Noninvasive Photothermal Therapy of Nasopharyngeal Cancer Guided by High Efficiency Optical-Absorption Nanomaterial Enhanced by NIR-II Photoacoustic Imaging.

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.

The prognostic value of pretreatment 18F-FDG PET-CT parameters with peripheral blood markers in patients with de novo metastatic nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To develop and validate a prognostic nomogram based on pretreatment 18F-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.

Skull Base Aspergillus Infection Mimicking Tumor Recurrence: A Nasopharyngeal Carcinoma Case Study.

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.

Lysosome-Targeted and pH-Activatable Phototheranostics for NIR-II Fluorescence Imaging-Guided Nasopharyngeal Carcinoma Phototherapy.

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.

A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma.

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.

Quantitative parameter analysis of pretreatment dual-energy computed tomography in nasopharyngeal carcinoma cervical lymph node characteristics and prediction of radiotherapy sensitivity.

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.

A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.

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.

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.

Synthetic MRI and diffusion-weighted imaging for differentiating nasopharyngeal lymphoma from nasopharyngeal carcinoma: combination with morphological features.

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.

68 Ga-DOTATATE PET/CT Detected Bone Metastasis Earlier Than 68 Ga-FAPI PET/CT and 99m Tc-MDP Bone Scintigraphy in a Patient With Nasopharyngeal Carcinoma.

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.

Comparison of [18F]FDG and [68 Ga]pentixafor PET/CT in Nasopharyngeal Carcinoma.

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-[18F]fuoro-2-deoxy-D-glucose ([18F]FDG) PET/CT within one week between November 2022 and March 2023. The [68 Ga]pentixafor and [18F]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 [18F]FDG PET/CT and [68 Ga]pentixafor PET/CT. [68 Ga]pentixafor PET/CT had the same detection rate as [18F]FDG for primary tumor (96% vs. 96%). The [68 Ga]pentixafor maximum standard uptake value (SUVmax) and target-to-background ratio (TBR) of primary tumors were lower than those of [18F]FDG (SUVmax: 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 (TVpentixafor) and tumor volume of [18F]FDG (TVFDG) 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 SUVmax than [18F]FDG PET/CT (SUVmax: 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 [18F]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 [18F]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.

Impact of magnetic resonance imaging-derived skeletal muscle index in locoregionally advanced nasopharyngeal carcinoma.

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.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

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.

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.

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.

Predictive significance of pretreatment 18F-FDG PET volumetric parameters on survival outcomes in pediatric patients with locally advanced undifferentiated nasopharyngeal carcinoma.

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. 18Fluorine-labeled fluoro-2-deoxyglucose positron emission tomography (18F-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 18F-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 SUVmean (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.

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.