Individualized radiology screening for newly diagnosed nasopharyngeal carcinoma.

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.

Enhanced MRI Radiomics Based Model for Predicting Recurrence or Metastasis of Nasopharyngeal Cancer (NC) Undergoing Concurrent Chemoradiotherapy: A Retrospective Study.

Nasopharyngeal Carcinoma (NC) refers to the malignant tumor that occurs at the top and side walls of the nasopharyngeal cavity. The NC incidence rate always dominates the first among the malignant tumors of the ear, nose and throat, and mainly occurs in Asia. NC cases are mainly concentrated in southern provinces in China, with about 4 million existing NC. With the pollution of environment and pickled diet, and the increase of life pressure, the domestic NC incidence rate has reached 4.5-6.5/100000 and is increasing year by year. It was reported that the known main causes of NC include hereditary factor, genetic mutations, and EB virus infection, common clinical symptoms of NC include nasal congestion, bloody mucus, etc. About 90% of NC is highly sensitive to radiotherapy which is regard as the preferred treatment method; However, for NC with lower differentiation, larger volume, and recurrence after treatment, surgical resection and local protons and heavy ions therapy are also indispensable means. According to reports, the subtle heterogeneity and diversity exists in some NC, with about 80% of NC undergone radiotherapy and about 25% experienced recurrence and death within five years after radiotherapy in China. Therefore, screening the NC population with suspected recurrence after concurrent chemoradiotherapy may improve survival rates in current clinical decision-making.

NC is one of the prevalent malignancies of the head and neck region with poor prognosis. The aim of this study is to establish a predictive model for assessing NC prognosis using clinical and MR radiomics data.

WET-UNet: Wavelet integrated efficient transformer networks for nasopharyngeal carcinoma tumor segmentation.

Nasopharyngeal carcinoma is a malignant tumor that occurs in the epithelium and mucosal glands of the nasopharynx, and its pathological type is mostly poorly differentiated squamous cell carcinoma. Since the nasopharynx is located deep in the head and neck, early diagnosis and timely treatment are critical to patient survival. However, nasopharyngeal carcinoma tumors are small in size and vary widely in shape, and it is also a challenge for experienced doctors to delineate tumor contours. In addition, due to the special location of nasopharyngeal carcinoma, complex treatments such as radiotherapy or surgical resection are often required, so accurate pathological diagnosis is also very important for the selection of treatment options. However, the current deep learning segmentation model faces the problems of inaccurate segmentation and unstable segmentation process, which are mainly limited by the accuracy of data sets, fuzzy boundaries, and complex lines. In order to solve these two challenges, this article proposes a hybrid model WET-UNet based on the UNet network as a powerful alternative for nasopharyngeal cancer image segmentation. On the one hand, wavelet transform is integrated into UNet to enhance the lesion boundary information by using low-frequency components to adjust the encoder at low frequencies and optimize the subsequent computational process of the Transformer to improve the accuracy and robustness of image segmentation. On the other hand, the attention mechanism retains the most valuable pixels in the image for us, captures the remote dependencies, and enables the network to learn more representative features to improve the recognition ability of the model. Comparative experiments show that our network structure outperforms other models for nasopharyngeal cancer image segmentation, and we demonstrate the effectiveness of adding two modules to help tumor segmentation. The total data set of this article is 5000, and the ratio of training and verification is 8:2. In the experiment, accuracy = 85.2% and precision = 84.9% can show that our proposed model has good performance in nasopharyngeal cancer image segmentation.

Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma.

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

Prognostic value of pre-treatment [18F] FDG PET/CT in recurrent nasopharyngeal carcinoma without distant metastasis.

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

MFNet: Meta-learning based on frequency-space mix for MRI segmentation in nasopharyngeal carcinoma.

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

Semi-supervised model based on implicit neural representation and mutual learning (SIMN) for multi-center nasopharyngeal carcinoma segmentation on MRI.

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.

Nomograms based on multiparametric MRI radiomics integrated with clinical-radiological features for predicting the response to induction chemotherapy in nasopharyngeal carcinoma.

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.

Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging.

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.

Three-dimensional pseudo-continuous arterial spin-labelled perfusion imaging for diagnosing upper cervical lymph node metastasis in patients with nasopharyngeal carcinoma: a whole-node histogram analysis.

AIM: To evaluate whole-node histogram parameters of blood flow (BF) maps derived from three-dimensional pseudo-continuous arterial spin-labelled (3D pCASL) imaging in discriminating metastatic from benign upper cervical lymph nodes (UCLNs) for nasopharyngeal carcinoma (NPC) patients. MATERIALS AND METHODS: Eighty NPC patients with a total of 170 histologically confirmed UCLNs (67 benign and 103 metastatic) were included retrospectively. Pre-treatment 3D pCASL imaging was performed and whole-node histogram analysis was then applied. Histogram parameters and morphological features, such as minimum axis diameter (MinAD), maximum axis diameter (MaxAD), and location of UCLNs, were assessed and compared between benign and metastatic lesions. Predictors were identified and further applied to establish a combined model by multivariate logistic regression in predicting the probability of metastatic UCLNs. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic performance. RESULTS: Metastatic UCLNs had larger MinAD and MinAD/MaxAD ratio, greater energy and entropy values, and higher incidence of level II (upper jugular group), but lower BF10th value than benign nodes (all p<0.05). MinAD, BF10th, energy, and entropy were validated as independent predictors in diagnosing metastatic UCLNs. The combined model yielded an area under the curve (AUC) of 0.932, accuracy of 84.42 %, sensitivity of 80.6 %, and specificity of 90.29 %. CONCLUSIONS: Whole-node histogram analysis on BF maps is a feasible tool to differentiate metastatic from benign UCLNs in NPC patients, and the combined model can further improve the diagnostic efficacy.

Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data.

Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However, this task suffers from the challenges of incomplete multi-modal examination data fusion and image data heterogeneity among sites. This paper proposes a cross-site survival analysis method for prognosis prediction of nasopharyngeal carcinoma from domain adaptation viewpoint. Utilizing a Cox model as the basic framework, our method equips it with a cross-attention based multi-modal fusion regularization. This regularization model effectively fuses the multi-modal information from multi-parametric MR images and clinical features onto a domain-adaptive space, despite the absence of some modalities. To enhance the feature discrimination, we also extend the contrastive learning technique to censored data cases. Compared with the conventional approaches which directly deploy a trained survival model in a new site, our method achieves superior prognosis prediction performance in cross-site validation experiments. These results highlight the key role of cross-site adaptability of our method and support its value in clinical practice.

Design, Preclinical Evaluation, and Clinical Translation of 68Ga-FAPI-LM3, a Heterobivalent Molecule for PET Imaging of Nasopharyngeal Carcinoma.

Extensive research has been conducted on radiolabeled fibroblast activation protein (FAP) inhibitors (FAPIs) and p-Cl-Phe-cyclo(d-Cys-Tyr-d-4-amino-Phe(carbamoyl)-Lys-Thr-Cys)d-Tyr-NH2 (LM3) peptides for imaging of FAP and somatostatin receptor 2 (SSTR2)-positive tumors. In this study, we designed and synthesized a FAPI-LM3 heterobivalent molecule radiolabeled with 68Ga and evaluated its effectiveness in both tumor xenografts and patients with nasopharyngeal carcinoma (NPC). Methods: The synthesis of FAPI-LM3 was based on the structures of FAPI-46 and LM3. After radiolabeling with 68Ga, its dual-receptor-binding affinity was evaluated in vitro and in vivo. Preclinical studies, including small-animal PET and biodistribution evaluation, were conducted on HT-1080-FAP and HT-1080-SSTR2 tumor xenografts. The feasibility of 68Ga-FAPI-LM3 PET/CT in a clinical setting was evaluated in patients with NPC, and the results were compared with those of 18F-FDG. Results: 68Ga-FAPI-LM3 showed high affinity for both FAP and SSTR2. The tumor uptake of 68Ga-FAPI-LM3 was significantly higher than that of 68Ga-FAPI-46 and 68Ga-DOTA-LM3 in HT-1080-FAP-plus-HT-1080-SSTR2 tumor xenografts. In a clinical study involving 6 NPC patients, 68Ga-FAPI-LM3 PET/CT showed significantly higher uptake than did 18F-FDG in primary and metastatic lesions, leading to enhanced lesion detectability and tumor delineation. Conclusion: 68Ga-FAPI-LM3 exhibited FAPI and SSTR2 dual-receptor-targeting properties both in vitro and in vivo, resulting in improved tumor uptake and retention compared with that observed with monomeric 68Ga-FAPI and 68Ga-DOTA-LM3. This study highlights the clinical feasibility of 68Ga-FAPI-LM3 PET/CT for NPC imaging.

CT-based peritumoral radiomics nomogram on prediction of response and survival to induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma.

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

Early detection of nasopharyngeal carcinoma: performance of a short contrast-free screening magnetic resonance imaging.

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.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

Synthetic MRI quantitative parameters in discriminating stage T1 nasopharyngeal carcinoma and benign hyperplasia: Combination with morphological features.

PURPOSE: To investigate the feasibility of synthetic MRI (syMRI) quantitative parameters and its combination with morphological features in discriminating stage T1 nasopharyngeal carcinoma (T1-NPC) and benign hyperplasia (BH). MATERIAL AND METHODS: Eighty-eight patients with nasopharyngeal lesions (T1-NPC, n = 54; BH, n = 34) were retrospectively enrolled between October 2020 and May 2022. The syMRI quantitative parameters of nasopharyngeal lesions (T1, T2, PD, T1SD, T2SD, PDSD) and longus capitis (T1, T2, PD) were measured, and T1ratio, T2ratio and PDratio were calculated (lesion/longus capitis). The morphological features (lesion pattern, retention cyst, serrated protrusion, middle ear effusion, tumor volume, and retropharyngeal lymph node) were compared. Statistical analyses were performed using the independent sample t test, Chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test. RESULTS: The T1, T2, PD, T1SD, T1ratio, and T2ratio values of T1-NPC were significantly lower than those of BH. The morphological features (lesion pattern, retention cyst, retropharyngeal lymph node) were significant difference between these two entities. T2 value has the highest AUC in all syMRI quantitative parameters, followed by T1, T1ratio, PD, T2ratio and T1SD. Combined syMRI quantitative parameters (T2, PD, T1ratio) can further improve the diagnosis efficiency. Combined syMRI parameters and morphological feature (T2, PD, lesion pattern, retropharyngeal lymph node) has the excellent diagnostic efficiency, with AUC, sensitivity, specificity, and accuracy of 0.979, 96.30%, 97.06%, 96.77%. CONCLUSIONS: Synthetic MRI was helpful in distinguishing T1-NPC from BH, and combined syMRI quantitative parameters and morphological features has the optimal diagnostic performance.

Radiologic extranodal extension for nodal staging in nasopharyngeal carcinoma.

PURPOSE: Extranodal extension (ENE) has the potential to add value to the current nodal staging system (N8th) for predicting outcome in nasopharyngeal carcinoma (NPC). This study aimed to incorporate ENE, as well as cervical nodal necrosis (CNN) to the current stage N3 and evaluated their impact on outcome prediction. The findings were validated on an external cohort. METHODS & MATERIALS: Pre-treatment MRI of 750 patients from the internal cohort were retrospectively reviewed. Predictive values of six modified nodal staging systems that incorporated four patterns of ENE and two patterns of CNN to the current stage N3 for disease-free survival (DFS) were compared with that of N8th using multivariate cox-regression and concordance statistics in the internal cohort. Performance of stage N3 for predicting disease recurrence was calculated. An external cohort of 179 patients was used to validate the findings. RESULTS: Incorporation of advanced ENE, which infiltrates into adjacent muscle/skin/salivary glands outperformed the other five modifications for predicting outcomes (p < 0.01) and achieved a significantly higher c-index for 5-year DFS (0.69 vs 0.72) (p < 0.01) when compared with that of N8th staging system. By adding advanced ENE to the current N3 increased the sensitivity for predicting disease recurrence from 22.4 % to 47.1 %. The finding was validated in the external cohort (5-year DFS 0.65 vs. 0.72, p < 0.01; sensitivity of stage N3 increased from 14.0 % to 41.9 % for disease recurrence). CONCLUSION: Results from two centre cohorts confirmed that the radiological advanced ENE should be considered as a criterion for stage N3 disease in NPC.

FDG PET/CT Demonstrated Unilateral Striatum Hypometabolism in a Case of Advanced Nasopharyngeal Cancer.

ABSTRACT: A 79-year-old man with nasopharyngeal cancer (NPC) presented with diplopia symptom and a history of diabetes mellitus was referred for an FDG PET/CT scan to determine the pretreatment staging. The FDG PET/CT scan revealed NPC with skull base invasion and decreased FDG uptake at the left striatum. A review of his clinical history and a brain MRI conducted 5 months ago confirmed a previous diagnosis of left hyperglycemic hemichorea. In this NPC patient with inadequate blood sugar control, unilateral striatum hypometabolism may persist for up to 5 months after the initial clinical symptoms.

Application of narrow band imaging and Lugol's iodine staining in screening for nasopharyngeal carcinoma.

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

Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma.

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