % CI: 19.20-23.90]; p＜0.01). All the results were confirmed in the validation cohort., Conclusion: The proposed nomogram including PET-CT parameters yielded accurate prognostic predictions for patients with dmNPC, enabling effective risk stratification for these patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: We aimed to compare the staging efficiency of [ 68 Ga]Ga-DOTATATE and [ 68 Ga]Ga-FAPI PET/CT in nasopharyngeal carcinoma (NPC) patients., Methods: Thirty-nine patients with pathologically confirmed NPC were enrolled in this prospective study. Each patient underwent paired [ 68 Ga]Ga-DOTATATE and [ 68 Ga]Ga-FAPI PET/CT on 2 successive days. The accuracy of two PET/CT for assessing T, N, and M stages was compared by using head-and-neck MRI, histopathologic diagnosis and follow-up results as reference standards. The radiotracer uptake derived from two PETs was also compared., Results: For treatment-naïve patients, [ 68 Ga]Ga-DOTATATE PET/CT showed identical sensitivity for the primary tumours but clearer tumor delineation induced by higher tumour-to-background (TBR) ratio (19.1 ± 8.7 vs. 12.4 ± 7.7, P = 0.003), compared with [ 68 Ga]Ga-FAPI PET/CT. Regarding cervical lymph node (CLN) metastases, [ 68 Ga]Ga-DOTATATE PET had significantly better sensitivity and accuracy based on neck sides (98% vs. 82%, P < 0.001; 99% vs. 88% P = 0.008), neck levels (98% vs. 78%, 99% vs. 97%; both P < 0.001) and individual nodes (89% vs. 56%, 91% vs. 76%; both P < 0.001), and higher TBR (8.1 ± 4.1 vs. 6.3 ± 3.7, P < 0.001). Additionally, [ 68 Ga]Ga-DOTATATE PET/CT revealed higher sensitivity and accuracy for distant metastases (96% vs. 53%, 95% vs. 52%; both P < 0.001), particularly in bone metastases (99% vs. 49%, 97% vs. 49%; both P < 0.001). For post-treatment patients, [ 68 Ga]Ga-DOTATATE PET/CT identified one more true-negative case than [ 68 Ga]Ga-FAPI PET/CT., Conclusion: [ 68 Ga]Ga-DOTATATE PET/CT performed better than [ 68 Ga]Ga-FAPI PET/CT in visualizing the primary tumours, detecting the metastatic lesions and identifying the local recurrence, suggesting [ 68 Ga]Ga-DOTATATE PET/CT may be superior to [ 68 Ga]Ga-FAPI PET/CT for NPC staging., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Nasopharyngeal carcinoma (NPC) is a malignant tumor primarily treated by radiotherapy. Accurate delineation of the target tumor is essential for improving the effectiveness of radiotherapy. However, the segmentation performance of current models is unsatisfactory due to poor boundaries, large-scale tumor volume variation, and the labor-intensive nature of manual delineation for radiotherapy. In this paper, MMCA-Net, a novel segmentation network for NPC using PET/CT images that incorporates an innovative multimodal cross attention transformer (MCA-Transformer) and a modified U-Net architecture, is introduced to enhance modal fusion by leveraging cross-attention mechanisms between CT and PET data. Our method, tested against ten algorithms via fivefold cross-validation on samples from Sun Yat-sen University Cancer Center and the public HECKTOR dataset, consistently topped all four evaluation metrics with average Dice similarity coefficients of 0.815 and 0.7944, respectively. Furthermore, ablation experiments were conducted to demonstrate the superiority of our method over multiple baseline and variant techniques. The proposed method has promising potential for application in other tasks.

Background: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT)., Methods: Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram., Results: A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility., Conclusions: The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC., (© 2024 The Author(s). Head & Neck published by Wiley Periodicals LLC.)

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.)

Abstract: A 53-year-old man with newly diagnosed nasopharyngeal carcinoma (NPC) underwent 99m Tc-MDP bone scintigraphy for the potential bone metastases, and paired 68 Ga-DOTATATE and 68 Ga-FAPI PET/CT for initial staging. 68 Ga-DOTATATE PET/CT identified 2 abnormal foci with increased tracer uptake in the cervical vertebra and the ilium, whereas 68 Ga-FAPI PET/CT and bone scan detected only the ilium lesion. A subsequent biopsy confirmed NPC metastasis in the ilium. Furthermore, baseline and follow-up bone scintigraphy revealed that the positive lesion in the cervical vertebra, as indicated in 68 Ga-DOTATATE PET/CT, was also a bone metastasis. This case highlighted the potential superiority of 68 Ga-DOTATATE in NPC., Competing Interests: Conflicts of interest and sources of funding: The authors declare that they have no conflicts of interest. This study was funded by the Natural Science Foundation of Fujian Province (No. 2023J01564)., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Abstract: We report a case of recurrent nasopharyngeal carcinoma postnasopharyngectomy, presenting with headaches. MRI revealed abnormal signals of the clivus with enhancement, and FDG PET/CT indicated intense uptake in the nasopharynx, clivus, and left neck lymph nodes. Bone SPECT/CT showed bony erosion and uptake in bilateral skull base areas. Biopsy confirmed aspergillosis. Despite the challenges in distinguishing tumor invasion from Aspergillus infection on MRI, bone SPECT/CT, and FDG PET/CT, the short postsurgery period and extensive uptake suggested skull base osteomyelitis., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma., Methods: This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis., Results: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups., Conclusions: A magnetic resonance imaging-based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Irradiation of the brain regions from nasopharyngeal carcinoma (NPC) radiotherapy (RT) is frequently unavoidable, which may result in radiation-induced cognitive deficit. Using deep learning (DL), the study aims to develop prediction models in predicting compromised cognition in patients following NPC RT using remote assessments and determine their relation to the quality of life (QoL) and MRI changes., Methods: Seventy patients (20-76 aged) with MRI imaging (pre- and post-RT (6 months-1 year)) and complete cognitive assessments were recruited. Hippocampus, temporal lobes (TLs), and cerebellum were delineated and dosimetry parameters were extracted. Assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE), and QLQ-H&N 43). Regression and deep neural network (DNN) models were used to predict post-RT cognition using anatomical and treatment dose features., Results: Remote cognitive assessments were inter-correlated (r > 0.9). TLs showed significance in pre- and post-RT volume differences and cognitive deficits, that are correlated with RT-associated volume atrophy and dose distribution. Good classification accuracy based on DNN area under receiver operating curve (AUROC) for cognitive prediction (T-MoCA AUROC = 0.878, TICS AUROC = 0.89, Tele-MACE AUROC = 0.919)., Conclusion: DL-based prediction models assessed using remote assessments can assist in predicting cognitive deficit following NPC RT. Comparable results of remote assessments in assessing cognition suggest its possibility in replacing standard assessments., Implications for Cancer Survivors: Application of prediction models in individual patient enables tailored interventions to be provided in managing cognitive changes following NPC RT., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: This study aimed to explore the feasibility of [ 68  Ga]pentixafor positron emission tomography/computed tomography (PET/CT) in patients with nasopharyngeal carcinoma (NPC)., Procedures: This prospective study included patients with NPC who underwent [ 68  Ga]pentixafor PET/CT and 2-[ 18 F]fuoro-2-deoxy-D-glucose ([ 18 F]FDG) PET/CT within one week between November 2022 and March 2023. The [ 68  Ga]pentixafor and [ 18 F]FDG uptakes in primary and metastatic lesions were measured and compared., Results: Twenty-five participants (21 patients for initial stage and four patients for recurrence detection) were enrolled in our study. The participants underwent [ 18 F]FDG PET/CT and [ 68  Ga]pentixafor PET/CT. [ 68  Ga]pentixafor PET/CT had the same detection rate as [ 18 F]FDG for primary tumor (96% vs. 96%). The [ 68  Ga]pentixafor maximum standard uptake value (SUV max ) and target-to-background ratio (TBR) of primary tumors were lower than those of [ 18 F]FDG (SUV max : 8.13 ± 2.78 vs. 14.25 ± 6.45; P < 0.01; TBR: 5.17 ± 2.14 vs. 9.81 ± 5.30, P < 0.01). The difference between tumor volume of [ 68  Ga]pentixafor (TV pentixafor ) and tumor volume of [ 18 F]FDG (TV FDG ) showed no significance (median: 16.01 vs. 9.56, P = 0.332). In the detection of suspected metastatic cervical lymph nodes (CLNs), [ 68  Ga]pentixafor PET possessed a lower SUV max than [ 18 F]FDG PET/CT (SUV max : 6.86 ± 2.63 vs. 10.39 ± 5.28, P < 0.01), but there was no significant difference in the detection rate between [ 68  Ga]pentixafor and [ 18 F]FDG PET/CT (96 vs. 98, P = 0.613)., Conclusions: [ 68  Ga]pentixafor is a promising imaging tracer for detecting primary and metastatic NPC. [ 68  Ga]pentixafor PET/CT is comparable to [ 18 F]FDG PET/CT in the detection rate of primary tumors and metastatic cervical lymph nodes in nasopharyngeal carcinoma, but [ 68  Ga]pentixafor uptake was heterogeneous. [ 68  Ga]pentixafor PET/CT may help select patients most likely to benefit from CXCR4-directed endoradiotherapy., Clinical Trial Registration No: ChiCTR2200065902., (© 2024. The Author(s), under exclusive licence to World Molecular Imaging Society.)

Background: The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting., Methods: We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard)., Results: In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1-4, 5-9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort., Conclusions: The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection., 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 B.V. All rights reserved.)

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.)

Currently, clinical therapeutic strategies for nasopharyngeal carcinoma (NPC) confront insurmountable dilemmas in which surgical resection is incomplete and chemotherapy/radiotherapy has significant side effects. Phototherapy offers a maneuverable, effective, and noninvasive pattern for NPC therapy. Herein, we developed a lysosome-targeted and pH-responsive nanophototheranostic for near-infrared II (NIR-II) fluorescence imaging-guided photodynamic therapy (PDT) and photothermal therapy (PTT) of NPC. A lysosome-targeted S-D-A-D-S-type NIR-II phototheranostic molecule (IRFEM) is encapsulated within the acid-sensitive amphiphilic DSPE-Hyd-PEG2k to form IRFEM@DHP nanoparticles (NPs). The prepared IRFEM@DHP exhibits a good accumulation in the acidic lysosomes for facilitating the release of IRFEM, which could disrupt lysosomal function by generating an amount of heat and ROS under laser irradiation. Moreover, the guidelines of NIR-II fluorescence enhance the accuracy of PTT/PDT for NPC and avoid damage to normal tissues. Remarkably, IRFEM@DHP enable efficient antitumor effects both in vitro and in vivo , opening up a new avenue for precise NPC theranostics.

Background: Nasopharyngeal carcinoma (NPC) is a rare pediatric cancer. Most children are first diagnosed with advanced locoregional disease. Identification of patients at higher risk of treatment failure is crucial as they may benefit from more aggressive initial treatment approaches. 18 Fluorine-labeled fluoro-2-deoxyglucose positron emission tomography ( 18 F-FDG PET) has shown promise as a prognostic tool for predicting outcomes., Methods: Retrospective study of pediatric patients with locally advanced undifferentiated NPC who underwent 18 F-FDG PET/CT prior to intial treatment. Predictive significance of metabolic PET parameters on survival outcomes were estimated., Results: Thirty-two children were included, age range was 7.1-18 years at the time of diagnosis. The median follow-up duration was 46.1 months. Three patients (9.4%) were classified as AJCC stage IIb, 13 patients (40.6%) as stage IIIa, eight patients (25%) as stage IIIb, and eight patients (25%) as stage IVa. Our findings revealed that high whole-body metabolic tumor volume at the threshold of hepatic reference SUV mean (WB-MTV-HR) (>135 mL) was associated with significantly lower event-free survival (EFS) compared to the low WB-MTV-HR group (≤135 mL) (3-year EFS: 50% ± 18% vs. 82% ± 8%; p = .015). Additionally, the 3-year overall survival (OS) rates differed significantly between the high whole-body metabolic tumor volume at the threshold of an SUV of 2.5 isocontour (WB-MTV-2.5) group (MTV >74 mL) and the low WB-MTV-2.5 group (MTV ≤74 mL) (63% ± 18% vs. 100%; p = .021)., Conclusion: Our study suggests that WB-MTV parameters could serve as significant prognostic factors for disease progression in pediatric patients with locally advanced undifferentiated NPC. However, further prospective studies with larger sample sizes are needed to validate these findings., (© 2024 Wiley Periodicals LLC.)

Purpose: To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH)., Materials and Methods: This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets., Results: Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets., Conclusion: The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Purpose: To evaluate the clinical implication of magnetic resonance imaging (MRI)-derived skeletal muscle index (SMI) in locoregionally advanced nasopharyngeal carcinoma (LANPC) patients undergoing induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) and further to develop a nomogram for predicting survival prognosis., Methods: SMI was determined through baseline MRI at the third cervical level. The nomogram was based on a training cohort involving 409 LANPC patients. We validated the prognostic accuracy of this prognostic model in an internal validation cohort (n = 204) and an external independent cohort (n = 272)., Results: SMI was an independent risk factor for OS. A prognostic model comprising age, TNM stage and SMI for individual survival prediction was developed and graphically represented as a nomogram. The model showed favorable discrimination (C-index: 0.686), predictive accuracy [time dependent area under the curve (tAUC) at 5 years: 0.70], and calibration, and was further validated in the internal and external validation datasets. A risk stratification derived from the model stratified these patients into three prognostic subgroups with significantly different survival., Conclusions: Low SMI accessed by MRI was significantly associated with poor overall survival in LANPC patients undergoing IC + CCRT. Moreover, we established and validated a novel nomogram involving age, TNM stage and SMI that could provide accurate prognostic stratification among this population., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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: To investigate the feasibility of synthetic MRI (syMRI), diffusion-weighted imaging (DWI), and their combination with morphological features for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC)., Methods: Sixty-nine patients with nasopharyngeal tumours (NPL, n = 22; NPC, n = 47) who underwent syMRI and DWI were retrospectively enrolled between October 2020 and May 2022. syMRI and DWI quantitative parameters (T1, T2, PD, ADC) and morphological features were obtained. Diagnostic performance was assessed by independent sample t-test, chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test., Results: NPL has significantly lower T2, PD, and ADC values compared to NPC (all P < .05), whereas no significant difference was found in T1 value between these two entities (P > .05). The morphological features of tumour type, skull-base involvement, Waldeyer ring involvement, and lymph nodes involvement region were significantly different between NPL and NPC (all P < .05). The syMRI (T2 + PD) model has better diagnostic efficacy, with AUC, sensitivity, specificity, and accuracy of 0.875, 77.27%, 89.36%, and 85.51%. Compared with syMRI model, syMRI + Morph (PD + Waldeyer ring involvement + lymph nodes involvement region), syMRI + DWI (T2 + PD + ADC), and syMRI + DWI + Morph (PD + ADC + skull-base involvement + Waldeyer ring involvement) models can further improve the diagnostic efficiency (all P < .05). Furthermore, syMRI + DWI + Morph model has excellent diagnostic performance, with AUC, sensitivity, specificity, and accuracy of 0.986, 95.47%, 97.87%, and 97.10%, respectively., Conclusion: syMRI and DWI quantitative parameters were helpful in discriminating NPL from NPC. syMRI + DWI + Morph model has the excellent diagnostic efficiency in differentiating these two entities., Advances in Knowledge: syMRI + DWI + morphological feature method can differentiate NPL from NPC with excellent diagnostic performance., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: To investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI)., Methods: We included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed., Results: In the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle., Conclusions: White matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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

Objectives: Current guidelines recommend universal PET/CT screening for metastases staging in newly diagnosed nasopharyngeal carcinoma (NPC) despite the low rate of synchronous distant metastasis (SDM). The study aims to achieve individualized screening recommendations of NPC based on the risk of SDM., Methods and Materials: 18 pre-treatment peripheral blood indicators was retrospectively collected from 2271 primary NPC patients. A peripheral blood risk score (PBRS) was constructed by indicators associated with SDM on least absolute shrinkage and selection operator (LASSO) regression. The PBRS-based distant metastases (PBDM) model was developed from features selected by logistic regression analyses in the training cohort and then validated in the validation cohort. Receiver operator characteristic curve analysis, calibration curves, and decision curve analysis were applied to evaluate PBDM model performance., Results: Pre-treatment Epstein-Barr viral DNA copy number, percentage of total lymphocytes, serum lactate dehydrogenase level, and monocyte-to-lymphocyte ratio were most strongly associated with SDM in NPC and used to construct the PBRS. Sex (male), T stage (T3-4), N stage (N2-3), and PBRS (≥1.076) were identified as independent risk factors for SDM and applied in the PBDM model, which showed good performance. Through the model, patients in the training cohort were stratified into low-, medium-, and high-risk groups. Individualized screening recommendations were then developed for patients with differing risk levels., Conclusion: The PBDM model offers individualized recommendations for applying PET/CT for metastases staging in NPC, allowing more targeted screening of patients with greater risk of SDM compared with current recommendations., 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.)

Background: The issue of using deep learning to obtain accurate gross tumor volume (GTV) and metastatic lymph nodes (MLN) segmentation for nasopharyngeal carcinoma (NPC) on heterogeneous magnetic resonance imaging (MRI) images with limited labeling remains unsolved., Method: We collected 918 patients with MRI images from three hospitals to develop and validate models and proposed a semi-supervised framework for the fine delineation of multi-center NPC boundaries by integrating uncertainty-based implicit neural representations named SIMN. The framework utilizes the deep mutual learning approach with CNN and Transformer, incorporating dynamic thresholds. Additionally, domain adaptive algorithms are employed to enhance the performance., Results: SIMN predictions have a high overlap ratio with the ground truth. Under the 20 % labeled cases, for the internal test cohorts, the average DSC in GTV and MLN are 0.7981 and 0.7804, respectively; for external test cohort Wu Zhou Red Cross Hospital, the average DSC in GTV and MLN are 0.7217 and 0.7581, respectively; for external test cohorts First People Hospital of Foshan, the average DSC in GTV and MLN are 0.7004 and 0.7692, respectively. No significant differences are found in DSC, HD95, ASD, and Recall for patients with different clinical categories. Moreover, SIMN outperformed existing classical semi-supervised methods., Conclusions: SIMN showed a highly accurate GTV and MLN segmentation for NPC on multi-center MRI images under Semi-Supervised Learning (SSL), which can easily transfer to other centers without fine-tuning. It suggests that it has the potential to act as a generalized delineation solution for heterogeneous MRI images with limited labels in clinical deployment., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Lymph node characteristics markedly affect nasopharyngeal carcinoma (NPC) prognosis. Matted node (MN), an important characteristic for lymph node, lacks explored MRI-based prognostic implications., Purpose: Investigate MRI-determined MNs' prognostic value in NPC, including 5-year overall survival (OS), distant metastasis-free survival (DMFS), local recurrence-free survival (LRFS), progression-free survival (PFS), and its role in induction chemotherapy (IC)., Study Type: Retrospective cohort survival study., Population: Seven hundred ninety-two patients with non-metastatic NPC (female: 27.3%, >45-year old: 50.1%) confirmed by biopsy., Field Strength/sequence: 5-T/3.0-T, T1-, T2- and post-contrast T1-weighted fast spin echo sequences acquired., Assessment: MNs were defined as ≥3 nodes abutting with intervening fat plane replaced by extracapsular nodal spread (ENS). Patients were observed every 3 months for 2 years and every 6 months for 5 years using MRI. Follow-up extended from treatment initiation to death or final follow-up. MNs were evaluated by three radiologists with inter-reader reliability calculated. A 1:1 matched-pair method compared survival differences between MN-positive patients with or without IC. Primary endpoints (OS, DMFS, LRFS, PFS) were calculated from therapy initiation to respective event., Statistical Tests: Kappa values assessed inter-reader reliability. Correlation between MN, ENS, and LNN was studied through Spearman's correlation coefficient. Clinical characteristics were calculated via Fisher's exact, Chi-squared, and Student's t-test. Kaplan-Meier curves and log-rank tests analyzed all time-to-event data. Confounding factors were included in Multivariable Cox proportional hazard models to identify independent prognostic factors. P-values <0.05 were considered statistically significant., Results: MNs incidence was 24.6%. MNs independently associated with decreased 5-year OS, DMFS, and PFS; not LRFS (P = 0.252). MN-positive patients gained significant survival benefit from IC in 5-year OS (88.4% vs. 66.0%) and PFS (76.4% vs. 53.5%), but not DMFS (83.1% vs. 69.9%, P = 0.145) or LRFS (89.9% vs. 77.8%, P = 0.140)., Data Conclusion: MNs may independently stratify NPC risk and offer survival benefit from IC., Evidence Level: 3 TECHNICAL EFFICACY: Stage 2., (© 2023 International Society for Magnetic Resonance in Medicine.)

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.

Background: Although contrast-enhanced magnetic resonance imaging (MRI) detects early-stage nasopharyngeal carcinoma (NPC) not detected by endoscopic-guided biopsy (EGB), a short contrast-free screening MRI would be desirable for NPC screening programs. This study evaluated a screening MRI in a plasma Epstein-Barr virus (EBV)-DNA NPC screening program., Methods: EBV-DNA-screen-positive patients underwent endoscopy, and endoscopy-positive patients underwent EGB. EGB was negative if the biopsy was negative or was not performed. Patients also underwent a screening MRI. Diagnostic performance was based on histologic confirmation of NPC in the initial study or during a follow-up period of at least 2 years., Results: The study prospectively recruited 354 patients for MRI and endoscopy; 40/354 (11.3%) endoscopy-positive patients underwent EGB. Eighteen had NPC (5.1%), and 336 without NPC (94.9%) were followed up for a median of 44.8 months. MRI detected additional NPCs in 3/18 (16.7%) endoscopy-negative and 2/18 (11.1%) EGB-negative patients (stage I/II, n = 4; stage III, n = 1). None of the 24 EGB-negative patients who were MRI-negative had NPC. MRI missed NPC in 2/18 (11.1%), one of which was also endoscopy-negative. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI, endoscopy, and EGB were 88.9%, 91.1%, 34.8%, 99.4%, and 91.0%; 77.8%, 92.3%, 35.0%, 98.7%, and 91.5%; and 66.7%, 92.3%, 31.6%, 98.1%, and 91.0%, respectively., Conclusion: A quick contrast-free screening MRI complements endoscopy in NPC screening programs. In EBV-screen-positive patients, MRI enables early detection of NPC that is endoscopically occult or negative on EGB and increases confidence that NPC has not been missed., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)