MR Imaging of Nasopharyngeal Carcinoma

Multimodal Image Confidence: A Novel Method for Tumor and Organ Boundary Representation

The indistinct boundaries of tumors and organs at risk in medical images present significant challenges in treatment planning and other tasks in radiation therapy. This study introduces an innovative analytical algorithm called multimodal image confidence (MMC), which leverages the complementary strengths of various multimodal medical images to assign a confidence measure to each voxel within the region of interest (ROI). MMC enables the generation of modality-specific ROI-enhanced images, providing a detailed depiction of both the boundaries and internal features of the ROI. By employing an interpretable mathematical model that propagates voxel confidence based on intervoxel correlations, MMC circumvents the need for model training, distinguishing it from deep learning-based methods. The alogorithm was evaluated qualitatively and quantitatively on 156 nasopharyngeal carcinoma cases and 1251 glioma cases. Qualitative assessments demonstrated MMC's accuracy in delineating lesion boundaries as well as capturing internal tumor characteristics. Quantitative analyses further revealed strong concordance between MMC and manual delineations. This study presents a cutting-edge algorithm for identifying and illustating ROI boundaries using multimodal 3D medical images. The versatility of the proposed method extends to both targets and organs at risk across various anatomic sites and multiple image modalities, enhancing its potential for accurate delineation of critical structures andmany image-related tasks in radiaton therapy and other fields.

Prognostic Value of Skull Base Foramen Invasion Subclassification in T Category Modification and Induction Chemotherapy Management for Nasopharyngeal Carcinoma: Post-Hoc Analysis of a Dual-Center Retrospective Cohort Study

Skull base foramen invasion (SBFI) indicates poor prognosis in nasopharyngeal carcinoma (NPC). However, only a few studies systematically assessed the role of SBFIin staging and treatment of NPC. To investigate the prognostic value of SBFI in NPC, a total of 1,752 patients with nonmetastatic NPC from two hospitals (1,320 and 432) between January 2010 and March 2014 are enrolled. The primary endpoint is overall survival (OS). Heatmap/cluster and network analyses are used to provide subclassification indication. Univariate and multivariate analyses with Kaplan-Meier method are performed to compare survival outcomes. SBFIs are classified into slight (only foramen lacerum and/or pterygopalatine fossa invasion) and severe (other SBFIs). The severe SBFI is an unfavorable prognosticator for OS in both the entire cohort and the T3 group. OS is similar between T3 with severe SBFI and T4 patients. Reclassifying T3 with severe SBFI as the T4 category yields an improved T category discrimination. Additionally, patients in the severe SBFI group gain significant survival benefits from induction chemotherapy ((IC). Therefore, T3 NPC with severe SBFI is an independent negative predictor for OS and is classified into the T4 category. T category adjustment enables better prognostic stratification. Severe SBFI benefits from IC in long-term OS.

Head and Neck Squamous Cell Carcinoma: Insights from Dual-Energy Computed Tomography (DECT)

Head and neck cancer represents the seventh most common neoplasm worldwide, with squamous cell carcinoma being the most represented histologic variant. The rising incidence of the neoplastic pathology of this district, coupled with the drastic changes in its epidemiology over the past decades, have posed significant challenges to physicians worldwide in terms of diagnosis, prognosis, and treatment. In order to meet these challenges, a considerable amount of effort has been spent by the authors of the recent literature to explore new technologies and their possible employment for the better diagnostic and prognostic definition of head and neck squamous cell carcinoma (HNSCC). Among these technologies, a growing interest has been gathering around the possible applications of dual-energy computed tomography (DECT) in head and neck pathology. Dual-energy computed tomography (DECT) utilizes two distinct X-ray energy spectra to obtain two datasets in a single scan, allowing for material differentiation based on unique attenuation profiles. DECT offers key benefits such as enhanced contrast resolution, reduced beam-hardening artifacts, and precise iodine quantification through monochromatic reconstructions. It also creates material decomposition images, like iodine maps, aiding in tumor characterization and therapy assessment. This paper aims to summarize recent findings on the use of DECT in HNSCC, providing a comprehensive overview to aid further research and exploration in the field.

Cross-Domain Mutual-Assistance Learning Framework for Fully Automated Diagnosis of Primary Tumor in Nasopharyngeal Carcinoma

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.

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.

Surgical Management of Complex Skull Base Tumor Using Preoperative Multimodal Image Fusion Technology

OBJECTIVE

To review our single-institution experience in the surgical management of complex skull base tumors using multimodal image fusion technology.

METHODS

From October 2019 to January 2022, 7 cases of complex skull base tumors that performed preoperative multimodal image fusion in Zhuhai People's Hospital neurosurgery department were involved in this study. The image data were uploaded to the GE AW workstation. Corresponding image sequences were opened in the workstation to complete registration fusion and 3D reconstruction. We retrospectively reviewed the clinical and imaging data, and surgical strategy, respectively.

RESULTS

one case of recurrent C2 schwannoma, 1 case of recurrent spindle cell tumor of the left cranio-orbital communication, 1 case of lobular malignant tumor of the left infratemporal fossa, 1 case of central giant cell repairing granuloma, 1 case of mesenchymal malignant tumor in left pharyngeal process, 1 case of meningioma in jugular foramen, and 1 case of hemangioblastoma with vascular malformation in fourth ventricular. All cases underwent preoperative multimodal image fusion for the surgical plan and all cases had gross total resection. Except for one case of mesenchymal malignant tumor in left pharyngeal process that had dysphagia and one case of hemangioblastoma that had discoordination, others cases were without postoperative complication.

CONCLUSIONS

Preoperative multimodal image fusion and surgical approach simulation benefit complex skull base tumor surgical treatment. Individually multiple image assessment of complex skull base tumors to determine the specific surgical strategy is more rational and should be recommended (Supplemental Digital Content 1, Supplementary Video, http://links.lww.com/SCS/F936).

Radiological features of thyroid-like low-grade nasopharyngeal papillary adenocarcinoma: case series and systematic review

PURPOSE

To comprehensively summarize the clinical data and CT/MRI characteristics of thyroid-like low-grade nasopharyngeal papillary adenocarcinoma (TL-LGNPPA).

METHODS

Twenty-seven lesions from 25 study articles identified through a systematic review and three lesions from our institution associated with TL-LGNPPA were evaluated.

RESULTS

The mean age of the patients at diagnosis was 35.7 years, and the male-to-female ratio was nearly half. The chief complaint was nasal obstruction, followed by epistaxis. All patients underwent excision. None of the patients had neck nodes or distant metastases. All patients survived with no locoregional/distant recurrence during 3-93 months of follow-up. All lesions were located at the posterior edge of the nasal septum, attached to the nasopharyngeal parietal wall, and showed no laterality. The mean lesion diameter was 1.7 cm. The margins of lesions were well-defined and lobulated, followed by well-defined smooth margins. None of lesions were associated with parapharyngeal space or skull base destruction. All lesions were iso- and low-density on non-contrast CT. Adjacent skull base sclerosis was detected in 63.6% of lesions. High signal intensity on T2-weighted imaging and mostly iso-signal intensity on T1-weighted imaging compared to muscle tissue. Most lesions were heterogeneous and exhibited moderate contrast enhancement. Relatively large lesions (≥1.4 cm) tended to be more lobulated than smooth margins compared to relatively small lesions (<1.4 cm) (p = 0.016).

CONCLUSION

We summarized the clinical and radiological features of TL-LGNPPA to facilitate accurate diagnosis and appropriate management.

Embracing Personalized Strategies in Radiotherapy for Nasopharyngeal Carcinoma: Beyond the Conventional Bounds of Fields and Borders

Radiotherapy is the primary treatment modality for non-metastatic nasopharyngeal carcinoma (NPC) across all TN-stages. Locoregional control rates have been impressive even from the 2D radiotherapy (RT) era, except when the ability to deliver optimal dose coverage to the tumor is compromised. However, short- and long-term complications following head and neck RT are potentially debilitating, and thus, there has been much research investigating technological advances in RT delivery over the past decades, with the primary goal of limiting normal tissue damage. On this note, with a plateau in gains of therapeutic ratio by modern RT techniques, future advances have to be focused on individualization of RT, both in terms of dose prescription and the delineation of target volumes. In this review, we analyzed the guidelines and evidence related to contouring methods, and dose prescription for early and locoregionally advanced (LA-) NPC. Next, with the preference for induction chemotherapy (IC) in patients with LA-NPC, we assessed the evidence concerning radiotherapy adaptations guided by IC response, as well as functional imaging and contour changes during treatment. Finally, we discussed on RT individualization that is guided by EBV DNA assessment, and its importance in the era of combinatorial immune checkpoint blockade therapy with RT.

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.

The Detection of Nasopharyngeal Carcinomas Using a Neural Network Based on Nasopharyngoscopic Images

OBJECTIVE

To construct and validate a deep convolutional neural network (DCNN)-based artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using archived nasopharyngoscopic images.

METHODS

We retrospectively collected 14107 nasopharyngoscopic images (7108 NPCs and 6999 noncancers) to construct a DCNN model and prepared a validation dataset containing 3501 images (1744 NPCs and 1757 noncancers) from a single center between January 2009 and December 2020. The DCNN model was established using the You Only Look Once (YOLOv5) architecture. Four otolaryngologists were asked to review the images of the validation set to benchmark the DCNN model performance.

RESULTS

The DCNN model analyzed the 3501 images in 69.35 s. For the validation dataset, the precision, recall, accuracy, and F1 score of the DCNN model in the detection of NPCs on white light imaging (WLI) and narrow band imaging (NBI) were 0.845 ± 0.038, 0.942 ± 0.021, 0.920 ± 0.024, and 0.890 ± 0.045, and 0.895 ± 0.045, 0.941 ± 0.018, and 0.975 ± 0.013, 0.918 ± 0.036, respectively. The diagnostic outcome of the DCNN model on WLI and NBI images was significantly higher than that of two junior otolaryngologists (p < 0.05).

CONCLUSION

The DCNN model showed better diagnostic outcomes for NPCs than those of junior otolaryngologists. Therefore, it could assist them in improving their diagnostic level and reducing missed diagnoses.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:127-135, 2024.

Prognostic modeling for nasopharyngeal carcinoma (NC) undergoing concurrent chemoradiotherapy using clinical and enhanced MRI-Delta radiomics data: A preliminary study

BACKGROUND

Nasopharyngeal carcinoma (NC) is one of the prevalent malignancies of the head and neck region with poor prognosis.

OBJECTIVE

The aim of this study is to establish a predictive model for assessing NC prognosis based on clinical and MR radiomics data, subsequently to develop a nomogram for practical application.

METHODS

Retrospective analysis was conducted on clinical and imaging data collected between May 2010 and August 2018, involving 211 patients diagnosed with histologically confirmed NC who received concurrent chemoradiotherapy or radical surgery in Xiangyang No. 1 People's Hospital. According to 5-10 years of follow-up results, the patients were divided into two groups: the study group (n= 76), which experienced recurrence, metastasis, or death, and the control group (n= 135), characterized by normal survival. Training and testing subsets were established at a 7:3 ratio, with a predefined time cutoff. In the training set, three prediction models were established: a clinical data model, an imaging model, and a combined model using the integrated variation in clinical characteristics along with MR radiomics parameters (Delta-Radscore) observed before and after concurrent chemoradiotherapy. Model performance was compared using Delong's test, and net clinical benefit was assessed via decision curve analysis (DCA). Then, external validation was conducted on the test set, and finally a nomogram predicting NC prognosis was created.

RESULTS

Univariate analysis identified that the risk factors impacting the prognosis of NC included gender, pathological type, neutrophil to lymphocyte ratio (NLR), degree of tumor differentiation, MR enhancement pattern, and Delta-Radscore (P< 0.05). The combined model established based on the abovementioned factors exhibited significantly higher predictive performance [AUC: 0.874, 95% CI (0.810-0.923)] than that of the clinical data model [AUC: 0.650, 95% CI (0.568-0.727)] and imaging model [AUC: 0.824, 95% CI (0.753-0.882)]. DCA also demonstrated superior clinical net benefit in the combined model, a finding further verified by results from the test set. The developed nomogram, based on the combined model, exhibited promising performance in clinical applications.

CONCLUSION

The Delta-Radscore derived from MR radiomics data before and after concurrent chemoradiotherapy helps enhance the performance of the NC prognostic model. The combined model and resultant nomogram provide valuable support for clinical decision-making in NC treatment, ultimately contributing to an improved survival rate.

Evaluation of 11 C-Choline PET/CT for T Staging and Tumor Volume Delineation in Nasopharyngeal Cancer Patients in Comparison to 18 F-FDG PET/CT

PURPOSE

Accurate determination of the primary tumor extension of nasopharyngeal carcinoma (NPC) by 18 F-FDG PET/CT is limited by the high physiological 18 F-FDG uptake in the surrounding area, especially in the brain tissue. We aimed to assess whether 11 C-choline PET/CT could improve the accuracy of T staging and tumor volume delineation for NPC patients.

METHODS

Patients with pathologically confirmed diagnosis of NPC were enrolled. The primary tumor extension of each patient was evaluated by 11 C-choline PET/CT, 18 F-FDG PET/CT, and contrast-enhanced MRI. The PET/CT-based tumor volume ( VPET ) was measured by 3 threshold methods, including the threshold of SUV 2.5 (Th 2.5 ), 40% of maximal SUV (Th 40% ), and the relative background-dependent threshold (Th bgd ). Tumor volume and Dice similarity coefficient were compared among VPET with different segmentation methods and VMR .

RESULTS

Thirty-three patients with treatment-naive NPC and 6 patients with suspicious recurrent disease were enrolled. The NPC lesions were avid for both 11 C-choline and 18 F-FDG. Visual analysis showed that 11 C-choline PET/CT had better contrast and higher discernability than 18 F-FDG PET/CT for intracranial, skull base, and orbital involvement. 11 C-choline PET/CT also exhibited advantage over MRI for differentiation between local recurrence and radiation-induced alterations. For the tumor delineated, the VMR was larger than VPET in general, except for 18 F-FDG PET/CT with Th 2.5 threshold. For all 3 threshold methods applied, 11 C-choline PET/CT produced more consistent and comparable tumor volume to MRI than 18 F-FDG PET/CT. 11 C-choline PET/CT with Th bgd threshold showed the closest tumor volume and highest similarity to MRI.

CONCLUSIONS

11 C-choline PET/CT provides a higher accuracy than 18 F-FDG PET/CT in mapping tumor extension in locally advanced NPC and may be a promising complement to MRI in delineating the primary tumor.

Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients

AIM

To establish and validate radiomic models for response prediction to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) using the radiomic features from pretreatment MRI.

MATERIALS AND METHODS

This retrospective analysis included 184 consecutive NPC patients, 132 in the primary cohort and 52 in the validation cohort. Radiomic features were derived from contrast-enhanced T1-weighted imaging (CE-T1) and T2-weighted imaging (T2-WI) for each subject. The radiomic features were then selected and combined with clinical characteristics to build radiomic models. The potential of the radiomic models was evaluated based on its discrimination and calibration. To measure the performance of these radiomic models in predicting the treatment response to IC in NPC, the area under the receiver operating characteristic curve (AUC), and sensitivity, specificity, and accuracy were used.

RESULTS

Four radiomic models were constructed in the present study including the radiomic signature of CE-T1, T2-WI, CE-T1 + T2-WI, and the radiomic nomogram of CE-T1. The radiomic signature of CE-T1 + T2-WI performed well in distinguishing response and non-response to IC in patients with NPC, which yielded an AUC of 0.940 (95% CI, 0.885-0.974), sensitivity of 83.1%, specificity of 91.8%, and accuracy of 87.1% in the primary cohort, and AUC of 0.952 (95% CI, 0.855-0.992), sensitivity of 74.2%, specificity of 95.2%, and accuracy of 82.7% in the validation cohort.

CONCLUSION

MRI-based radiomic models could be helpful for personalised risk stratification and treatment in NPC patients receiving IC.

Deep learning-based accurate delineation of primary gross tumor volume of nasopharyngeal carcinoma on heterogeneous magnetic resonance imaging: A large-scale and multi-center study

BACKGROUND AND PURPOSE

The problem of obtaining accurate primary gross tumor volume (GTVp) segmentation for nasopharyngeal carcinoma (NPC) on heterogeneous magnetic resonance imaging (MRI) images with deep learning remains unsolved. Herein, we reported a new deep-learning method than can accurately delineate GTVp for NPC on multi-center MRI scans.

MATERIAL AND METHODS

We collected 1057 patients with MRI images from five hospitals and randomly selected 600 patients from three hospitals to constitute a mixed training cohort for model development. The resting patients were used as internal (n = 259) and external (n = 198) testing cohorts for model evaluation. An augmentation-invariant strategy was proposed to delineate GTVp from multi-center MRI images, which encouraged networks to produce similar predictions for inputs with different augmentations to learn invariant anatomical structure features. The Dice similarity coefficient (DSC), 95 % Hausdorff distance (HD95), average surface distance (ASD), and relative absolute volume difference (RAVD) were used to measure segmentation performance.

RESULTS

The model-generated predictions had a high overlap ratio with the ground truth. For the internal testing cohorts, the average DSC, HD95, ASD, and RAVD were 0.88, 4.99 mm, 1.03 mm, and 0.13, respectively. For external testing cohorts, the average DSC, HD95, ASD, and RAVD were 0.88, 3.97 mm, 0.97 mm, and 0.10, respectively. No significant differences were found in DSC, HD95, and ASD for patients with different T categories, MRI thickness, or in-plane spacings. Moreover, the proposed augmentation-invariant strategy outperformed the widely-used nnUNet, which uses conventional data augmentation approaches.

CONCLUSION

Our proposed method showed a highly accurate GTVp segmentation for NPC on multi-center MRI images, suggesting that it has the potential to act as a generalized delineation solution for heterogeneous MRI images.

Normal size of benign upper neck nodes on MRI: parotid, submandibular, occipital, facial, retroauricular and level IIb nodal groups

PURPOSE

Nodal size is an important imaging criterion for differentiating benign from malignant nodes in the head and neck cancer staging. This study evaluated the size of normal nodes in less well-documented nodal groups in the upper head and neck on magnetic resonance imaging (MRI).

METHODS

Analysis was performed on 289 upper head and neck MRIs of patients without head and neck cancer. The short axial diameters (SAD) of the largest node in the parotid, submandibular, occipital, facial, retroauricular and Level IIb of the upper internal jugular nodal groups were documented and compared to the commonly used threshold of ≥ 10 mm for diagnosis of a malignant node.

RESULTS

Normal nodes in the parotid, occipital, retroauricular and Level IIb groups were small with a mean SAD ranging from 3.8 to 4.4 mm, nodes in the submandibular group were larger with a mean SAD of 5.5 mm and facial nodes were not identified. A size ≥ 10 mm was found in 0.8% of submandibular nodes. Less than 10% of the other nodal group had a SAD of ≥ 6 mm and none of them had a SAD ≥ 8 mm.

CONCLUSION

To identify malignant neck nodes in these groups there is scope to reduce the size threshold of ≥ 10 mm to improve sensitivity without substantial loss of specificity.

MRI detection of suspected nasopharyngeal carcinoma: a systematic review and meta-analysis

PURPOSE

Endoscopic biopsy is recommended for diagnosis of nasopharyngeal carcinoma (NPC). A proportion of lesions are hidden from endoscopic view but detected with magnetic resonance imaging (MRI). This systematic review and meta-analysis investigated the diagnostic performance of MRI for detection of NPC.

METHODS

An electronic search of twelve databases and registries was performed. Studies were included if they compared the diagnostic accuracy of MRI to a reference standard (histopathology) in patients suspected of having NPC. The primary outcome was accuracy for detection of NPC. Random-effects models were used to pool outcomes for sensitivity, specificity, and positive and negative likelihood ratio (LR). Bias and applicability were assessed using the modified QUADAS-2 tool.

RESULTS

Nine studies were included involving 1736 patients of whom 337 were diagnosed with NPC. MRI demonstrated a pooled sensitivity of 98.1% (95% CI 95.2-99.3%), specificity of 91.7% (95% CI 88.3-94.2%), negative LR of 0.02 (95% CI 0.01-0.05), and positive LR of 11.9 (95% CI 8.35-16.81) for detection of NPC. Most studies were performed in regions where NPC is endemic, and there was a risk of selection bias due to inclusion of retrospective studies and one case-control study. There was limited reporting of study randomization strategy.

CONCLUSION

This study demonstrates that MRI has a high pooled sensitivity, specificity, and negative predictive value for detection of NPC. MRI may be useful for lesion detection prior to endoscopic biopsy and aid the decision to avoid biopsy in patients with a low post-test probability of disease.

Minimally Invasive Surgery for Early-Stage Nasopharyngeal Carcinoma

According to the National Comprehensive Cancer Network guidelines, the preferred treatment for early-stage nasopharyngeal carcinoma (NPC) is radiotherapy, however, the toxic effects associated with radiotherapy have been a nuisance for patients. Minimally invasive surgery for recurrent NPC has been widely recognized as an effective way to completely remove the tumor and free the patient from or mitigate the toxicity of radiotherapy. Therefore, some researchers hope that minimally invasive surgery can be used to treat early-stage NPC. It is a bold and controversial attempt, and the researchers' efforts have achieved initial results. This article reviews the preliminary results of minimally invasive surgery for NPC, especially the feasibility and challenges of minimally invasive surgery for early-stage NPC.

Multi-Modal Optical Imaging and Combined Phototherapy of Nasopharyngeal Carcinoma Based on a Nanoplatform

Nasopharyngeal carcinoma (NPC) is a common malignant tumor of the head and neck with a high incidence rate worldwide, especially in southern China. Phototheranostics in combination with nanoparticles is an integrated strategy for enabling simultaneous diagnosis, real-time monitoring, and administration of precision therapy for nasopharyngeal carcinoma (NPC). It has shown great potential in the field of cancer diagnosis and treatment owing to its unique noninvasive advantages. Many Chinese and international research teams have applied nano-targeted drugs to optical diagnosis and treatment technology to conduct multimodal imaging and collaborative treatment of NPC, which has become a hot research topic. In this review, we aimed to introduce the recent developments in phototheranostics of NPC based on a nanoplatform. This study aimed to elaborate on the applications of nanoplatform-based optical imaging strategies and treatment modalities, including fluorescence imaging, photoacoustic imaging, Raman spectroscopy imaging, photodynamic therapy, and photothermal therapy. This study is expected to provide a scientific basis for further research and development of NPC diagnosis and treatment.