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.

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.

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.

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.

Imaging spectrum and complications of otogenic infections: insights into delayed diagnosis

Skull base osteomyelitis (SBO) is a late manifestation of complicated otogenic infections that presents a diagnostic challenge. Delayed or missed diagnoses lead to high morbidity and mortality and can be attributed to non-specific symptoms, subtle early radiologic findings, radiologic mimicry of nasopharyngeal carcinoma (NPC), and under-recognition from clinician and radiologists. This pictorial review aims to emphasize on early imaging recognition and distinction between SBO and NPC.

Salvage neck surgery in recurrent nodal NPC: Do all patients require a comprehensive neck dissection in the modern MRI era?

OBJECTIVE

The standard treatment for regional failure in nasopharyngeal carcinoma (NPC) is the radical neck dissection (RND). Our study sought to determine if magnetic resonance imaging (MRI) may accurately predict nodal involvement to allow selected levels of neck dissection to be preserved.

STUDY DESIGN AND SETTING

We analysed retrospectively all NPC patients in our centre undergoing neck dissections as salvage therapy for nodal recurrence. Nodal involvement based on the preoperative MRI was assessed and compared with postoperative histopathology.

METHODS

This is a retrospective study conducted on patients in our centre with recurrent NPC from February 2002 to February 2017. Patients were identified from the database of the otolaryngology oncology division at our institution. Of these, 28 patients met all our inclusion and exclusion criteria. We calculated sensitivity and specificity as well as average number of nodes per patient.

RESULTS

In our study, we calculated the false negative and false positive rates of preoperative MRI neck by levels. Overall sensitivity of MRI picking up disease by level was 76% and specificity was 86%.

CONCLUSION

Based on our study, we will be missing a total of 10 (7.1%) diseased neck levels in eight (28.5%) patients. MRI alone, therefore, does not provide enough information to allow safe selective preservation of neck levels in surgical salvage of neck recurrences in NPC.

Endoscopic endonasal combined transoral medial approach to the nasopharynx, parapharyngeal space, and jugular foramen

OBJECTIVE

This study aimed to validate the feasibility of an endoscopic endonasal combined transoral medial approach for treating lesions in the nasopharynx, parapharyngeal space (PPS), and jugular foramen.

METHODS

Anatomical and imaging information of six patients who underwent surgery via this approach were reviewed and analyzed.

RESULTS

The feasibility and advantages of the endoscopic endonasal combined transoral medial approach, which uses an inside-to-outside medial surgical corridor, were identified. Total resection was achieved in 3 cases with benign tumors. Safe resection margins were obtained in 2 cases with recurrent nasopharyngeal carcinoma (NPC). Pathological biopsy of NPC lesion between the Eustachian tube and arterial sheath was achieved. The internal carotid artery (ICA) was accurately located and protected in all cases and no complications occurred.

CONCLUSION

Lesions in the nasopharynx, PPS, and jugular foramen can be directly assessed via this approach. The ICA can be well identified during the surgery.

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.

Significance of radiologic extranodal extension in locoregionally advanced nasopharyngeal carcinoma with lymph node metastasis: a comprehensive nomogram

OBJECTIVE

We aimed to assess the significance of rENE and creat a predictive tool (nomogram) for estimating Overall Survival (OS) in locoregionally advanced Nasopharyngeal Carcinoma (NPC) patients with Lymph Node Metastasis (LNM) based on their clinical characteristics and Radiologic Extranodal Extension (rENE).

METHODS

Five hundred and sixty-nine NPC patients with LNM were randomly divided into training and validation groups. Significant factors were identified using univariate and multivariate analyses in the training cohort. Then, the nomogram based on the screening results was established to predict the Overall Survival (OS). Calibration curves and the Concordance index (C-index) gauged predictive accuracy and discrimination. Receiver Operating Characteristic (ROC) analysis assessed risk stratification, and clinical utility was measured using Decision Curve Analysis (DCA). The nomogram's performance was validated for discrimination and calibration in an independent validation cohort.

RESULTS

A total of 360 (63.2%) patients were present with radiologic extranodal extension at initial diagnosis. Patients with rENE had significantly lower OS than other patients. Multivariate analysis identified the five factors, including rENE, for the nomogram model. The C-index was 0.75 (0.71-0.78) in the training cohort and 0.76 (0.69-0.83) in the validation cohort. Notably, the nomogram outperformed the 8th TNM staging system, as evident from the higher AUC values (0.77 vs. 0.60 for 2year and 0.75 vs. 0.65 for 3year) and well-calibrated calibration curves. Decision curve analysis indicated improved Net Benefit (NB) with the nomogram for predicting OS. The log-rank test confirmed significant survival distinctions between risk groups in both training and validation cohorts.

CONCLUSIONS

We demonstrated the prognostic value of rENE in nasopharyngeal carcinoma and developed a nomogram based on rENE and other factors to provide individual prediction of OS for locoregionally advanced nasopharyngeal carcinoma with lymph node metastasis.

LEVEL OF EVIDENCE

III.

Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma

BACKGROUND

The prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment.

PURPOSE

To investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC.

MATERIALS AND METHODS

A retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme.

RESULTS

Integrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (p<0.05). Multimodality radiomics combined with clinical data offered the highest performance. Despite the absence of EBV DNA, radiomics integration significantly improved survival predictions (C-index ranging from 0.770 ± 0.070 to 0.831 ± 0.083 in combined model versus 0.727 ± 0.084 to 0.734 ± 0.088 in clinical model, p<0.05).

CONCLUSIONS

The combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone.

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.

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.

Diffusion tensor imaging parameters for the early diagnosis of radiation-induced brain injury in patients with nasopharyngeal carcinoma: a meta-analysis

PURPOSE

To estimate diffusion tensor imaging (DTI) parameters for early diagnosis during the stage of radiation-induced brain injury (RBI) in nasopharyngeal carcinoma (NPC) patients.PubMed, Embase, Web of Science and Cochrane Library were searched up to March 2019. Eligible studies comparing early brain injuries with controls of temporal lobe in NPC patients before and after radiotherapy which collected the DTI parameters such as apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusibility (λa), radial diffusibility (λr), mean diffusion (MD) were included.

CONCLUSION

Seven studies (N = 21) were selected from the studies in the databases. Overall, FA, λa, λr values were significant difference between early RBI and healthy control (HC) in NPC patients after radiotherapy (MD= -0.03, 95% CI= -0.05∼-0.01; p = .008 in FA, MD= -0.07, 95% CI= -0.11∼-0.02; p = .002 in λa and MD = 0.02, 95% CI = 0.00 ∼ 0.04; p = .04 in λr). The meta regression analysis about dose dependence with FA value was: -0.057 ∼ 0.0003 in 95% CI, I2=74.70%, P = 0.052 (adjust p = .029). The overall heterogeneity is p < .001, I2=91% in FA, P = 0.08, I2=61% in λa and p = .04, I2=69% in λr. DTI parameters such as the reduced FA value, the decreased λa value, and the increased λr value were significant in the early period of RBI in NPC patients after radiotherapy, which becoming a more sensitive method in diagnosing the early stage of RBI.

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.

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.

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.

Pretreatment dual-energy CT for predicting early response to induction chemotherapy and survival in nasopharyngeal carcinoma

OBJECTIVES

To evaluate the predictive performance of pretreatment dual-energy CT (DECT) for early response to induction chemotherapy and survival in nasopharyngeal carcinoma (NPC).

METHODS

In this retrospective study, 56 NPC patients who underwent pretreatment DECT scans with posttreatment follow-up were enrolled. The DECT-derived normalised iodine concentration (nIC), effective atomic number (Zeff), 40-180 keV (20 keV interval), and Mix-0.3 value of the tumour lesions were measured to predict the early response to induction chemotherapy and survival in nasopharyngeal carcinoma. The Mann‒Whitney U test, ROC analysis, Kaplan‒Meier method with log-rank test, and Cox proportional hazards model were performed to evaluate the predictive performance of DECT parameters, respectively.

RESULTS

Among all DECT-derived parameters, ROC analysis showed the predictive performances of nIC and Zeff values for early objective response to induction chemotherapy (AUCs of 0.803 and 0.826), locoregional failure-free survival (AUCs of 0.786 and 0.767), progression-free survival (AUCs of 0.856 and 0.731) and overall survival (AUCs of 0.765 and 0.799) in NPC patients, respectively (all p < 0.05). Moreover, multivariate analysis showed that a high nIC value was an independent predictor of poor survival in NPC. In addition, survival analysis indicated that NPC patients with higher nIC values in primary tumours tend to have lower 5-year locoregional failure-free survival, progression-free survival and overall survival rates than those with lower nIC values.

CONCLUSIONS

DECT-derived nIC and Zeff values can predict early response to induction chemotherapy and survival in NPC; in particular, a high nIC value is an independent predictive factor of poor survival in NPC.

CLINICAL RELEVANCE STATEMENT

Preoperative dual-energy computed tomography may provide predictive value for early response and survival outcomes in patients with nasopharyngeal carcinoma, and facilitate their clinical management.

KEY POINTS

• Pretreatment dual-energy computed tomography helps to predict early response to therapy and survival in NPC. • NIC and Zeff values derived from dual-energy computed tomography can predict early objective response to induction chemotherapy and survival in NPC. • A high nIC value is an independent predictive factor of poor survival in NPC.

Prognosis Forecast of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma Based on Deep Learning Multi-Modal Information Fusion

Radiation therapy is the primary treatment for recurrent nasopharyngeal carcinoma. However, it may induce necrosis of the nasopharynx, leading to severe complications such as bleeding and headache. Therefore, forecasting necrosis of the nasopharynx and initiating timely clinical intervention has important implications for reducing complications caused by re-irradiation. This research informs clinical decision-making by making predictions on re-irradiation of recurrent nasopharyngeal carcinoma using deep learning multi-modal information fusion between multi-sequence nuclear magnetic resonance imaging and plan dose. Specifically, we assume that the hidden variables of model data can be divided into two categories: task-consistency and task-inconsistency. The task-consistency variables are characteristic variables contributing to target tasks, while the task-inconsistency variables are not apparently helpful. These modal characteristics are adaptively fused when the relevant tasks are expressed through the construction of supervised classification loss and self-supervised reconstruction loss. The cooperation of supervised classification loss and self-supervised reconstruction loss simultaneously reserves the information of characteristic space and controls potential interference simultaneously. Finally, multi-modal fusion effectively fuses information through an adaptive linking module. We evaluated this method on a multi-center dataset. and found the prediction based on multi-modal features fusion outperformed predictions based on single-modal, partial modal fusion or traditional machine learning methods.

A Rulefit-based prognostic analysis using structured MRI report to select potential beneficiaries from induction chemotherapy in advanced nasopharyngeal carcinoma: A dual-centre study

BACKGROUND AND PURPOSE

Structured MRI report facilitate prognostic prediction for nasopharyngeal carcinoma (NPC). However, the intrinsic association among structured variables is not fully utilised. This study aimed to investigate the performance of a Rulefit-based model in feature integration behind structured MRI report and prognostic prediction in advanced NPC.

MATERIALS AND METHODS

We retrospectively enrolled 1207 patients diagnosed with non-metastatic advanced NPC from two centres, and divided into training (N = 544), internal testing (N = 367), and external testing (N = 296) cohorts. Machine learning algorithms including multivariate analysis, deep learning, Lasso, and Rulefit were used to establish corresponding prognostic models. The concordance indices (C- indices) of three clinical and six combined models with different algorithms for overall survival (OS) prediction were compared. Survival benefits of induction chemotherapy (IC) were calculated among risk groups stratified by different models. A website was established for individualised survival visualisation.

RESULTS

Incorporating structured variables into Stage model significantly improved the prognostic prediction performance. Six prognostic rules with structured variables were identified by Rulefit. OS prediction of Rules model was comparable to Lasso model in internal testing cohort (C-index: 0.720 vs. 0.713, P = 0.100) and achieved the highest C-index of 0.711 in external testing cohort, indicating better generalisability. The Rules model stratified patients into risk groups with significant 5-year OS differences in each cohort, and revealed significant survival benefits from additional IC in high-risk group.

CONCLUSION

The Rulefit-based Rules model, with the revelation of intrinsic associations behind structured variables, is promising in risk stratification and guiding individualised IC treatment for advanced NPC.

OCT-A evaluation of the retinal and choroidal structures of patients with a history of radiotherapy due to nasopharyngeal carcinoma

BACKGROUND

To perform the optical coherence tomography angiography (OCT-A) evaluation of the microvascular structures of the retina and choroidal tissue in asymptomatic patients who received radiotherapy for nasopharyngeal carcinoma and to compare the results to those of healthy individuals.

METHODS

Ophthalmological examinations were performed in all asymptomatic patients without vascular or systemic diseases, or fundus findings who had received radiotherapy at least two years earlier. Then, OCT-A scans were obtained. Foveal, parafoveal, and whole retinal thicknesses, vessel densities in the superficial and deep capillary plexuses, subfoveal choroidal thickness, the non-flow area in the superficial capillary plexus, and the choriocapillaris flow area were measured and compared to the values of the healthy control group.

RESULTS

The radiotherapy group had significantly lower deep capillary plexus vascular density and subfoveal choroidal thickness values and significantly higher choriocapillaris flow area values compared to the control group.

CONCLUSIONS

We consider that OCT-A is useful in the early diagnosis of radiation retinopathy that may develop during follow-up in patients with nasopharyngeal carcinoma who have received radiotherapy.

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.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study

BACKGROUND

Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC.

METHODS

This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes.

RESULTS

The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity.

CONCLUSIONS

We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

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.

Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma

PURPOSE

Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients.

METHODS

A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability.

RESULTS

Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups.

CONCLUSION

Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.

AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality

OBJECTIVE

To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI of patients with nasopharyngeal carcinoma (NPC).

METHODS

Sixty-six patients with pathologically confirmed NPC underwent nasopharynx and neck examination using a 3.0-T MRI system. Transverse T2-weighted fast spin-echo (FSE) sequence, transverse T1-weighted FSE sequence, post-contrast transverse T1-weighted FSE sequence, and post-contrast coronal T1-weighted FSE were obtained by both ACS and PI techniques, respectively. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and duration of scanning of both sets of images analyzed by ACS and PI techniques were compared. The images from the ACS and PI techniques were scored for lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale.

RESULTS

The examination time with ACS technique was significantly shorter than that with PI technique (p < 0.0001). The comparison of SNR and CNR showed that ACS technique was significantly superior with PI technique (p < 0.005). Qualitative image analysis showed that the scores of lesion detection, margin sharpness of lesions, artifacts, and overall image quality were higher in the ACS sequences than those in the PI sequences (p < 0.0001). Inter-observer agreement was evaluated for all qualitative indicators for each method, in which the results showed satisfactory-to-excellent agreement (p < 0.0001).

CONCLUSION

Compared with the PI technique, the ACS technique for MR examination of NPC can not only shorten scanning time but also improve image quality.

CLINICAL RELEVANCE STATEMENT

The artificial intelligence (AI)-assisted compressed sensing (ACS) technique shortens examination time for patients with nasopharyngeal carcinoma, while improving the image quality and examination success rate, which will benefit more patients.

KEY POINTS

• Compared with the parallel imaging (PI) technique, the artificial intelligence (AI)-assisted compressed sensing (ACS) technique not only reduced examination time, but also improved image quality. • Artificial intelligence (AI)-assisted compressed sensing (ACS) pulls the state-of-the-art deep learning technique into the reconstruction procedure and helps find an optimal balance of imaging speed and image quality.

Deep learning-based recurrence detector on magnetic resonance scans in nasopharyngeal carcinoma: A multicenter study

OBJECTIVES

Accuracy in the detection of recurrent nasopharyngeal carcinoma (NPC) on follow-up magnetic resonance (MR) scans needs to be improved.

MATERIAL AND METHODS

A total of 5 035 follow-up MR scans from 5 035 survivors with treated NPC between April 2007 and July 2020 were retrospectively collected from three cancer centers for developing and evaluating the deep learning (DL) model MODERN (MR-based Deep learning model for dEtecting Recurrent Nasopharyngeal carcinoma). In a reader study with 220 scans, the accuracy of two radiologists in detecting recurrence on scans with vs without MODERN was evaluated. The performance was measured using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy with a 95% confidence interval (CI).

RESULTS

MODERN exhibited sound performance in the validation cohort (internal: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 1: ROC-AUC, 0.88, 95% CI, 0.86-0.90; external 2: ROC-AUC, 0.85, 95% CI, 0.82-0.88). In a reader study, MODERN alone achieved reliable accuracy compared to that of radiologists (MODERN: 84.1%, 95% CI, 79.3%-88.9%; competent: 80.9%, 95% CI, 75.7%-86.1%, P < 0.001; expert: 85.9%, 95% CI, 81.3%-90.5%, P < 0.001). The accuracy of radiologists was boosted by the MODERN score (competent with MODERN score: 84.6%, 95% CI, 79.8%-89.3%, P < 0.001; expert with MODERN score: 87.7%, 95% CI, 83.4%-92.1%, P < 0.001).

CONCLUSION

We developed a DL model for recurrence detection with reliable performance. Computer-human collaboration has the potential to refine the workflow in interpreting surveillant MR scans among patients with treated NPC.

A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma

OBJECTIVE

As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various factors, making it difficult for clinical physicians to predict the outcome, the objective of this study was to develop a deep learning-based signature for risk stratification in NPC patients.

METHODS

A total of 293 patients were enrolled in the study and divided into training, validation, and testing groups with a ratio of 7:1:2. MRI scans and corresponding clinical information were collected, and the 3-year disease-free survival (DFS) was chosen as the endpoint. The Res-Net18 algorithm was used to develop two deep learning (DL) models and another solely based on clinical characteristics developed by multivariate cox analysis. The performance of both models was evaluated using the area under the curve (AUC) and the concordance index (C-index). Discriminative performance was assessed using Kaplan-Meier survival analysis.

RESULTS

The deep learning approach identified DL prognostic models. The MRI-based DL model showed significantly better performance compared to the traditional model solely based on clinical characteristics (AUC: 0.8861 vs 0.745, p = 0.04 and C-index: 0.865 vs 0.727, p = 0.03). The survival analysis showed significant survival differences between the risk groups identified by the MRI-based model.

CONCLUSION

Our study highlights the potential of MRI in predicting the prognosis of NPC through DL algorithm. This approach has the potential to become a novel tool for prognosis prediction and can help physicians to develop more valid treatment strategies in the future.

Integrating pretreatment MRI-detected nodal features and Epstein-Barr virus DNA to identify optimal candidates for intensity-modulated radiotherapy alone in patients with stage II nasopharyngeal carcinoma

OBJECTIVES

To develop and validate a prognostic nomogram based on MRI-detected features of retropharyngeal and cervical lymph nodes and Epstein-Barr virus (EBV) DNA in patients with stage II nasopharyngeal carcinoma (NPC) to distinguish low-risk patients for whom intensity-modulated radiotherapy (IMRT) alone is sufficient.

METHODS

This retrospective study enrolled 894 patients with stage II NPC (596 and 298 in the training and validation cohorts, respectively) with pretreatment MRI between August 2010 and May 2019. All patients received IMRT with or without additional chemotherapy. We identified independent risk factors using univariate and multivariate Cox regression analyses. Survival was compared using Kaplan-Meier curves with the log-rank test.

RESULTS

Independent factors derived from the multivariate analysis include cervical nodal necrosis (CNN), the extracapsular spread (ECS) of cervical and retropharyngeal lymph nodes, and gamma-glutamyl transferase (γ-GGT). Nomograms A, B, and C were established based on the clinical [tumor-node-metastasis (TNM) stage + Epstein-Barr virus (EBV) DNA], the clinical-radiological [all independent predictors] and the combined models [the clinical-radiological model + EBV DNA], respectively. Nomogram C (C-index 0.769 [0.718-0.820]) demonstrated better risk discrimination than nomogram B (0.762 [0.715-0.809]), nomogram A (0.619 [0.564-0.674]), and the TNM stage (0.560 [0.509-0.611]). In the low-risk group divided by nomogram C, no significant survival differences were observed between patients treated with radiotherapy (RT) alone and other regimens including additional chemotherapy.

CONCLUSIONS

The nomogram combining MRI-detected retropharyngeal and cervical lymph node features with pretreatment EBV-DNA improved the prognostic risk stratification for stage II NPC.

Prognostic value of primary tumor and lymph node volumetric metabolic parameters at pre-treatment F-18 FDG PET/CT in nasopharyngeal carcinoma

BACKGROUND

The aim of this study was to evaluate the prognostic significance of volumetric metabolic parameters of pre-treatment PET/CT along with clinical characteristics in patients with non-metastatic nasopharyngeal carcinoma.

MATERIAL AND METHODS

Seventy-nine patients with nasopharyngeal carcinoma underwent F18- FDG PET/CT for pretreatment evaluation and included in this study. The patient features (patient age, tumor histopathology, T and N stage, size of primary tumor and the largest cervical lymph node) and PET parameters were analyzed: maximum, mean and peak standardized uptake values (SUVmax, SUVmean, SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for primary tumor and largest cervical lymph node. After treatment, patients were evaluated for disease progression and mortality. Survival analysis for progression-free survival (PFS) and over-all survival (OS) was performed with Kaplan-Meier method using PET findings and clinical characteristics.

RESULTS

The median follow-up duration was 29.7 months (range 3-125 months). Among clinical characteristics, no parameters had significance association for PFS. Primary tumor-MTV and cervical lymph node-MTV were independent prognostic factors for PFS (p = 0.025 and p = 0.004, respectively).Patients with primary tumor-MTV >19.4 and patients with lymph node-MTV>3.4 had shorter PFS. For OS, age and the size of the lymph node were independent prognostic factor (p = 0.031 and p = 0.029).Patients with age over 54 years and patients with lymph node size >1 cm were associated with decreased OS.

CONCLUSION

Primary tumor-MTV and lymph node-MTV on pre-treatment PET/CT are significant prognostic factors for long-term PFS in non-metastatic nasopharyngeal carcinoma. We consider that measuring MTV as volume-based metabolic parameter on pretreatment PET/CT may contribute decision of treatment intensity and individualized risk stratification and may improve long-term PFS. Additionally, age and the size of lymph node are independent prognostic factors for mortality.

Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy

BACKGROUND

Cone-beam computed tomography (CBCT) scanning is used for patient setup in image-guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning.

PURPOSE

This study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer.

METHODS

CBCTs and deformed planning CT (dCT) from 75 patients (60/5/10 for training, validation and testing) were used to compare cycle-consistent Generative Adversarial Network (cycleGAN), Unet, Unet+cycleGAN and conditionalGenerative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), spatial non-uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose-volume histogram metrics.

RESULTS

The cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%/2 mm) has reached 99.02%, and the WET difference is only 1.28 mm. The D95 value of CTVs coverage and Dmax value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model.

CONCLUSIONS

The cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.

Improved prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma using pre-treatment CT radiomics

When planning radiation therapy, late effects due to the treatment should be considered. One of the most common complications of head and neck radiation therapy is hypothyroidism. Although clinical and dosimetric data are routinely used to assess the risk of hypothyroidism after radiation, the outcome is still unsatisfactory. Medical imaging can provide additional information that improves the prediction of hypothyroidism. In this study, pre-treatment computed tomography (CT) radiomics features of the thyroid gland were combined with clinical and dosimetric data from 220 participants to predict the occurrence of hypothyroidism within 2 years after radiation therapy. The findings demonstrated that the addition of CT radiomics consistently and significantly improves upon conventional model, achieving the highest area under the receiver operating characteristic curve (AUCs) of 0.81 ± 0.06 with a random forest model. Hence, pre-treatment thyroid CT imaging provides useful information that have the potential to improve the ability to predict hypothyroidism after nasopharyngeal radiation therapy.

Reproducibility and usefulness of quantitative apparent diffusion coefficient measurements for predicting program death-ligand 1 expression in nasopharyngeal carcinoma

BACKGROUND

Accurate assessment of programmed death-ligand 1 (PD-L1) expression status in nasopharyngeal carcinoma (NPC) before immunotherapy is crucial. We aimed to explore the reproducibility and usefulness of the quantitative apparent diffusion coefficient (ADC) measurements for predicting PD-L1expression status in NPC.

METHODS

We retrospectively recruited 134 NPC patients who underwent MRI scans and PD-L1 detection. A PD-L1 combined positive score (CPS) ≥ 20 was identified as high expression status. Patients were divide into two cohorts based on the MRI scanning devices, including a 1.5-T MRI cohort (n = 85, 44 PD-L1 high expression) and a 3.0-T MRI cohort (n = 49, 24 PD-L1 high expression). The mean ADC (ADCmean), minimum ADC (ADCmin) and maximal ADC (ADCmax) values were independently measured by two observers. The ADC measurement reproducibility was assessed by interclass correlation coefficients (ICC). The correlations between ADC parameters and CPS were analyzed by spearman's correlation coefficient (r), and the performance for PD-L1expression status prediction was assessed by the area under receiver operating characteristic curve (AUC).

RESULTS

The measurement reproducibility of ADCmean, ADCmin and ADCmax was good in the 1.5-T MRI cohort (ICC: 0.843-0.930) and 3.0-T MRI cohort (ICC: 0.929-0.960). The ADCmean, ADCmin, and ADCmax tended to inversely correlate with the CPS (r:-0.37 - -0.52 in the 1.5-T MRI cohort, and - 0.52 - -0.60 in the 3.0-T MRI cohort; P all < 0.01). The ADCmean, ADCmin and ADCmax yielded the AUC of 0.756 (95% CI: 0.651, 0.861), 0.689 (95% CI: 0.576, 0.802), and 0.733 (95%CI: 0.626, 0.839) in the 1.5-T MRI cohort and 0.820 (95%CI: 0.703, 0.937), 0.755 (95% CI: 0.616, 0.894), and 0.760 (95%CI: 0.627, 0.893) in the 3.0-T MRI cohort for predicting PD-L1 high expression status, respectively.

CONCLUSION

ADC measurements may act as a reproducible and feasible method to predict PD-L1 expression status in NPC.

Multimodality radiomics analysis based on [18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis

OBJECTIVES

To determine whether radiomics models developed from 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients.

METHODS

One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs. testing). For each modality/sequence (i.e., PET, CT, T1, T1C, and T2), 1906 radiomics features were extracted from the primary tumor volume. Univariate Cox model and correlation analysis were used for feature selection. A multivariate Cox model was used to establish radiomics signature. Prognostic performances of 5 individual modality models and 12 multimodality models (3 integrations × 4 fusion strategies) were assessed by the concordance index (C-index) and log-rank test. A clinical-radiomics nomogram was built to explore the clinical utilities of radiomics signature, which was evaluated by discrimination, calibration curve, and decision curve analysis (DCA).

RESULTS

The radiomics signatures of individual modalities showed limited prognostic efficacy with a C-index of 0.539-0.664 in the testing cohort. Different fusion strategies exhibited a slight difference in predictive performance. The PET/CT and MRI integrated model achieved the best performance with a C-index of 0.745 (95% CI, 0.619-0.865) in the testing cohort (log-rank test, p < 0.05). Clinical-radiomics nomogram further improved the prognosis, which also showed satisfactory discrimination, calibration, and net benefit.

CONCLUSIONS

Multimodality radiomics analysis by combining PET/CT with multisequence MRI could potentially improve the efficacy of PFS prediction for NPC patients.

KEY POINTS

• Individual modality radiomics models showed limited performance in prognosis evaluation for NPC patients. • Combined PET, CT and multisequence MRI radiomics signature could improve the prognostic efficacy. • Multilevel fusion strategies exhibit comparable performance but feature-level fusion deserves more attention.

Nasal Septal Angiofibroma

Juvenile nasopharyngeal angiofibroma is a benign vascular tumor seen predominantly in adolescent males in the second decade of life. Extranasopharyngeal angiofibroma includes vascular fibrous masses that occur outside the nasopharynx. The diagnosis of an angiofibroma is based on the clinical presentation and imaging, with biopsies being avoided to avoid excessive bleeding. Computed tomography scan is considered sufficient for the diagnosis of extranasopharyngeal angiofibroma as it clearly delineates and identifies the tumor.

Adjuvant chemotherapy or no adjuvant chemotherapy? A prediction model for the risk stratification of recurrence or metastasis of nasopharyngeal carcinoma combining MRI radiomics with clinical factors

BACKGROUND

Dose adjuvant chemotherapy (AC) should be offered in nasopharyngeal carcinoma (NPC) patients? Different guidelines provided the different recommendations.

METHODS

In this retrospective study, a total of 140 patients were enrolled and followed for 3 years, with 24 clinical features being collected. The imaging features on the enhanced-MRI sequence were extracted by using PyRadiomics platform. The pearson correlation coefficient and the random forest was used to filter the features associated with recurrence or metastasis. A clinical-radiomics model (CRM) was constructed by the Cox multivariable analysis in training cohort, and was validated in validation cohort. All patients were divided into high- and low-risk groups through the median Rad-score of the model. The Kaplan-Meier survival curves were used to compare the 3-year recurrence or metastasis free rate (RMFR) of patients with or without AC in high- and low-groups.

RESULTS

In total, 960 imaging features were extracted. A CRM was constructed from nine features (seven imaging features and two clinical factors). In the training cohort, the area under curve (AUC) of CRM for 3-year RMFR was 0.872 (P <0.001), and the sensitivity and specificity were 0.935 and 0.672, respectively; In the validation cohort, the AUC was 0.864 (P <0.001), and the sensitivity and specificity were 1.00 and 0.75, respectively. Kaplan-Meier curve showed that the 3-year RMFR and 3-year cancer specific survival (CSS) rate in the high-risk group were significantly lower than those in the low-risk group (P <0.001). In the high-risk group, patients who received AC had greater 3-year RMFR than those who did not receive AC (78.6% vs. 48.1%) (p = 0.03).

CONCLUSION

Considering increasing RMFR, a prediction model for NPC based on two clinical factors and seven imaging features suggested the AC needs to be added to patients in the high-risk group and not in the low-risk group.

A collision tumor of nasopharyngeal carcinoma and primary mantle cell lymphoma in the nasopharynx: a case report and review of the literature

BACKGROUND

Nasopharyngeal carcinoma (NPC) is more common in men aged 40 to 59, and radiotherapy is an effective treatment. Nasopharyngeal lymphoma (NPL) is rare, and the coexistence of nasopharyngeal mantle cell lymphoma (MCL) and NPC is even rarer. A collision tumor is a rare type of tumor that refers to two or more different tumors occurring in the same organ. No reports to date have described a collision tumor of NPC and MCL occurring within the same nasopharyngeal mass. We herein report the successful treatment of a unique case of synchronous coexistence of NPC and MCL occurring in the nasopharynx of a Chinese man.

CASE PRESENTATION

A 58-year-old man presented with a 5-month history of swallowing discomfort. Biopsy was performed under nasopharyngeal endoscopy, and histopathology revealed NPC. Magnetic resonance imaging revealed lesions in the nasopharynx, oropharynx, and tonsils, as well as enlarged lymph nodes in the parotid gland, posterior ear, and neck. This may be a synchronous dual primary tumor coexisting with NPC and NPL. Pathology consultation confirmed that the biopsy specimen of the nasopharynx was a collision tumor of NPC and MCL. Positron emission tomography computed tomography (PET-CT) revealed thickening of the posterior wall of the nasopharynx, which was considered NPC with lymphoma. The enlargement of the pharyngeal lymph ring and multiple hypermetabolic lymph nodes were evaluated as lymphoma infiltration. The patient received two courses of R-CHOP chemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) followed by head and neck radiotherapy. At the time of this writing, he had remained alive without recurrence for 61 months since the initial treatment and was still undergoing follow-up.

CONCLUSIONS

It is very important to correctly recognize collision tumors. Magnetic resonance imaging helps identify different components of collision tumors. Pathological examination helps to confirm the diagnosis. Histological examination reveals different components, and PET-CT can help determine the extent of the lesion. Dose-adjusted chemotherapy combined with radiotherapy may have promising herapeutic effects, but additional case studies are needed to confirm.

Multifunctional Nanotheranostics for Dual-Modal Imaging-Guided Precision Therapy of Nasopharyngeal Carcinoma

Currently, the low survival rate and poor prognosis of patients with nasopharyngeal carcinoma are ascribed to the lack of early and accurate diagnosis and resistance to radiotherapy. In parallel, the integration of imaging-guided diagnosis and precise treatment has gained much attention in the field of theranostic nanotechnology. However, constructing dual-modal imaging-guided nanotheranostics with desired imaging performance as well as great biocompatibility remains challenging. Therefore, we developed a simple but multifunctional nanotheranostic GdCPP for the early and accurate diagnosis and efficient treatment of nasopharyngeal carcinoma (NPC), which combined fluorescence imaging and magnetic resonance imaging (MRI) onto a single nanoplatform for imaging-guided subsequent photodynamic therapy (PDT). GdCPP had an appropriate particle size (81.93 ± 0.69 nm) and was highly stable, resulting in sufficient tumor accumulation, which along with massive reactive oxygen species (ROS) generation upon irradiation further significantly killed tumor cells. Moreover, GdCPP owned much stronger r1 relaxivity (9.396 mM-1 s-1) compared to clinically used Gd-DTPA (5.034 mM-1 s-1) and exhibited better T1WI MRI performance. Under dual-modal imaging-guided PDT, GdCPP achieved efficient therapeutic outcomes without causing any noticeable tissue damage. The results of in vitro and in vivo studies indicated that GdCPP may be a suitable candidate for dual-modal imaging-guided precision tumor therapy.

Amide proton transfer (APT) and magnetization transfer (MT) in predicting short-term therapeutic outcome in nasopharyngeal carcinoma after chemoradiotherapy: a feasibility study of three-dimensional chemical exchange saturation transfer (CEST) MRI

BACKGROUND

The three-dimensional chemical exchange saturation transfer (3D CEST) technique is a novel and promising magnetic resonance sequence; however, its application in nasopharyngeal carcinoma (NPC) lacks sufficient evaluation. This study aimed to assess the feasibility of the 3D CEST technique in predicting the short-term treatment outcomes for chemoradiotherapy (CRT) in NPC patients.

METHODS

Forty NPC patients and fourteen healthy volunteers were enrolled and underwent the pre-treatment 3D CEST magnetic resonance imaging and diffusion-weighted imaging (DWI). The reliability of 3D CEST was assessed in healthy volunteers by calculating the intra- and inter-observer correlation coefficient (ICC) for amide proton transfer weighted-signal intensity (APTw-SI) and magnetization transfer ratio (MTR) values. NPC patients were divided into residual and non-residual groups based on short-term treatment outcomes after CRT. Whole-tumor regions of interest (ROIs) were manually drawn to measure APTw-SI, MTR and apparent diffusion coefficient (ADC) values. Multivariate analysis and the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of clinical characteristics, APTw-SI, MTR, ADC values, and combined models in predicting short-term treatment outcomes in NPC patients.

RESULTS

For the healthy volunteer group, all APTw-SI and MTR values exhibited good to excellent intra- and inter-observer agreements (0.736-0.910, 0.895-0.981, all P > 0.05). For NPC patients, MTR values showed a significant difference between the non-residual and residual groups (31.24 ± 5.21% vs. 34.74 ± 1.54%, P = 0.003) while no significant differences were observed for APTw-SI and ADC values (P > 0.05). Moreover, the diagnostic power of MTR value was superior to APTw-SI (AUC: 0.818 vs. 0.521, P = 0.017) and comparable to ADC values (AUC: 0.818 vs. 0.649, P > 0.05) in predicting short-term treatment outcomes for NPC patients. The prediction performance did not improve even when combining MTR values with APTw-SI and/or ADC values (P > 0.05).

CONCLUSIONS

The pre-treatment MTR value acquired through 3D CEST demonstrated superior predictive performance for short-term treatment outcomes compared to APTw-SI and ADC values in NPC patients after CRT.

Application value of 3D pCASL in early assessment of potential radiation encephalopathy in nasopharyngeal carcinoma patients undergoing radiotherapy

OBJECTIVE

The study explores the application value of three-dimensional arterial spin labeling magnetic resonance imaging (3D pCASL) in early assessment of radiation encephalopathy (REP) in patients with nasopharyngeal carcinoma (NPC).

METHODS

A retrospective analysis of 39 cases of NPC was performed. Routine enhanced MRI scan and 3D pCASL imaging were used to examine the apparent diffusion coefficient (ADC) and brain blood flow (CBF) before and after treatment with intensity-modulated radiotherapy (IMRT). Dosimetric analysis of irradiation was performed. Receiver operating characteristic curve (ROC) was used to analyze diagnostic performance of two imaging methods.

RESULTS

There was no statistically significant difference between the two methods for the measurement of temporal white matter ADC, but statistically significant difference was found in CBF. 3D pCASL imaging showed more sensitivity, specificity and higher accuracy than conventional MRI enhanced scan in showing REP. The maximum dose of the temporal lobe was at the enhanced area.

CONCLUSION

The present study demonstrates that 3D pCASL scan at month 3 can reflect blood flow perfusion differences in NPC patients after IMRT and can accurately assess the possibility of REP at early stage. Enhanced areas have a higher probability of REP than the surrounding areas.

ADVANCES IN KNOWLEDGE

There is few magnetic resonance angiography studies used to evaluate arterial circulation on its application on potential REP after radiotherapy for NPC. In our study, we evaluate the application value of 3D pCASL in the early assessment of potential REP in patients with NPC after radiotherapy. The study was to provide an improved understanding of the early specific characteristics on MRI imaging and evolution of potential radiation encephalopathy using 3D pCASL technique, which can quantitatively evaluate the changes of blood flow in tissues at early stage and help to diagnose and treat potential radiation encephalopathy as early as possible.

Preoperative Embolization of Primary Juvenile Nasopharyngeal Angiofibroma: Is Embolization of Internal Carotid Artery Branches Necessary?

PURPOSE

To determine the effects of blood supply from internal carotid artery (ICA) on the surgical outcomes of primary juvenile nasopharyngeal angiofibroma (JNA) after transarterial embolization (TAE).

METHODS

A retrospective analysis was performed on primary JNA patients who underwent TAE and endoscopic resection in our hospital between December 2020 and June 2022. The angiography images of these patients were reviewed, and then they were divided into ICA + external carotid artery (ECA) feeding group and ECA feeding group according to whether the ICA branches were part of the feeding arteries. Tumors in ICA + ECA feeding group were fed by both ICA and ECA branches, while tumors in ECA feeding group were fed by ECA branches alone. All patients underwent tumor resection immediately after ECA feeding branches embolization. None of the patients underwent ICA feeding branches embolization. Data on demographics, tumor characteristics, blood loss, adverse events, residual and recurrence were collected, and case-control analysis was performed for the two groups. Differences in characteristics between the groups were tested using Fisher's exact and Wilcoxon tests.

RESULTS

Eighteen patients were included in this study: nine in ICA + ECA feeding group and nine in ECA feeding group. The median blood loss was 700 mL (IQR 550-1000 mL) in ICA + ECA feeding group versus 300 mL (IQR 200-1000 mL) in ECA feeding group, with no significant statistical difference (P = 0.306). Residual tumor was found in one patient (11.1%) in both groups. Recurrence was not observed in any patient. There were no adverse events from embolization and resection in either group.

CONCLUSION

The results of this small series suggest that the presence of blood supply from ICA branches in primary JNA has no significant effect on intraoperative blood loss, adverse event, residual and postoperative recurrence. Therefore, we do not recommend routine preoperative embolization of ICA branches.

LEVEL OF EVIDENCE

Level 4, Case-control.

Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors in nasopharyngeal carcinoma

OBJECTIVES

To evaluate the correlation between histogram parameters derived from synthetic magnetic resonance imaging (SyMRI) and prognostically relevant factors in nasopharyngeal carcinoma (NPC).

METHODS

Fifty-nine consecutive NPC patients were prospectively enrolled. Quantitative parameters (T1, T2, and proton density (PD)) were obtained by outlining the three-dimensional volume of interest (VOI) of all lesions. Then, histogram analysis of these quantitative parameters was performed and the correlations with prognostically relevant factors were assessed. By choosing appropriate cutoff, we divided the sample into two groups. Independent-samples t test/Mann-Whitney U test was used and ROC curve analysis was further processed.

RESULTS

Histogram parameters of the T1, T2, and PD maps were positively correlated with the Ki-67 expression levels, and PD_mean was the most representative parameter (AUC: 0.861). The PD map exhibited good performance in differentiating epidermal growth factor receptor (EGFR) expression levels (AUC: 0.706~0.732) and histological type (AUC: 0.650~0.660). T2_minimum was highest correlated with Epstein-Barr virus (EBV) DNA levels (r =  - 0.419), and PD_75th percentile exhibited the highest performance in distinguishing positive and negative EBV DNA groups (AUC: 0.721). T1_minimum was statistically correlated with EA-IgA expression (r =  - 0.313). Additionally, several histogram parameters were negatively correlated with tumor stage (T stage: r =  - 0.259 ~ - 0.301; N stage: r =  - 0.348 ~  - 0.456; clinical stage: r =  - 0.419).

CONCLUSIONS

Histogram parameters of SyMRI could reflect tissue intrinsic characteristics and showed potential value in assessing the Ki-67 and EGFR expression levels, histological type, EBV DNA level, EA-IgA, and tumor stage.

KEY POINTS

• SyMRI combined with histogram analysis may help clinicians to assess different prognostic factor statuses in nasopharyngeal carcinoma. • The PD map exhibited good discriminating performance in the Ki-67 and EGFR expression levels. • Histogram parameters of SyMRI were negatively correlated with EBV-related blood biomarkers and TNM stage.

Anatomic Considerations Guiding Single Versus Multiportal Endoscopic Approaches for Resection of Juvenile Nasopharyngeal Angiofibroma: Cases Series With Graded Multicorridor Resections

BACKGROUND

Juvenile nasopharyngeal angiofibromas (JNAs) are characterized by expansive and destructive growth, often invading the midline/paranasal sinuses, pterygopalatine fossa, and infratemporal fossa and can extend into the orbit, cavernous sinus, or intracranially.

OBJECTIVE

To evaluete the major benefits of the extended endoscopic endonasal approach (EEA) for JNA resection as compared with more traditional and invasive transpalatal and transfacial approaches. When JNAs extend into lateral anatomic compartments, the optimal operative trajectory often requires additional approach strategies or surgical staging.

METHODS

We retrospectively reviewed 8 cases of large JNAs arising in symptomatic adolescent boys (University of Pittsburgh Medical Center Stages II, III, and V) and discuss anatomic and tumor considerations guiding the decision of a pure EEA vs combined EEA and sublabial transmaxillary approach (Caldwell-Luc).

RESULTS

A pure extended EEA was used in 6 JNA cases (UPMC Stages II-III); a multiportal EEA + Caldwell-Luc maxillotomy was used in 2 cases. One of the 2 patients (UPMC Stage V) previously treated with multiportal EEA + Caldwell-Luc maxillotomy underwent staged left temporal/transzygomatic craniotomy, obtaining gross total resection. Seven patients ultimately underwent complete removal without recurrence. One patient with a small residual JNA (UPMC II) underwent stereotactic radiosurgery without progression to date.

CONCLUSION

JNAs with lateral extension into the infratemporal fossa often benefited from additional lateral exposure using a Caldwell-Luc maxillotomy. Cases with significant skull base and/or dural involvement may undergo staged surgical treatment; temporalis + transzygomatic craniotomy is often useful for second-stage approaches for residual tumor in these lateral infratemporal or intracranial regions. SRS should be considered for residual tumor if additional surgery is not warranted.

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.

[Diagnosis of nasopharyngeal carcinoma with convolutional neural network on narrowband imaging]

Objective:To evaluate the diagnostic accuracy of the convolutional neural network（CNN） in diagnosing nasopharyngeal carcinoma using endoscopic narrowband imaging. Methods:A total of 834 cases with nasopharyngeal lesions were collected from the People's Hospital of Guangxi Zhuang Autonomous Region between 2014 and 2016. We trained the DenseNet201 model to classify the endoscopic images, evaluated its performance using the test dataset, and compared the results with those of two independent endoscopic experts. Results:The area under the ROC curve of the CNN in diagnosing nasopharyngeal carcinoma was 0.98. The sensitivity and specificity of the CNN were 91.90% and 94.69%, respectively. The sensitivity of the two expert-based assessment was 92.08% and 91.06%, respectively, and the specificity was 95.58% and 92.79%, respectively. There was no significant difference between the diagnostic accuracy of CNN and the expert-based assessment （P=0.282, P=0.085）. Moreover, there was no significant difference in the accuracy in discriminating early-stage and late-stage nasopharyngeal carcinoma（P=0.382）. The CNN model could rapidly distinguish nasopharyngeal carcinoma from benign lesions, with an image recognition time of 0.1 s/piece. Conclusion:The CNN model can quickly distinguish nasopharyngeal carcinoma from benign nasopharyngeal lesions, which can aid endoscopists in diagnosing nasopharyngeal lesions and reduce the rate of nasopharyngeal biopsy.

Assessment of bone lesions with 18 F-FDG PET/MRI in patients with nasopharyngeal carcinoma

PURPOSE

The purpose of this study is to evaluate the role of 18 fluorodeoxyglucose ( 18 F) PET/MRI ( 18 F-FDG PET/MRI) for detecting bone metastasis in nasopharyngeal carcinoma (NPC).

PATIENTS AND METHODS

Between May 2017 and May 2021, 58 histologically proven NPC patients who underwent both 18 F-FDG PET/MRI and 99m Tc-MDP planar bone scintigraphy (PBS) for tumor staging were included. With the exception of the head, the skeletal system was classified into four groups: the spine, the pelvis, the thorax and the appendix.

RESULTS

Nine (15.5 %) of 58 patients were confirmed to have bone metastasis. There was no statistical difference between PET/MRI and PBS in patient-based analysis ( P  = 0.125). One patient with a super scan was confirmed to have extensive and diffuse bone metastases and excluded for lesion-based analysis. Of the 57 patients, all 48 true metastatic lesions were positive in PET/MRI whereas only 24 true metastatic lesions were positive in PBS (spine: 8, thorax: 0, pelvis: 11 and appendix: 5). PET/MRI was observed to be more sensitive than PBS in lesion-based analysis (sensitivity 100.0% versus 50.0 %; P  < 0.001).

CONCLUSIONS

Compared with PBS for tumor staging of NPC, PET/MRI was observed to be more sensitive in the lesion-based analysis of bone metastasis.

MRI-Based Metastatic Nodal Number and Associated Nomogram Improve Stratification of Nasopharyngeal Carcinoma Patients: Potential Indications for Individual Induction Chemotherapy

BACKGROUND

Metastatic lymph nodal number (LNN) is associated with the survival of nasopharyngeal carcinoma (NPC); however, counting multiple nodes is cumbersome.

PURPOSE

To explore LNN threshold and evaluate its use in risk stratification and induction chemotherapy (IC) indication.

STUDY TYPE

Retrospective.

POPULATION

A total of 792 radiotherapy-treated NPC patients (N classification: N0 182, N1 438, N2 113, N3 59; training group: 396, validation group: 396; receiving IC: 390).

FIELD STRENGTH/SEQUENCE

T1-, T2- and postcontrast T1-weighted fast spin echo MRI at 1.5 or 3.0 T.

ASSESSMENT

Nomogram with (model B) or without (model A) LNN was constructed to evaluate the 5-year overall (OS), distant metastasis-free (DMFS), and progression-free survival (PFS) for the group as a whole and N1 stage subgroup. High- and low-risk groups were divided (above vs below LNN- or model B-threshold); their response to IC was evaluated among advanced patients in stage III/IV.

STATISTICAL TESTS

Maximally selected rank, univariate and multivariable Cox analysis identified the optimal LNN threshold and other variables. Harrell's concordance index (C-index) and 2-fold cross-validation evaluated discriminative ability of models. Matched-pair analysis compared survival outcomes of adding IC or not. A P value < 0.05 was considered statistically significant.

RESULTS

Median follow-up duration was 62.1 months. LNN ≥ 4 was independently associated with decreased 5-year DMFS, OS, and PFS in entire patients or N1 subgroup. Compared to model A, model B (adding LNN, LNN ≥ 4 vs <4) presented superior C-indexes in the training (0.755 vs 0.727) and validation groups (0.676 vs 0.642) for discriminating DMFS. High-risk patients benefited from IC with improved post-IC response and OS, but low-risk patients did not (P = 0.785 and 0.690, respectively).

CONCLUSIONS

LNN ≥ 4 is an independent risk stratification factor of worse survival in entire or N1 staging NPC patients. LNN ≥ 4 or the associated nomogram has potential to identify high-risk patients requiring IC.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: 4.

Prognostic generalization of multi-level CT-dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma

PURPOSE

To investigate the prognostic performance of multi-level computed tomography (CT)-dose fusion dosiomics at the image-, matrix-, and feature-levels from the gross tumor volume (GTV) at nasopharynx and the involved lymph node for nasopharyngeal carcinoma (NPC) patients.

METHODS

Two hundred and nineteen NPC patients (175 vs. 44 for training vs. internal validation) were used to train prediction model, and 32 NPC patients were used for external validation. We first extracted CT and dose information from intratumoral nasopharynx (GTV_nx) and lymph node (GTV_nd) regions. Then, the corresponding peritumoral regions (RING_3 mm and RING_5 mm) were also considered. Thus, the individual and combination of intratumoral and peritumoral regions were as follows: GTV_nx, GTV_nd, RING_3 mm_nx, RING_3 mm_nd, RING_5 mm_nx, RING_5 mm_nd, GTV_nxnd, RING_3 mm_nxnd, RING_5 mm_nxnd, GTV + RING_3 mm_nxnd, and GTV + RING_5 mm_nxnd. For each region, 11 models were built by combining five clinical parameters and 127 features from: (1) dose images alone; (2-7) fused dose and CT images via wavelet-based fusion using CT weights of 0.2, 0.4, 0.6, and 0.8, gradient transfer fusion, and guided-filtering-based fusion (GFF); (8) fused matrices (sumMat); (9-10) fused features derived via feature averaging (avgFea) and feature concatenation (conFea); and finally, (11) CT images alone. The concordance index (C-index) and Kaplan-Meier curves with log-rank test were used to assess model performance.

RESULTS

The fusion models' performance was better than single CT/dose model on both internal and external validation. Models that combined the information from both GTV_nx and GTV_nd regions outperformed the single region model. For internal validation, GTV + RING_3 mm_nxnd GFF model achieved the highest C-index both in recurrence-free survival (RFS) and metastasis-free survival (MFS) predictions (RFS: 0.822; MFS: 0.786). The highest C-index in external validation set was achieved by RING_3 mm_nxnd model (RFS: 0.762; MFS: 0.719). The GTV + RING_3 mm_nxnd GFF model is able to significantly separate patients into high-risk and low-risk groups compared to dose-only or CT-only models.

CONCLUSION

Fusion dosiomics model combining the primary tumor, the involved lymph node, and 3 mm peritumoral information outperformed single-modality models for different outcome predictions, which is helpful for clinical decision-making and the development of personalized treatment.