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Nasopharyngeal carcinoma (NPC) is a malignant tumor whose survivability is greatly improved if early diagnosis and timely treatment are provided. Accurate segmentation of both the primary NPC tumors and metastatic lymph nodes (MLNs) is crucial for patient staging and radiotherapy scheduling. However, existing studies mainly focus on the segmentation of primary tumors, eliding the recognition of MLNs, and thus fail to comprehensively provide a landscape for tumor identification. There are three main challenges in segmenting primary NPC tumors and MLNs: variable location, variable size, and irregular boundary. To address these challenges, we propose an automatic segmentation network, named by NPCNet, to achieve segmentation of primary NPC tumors and MLNs simultaneously. Specifically, we design three modules, including position enhancement module (PEM), scale enhancement module (SEM), and boundary enhancement module (BEM), to address the above challenges. First, the PEM enhances the feature representations of the most suspicious regions. Subsequently, the SEM captures multiscale context information and target context information. Finally, the BEM rectifies the unreliable predictions in the segmentation mask. To that end, extensive experiments are conducted on our dataset of 9124 samples collected from 754 patients. Empirical results demonstrate that each module realizes its designed functionalities and is complementary to the others. By incorporating the three proposed modules together, our model achieves state-of-the-art performance compared with nine popular models.

Objectives Prognoses for nasopharyngeal carcinoma (NPC) between categories T2 and T3 in the Eighth American Joint Committee on Cancer (AJCC) staging system were overlapped. We explored the value of skull base invasion (SBI) subclassification in prognostic stratification and use of induction chemotherapy (IC) to optimize T2/T3 categorization for NPC patients. Methods We retrospectively reviewed 1752 NPC patients from two hospitals. Eight skull base bone structures were evaluated. Survival differences were compared between slight SBI (T3 patients with pterygoid process and/or base of the sphenoid bone invasion only) and severe SBI (T3 patients with other SBIs) with or without IC using random matched-pair analysis. We calculated the prognosis and Harrel concordance index (C-index) for the revised T category and compared IC outcomes for the revised tumor stages. Results Compared to severe SBI, slight SBI showed better 5-year overall survival (OS) (81.5% vs. 92.3%, p = 0.001) and progression-free survival (PFS) (71.5% vs. 83.0%, p = 0.002). Additional IC therapy did not significantly improve OS and PFS in slight SBI. The proposed T category separated OS, PFS, and locoregional recurrence-free survival in T2 and T3 categories with statistical significance. An improved C-index for OS prediction was observed in the proposed T category with combined confounding factors, compared to the AJCC T staging system (0.725 vs. 0.713, p = 0.046). The survival benefits of IC were more obvious in the advanced stage. Conclusions NPC patients with slight SBI were recommended to downstage to T2 category. The adjustment for T category enabled better prognostic stratification and guidance for IC use. Key Points • For nasopharyngeal carcinoma (NPC) patients in T3 category, slight skull base invasion was a significant positive predictor for OS and PFS. • NPC patients with slight SBI might not gain significant survival benefits from induction chemotherapy. • Downstaging slight SBI NPC patients to T2 category would make a more accurate risk stratification, improve the predicting performance in OS, and have a better guidance in the use of IC for patients in advanced stage.

Summary Background & aim Several studies explored the association of vitamin B6 intake with the risk of cancers. However, it is unclear whether different doses of vitamin B6 have distinct effects on the prognosis of nasopharyngeal carcinoma (NPC) patients. This study investigated the relationship between different doses of B6 intake and the prognosis of NPC patients. Methods This retrospective cohort analysis included 792 newly diagnosed NPC patients with a median follow-up of 62.05 months. Restricted cubic spline and maximally selected rank statistics were performed to determine the cut-off value of vitamin B6 during treatment (VB6DT). Kaplan–Meier method and log-rank tests were performed to analyze survival outcomes. A multivariable Cox proportional hazard model was performed to determine the independent prognostic factors. Results NPC patients were divided into three groups according to the cut-off value of VB6DT: non-users (0 mg/d), VB6DT > 8.6 mg/d, and VB6DT ≤ 8.6 mg/d. Patients with VB6DT > 8.6 mg/d had significantly lower 5-year overall survival (OS) (83.5% vs. 90.8%, p = 0.006), distant metastasis-free survival (DMFS) (83.5% vs. 91.0%, p = 0.004), and progression-free survival (PFS) (73.7% vs. 81.7%, p = 0.011) and slightly but not significantly lower 5-year local recurrence-free survival (LRFS) (87.7% vs. 90.7%, p = 0.214) than the non-users. Patients with VB6DT ≤ 8.6 mg/d had slightly but not significantly better 5-year OS (93.3% vs. 90.8%, p = 0.283) than the non-users, while all other primary endpoints were similar (p > 0.50). Multivariable analyses confirmed that VB6DT > 8.6 mg/d was an independent negative prognostic factor of OS (p = 0.010), DMFS (p = 0.017), and PFS (p = 0.030) but not of LRFS (p = 0.428). Conclusions Excessive VB6DT higher than the cut-off value is an independent negative prognostic factor for NPC patients. Additionally, low dose intake improved OS only slightly but not significantly.

BACKGROUND Soft tissue involvement (STI) indicates poor prognosis in nasopharyngeal carcinoma (NPC). However, only a few studies have systematically assessed this extension using network analysis. PURPOSE To investigate the prognostic value of STI and to propose an improved STI grading system for NPC therapy. STUDY TYPE Retrospective study. POPULATION A total of 1225 consecutive patients with pathologically confirmed NPC treated with intensive-modulated radiotherapy from January 2010 to March 2014 were enrolled from two centers. FIELD STRENGTH/SEQUENCE T1- and T2-weighted imaging and enhanced T1-weighted imaging with fast spin echo sequence at 1.5 or 3.0 T. ASSESSMENT The levator veli palatini and tensor veli palatini involvement were graded "mild," prevertebral muscle involvement, "moderate," medial pterygoid, lateral pterygoid, and the infratemporal fossa involvement, "severe" STI. The above STI sites were evaluated separately by three radiologists using MRI images and graded using network analysis. Overall survival (OS) and progression-free survival (PFS) were assessed. STATISTICAL TESTS Kaplan-Meier method, Cox's proportional hazards model, and concordance index (C-index) were used. RESULTS Five-year OS and PFS rates between mild and moderate groups (90.5% vs. 81.7%, P

Do nasopharyngeal carcinoma (NPC) patients benefit from induction chemotherapy (IC)? This problem is of great clinical interest; however, it is difficult to obtain an accurate and interpretable model to inform IC decisions for NPC patients. In this study, a time-to-event supervised genetic algorithm was developed to obtain an IC decision-making model for NPC patients. In this algorithm, the fitness function is directly related to the time-to-event, which reflects the IC therapeutic effect for NPC. Then, the optimal models are obtained by stability and validation analysis. The comprehensive clinical model is determined by comprehensive feature analysis using the “or” operation. The overall survival for non-IC vs. IC patients in the potential benefit group was 63.4% vs. 81.5%, with p = 0.020, and the comprehensive clinical model exhibited good generalization ability. However, the benefits of OS according to the current NCCN guidelines are limited (p > 0.05). None of the possible processes of LASSO we tried could obtain the significant models validated in the testing cohort. The proposed method provides an interpretable model construction process, reasonable data grouping strategy, concise experimental design, and convenient clinical application. Moreover, we will develop a toolkit for the treatment decision-making model research to facilitate the use of clinicians and provide technical support for precision medicine.

Objectives We aimed to reconstitute T2 and T3 stage classification in nasopharyngeal carcinoma (NPC) cases and verify its utility in clinical settings. Materials and Methods We enrolled 792 NPC patients. Cox proportional hazards model was used to compare the effect sizes (hazard ratio [HR]) of the cranial structure invasion on survival and select the structures for up‐staging or downstaging T2 and T3 NPC. The samples were reclassified and the survival curves for T2 and T3 stages were analyzed. The proposed new staging system was validated on an external sample (n = 433). Results Thirteen cranial structures were examined. American Joint Committee on Cancer (AJCC) T3 stage patients with the invasion of the base of the sphenoid (HR = 2.58, 95% CI = 1.16‐5.77) or base of the pterygoid (HR = 2.00, 95% CI = 0.84‐4.77) had significantly lower hazard ratios than T2 stage patients with the invasion of soft tissues in the bilateral parapharyngeal space (HR = 5.26, 95% CI = 2.02‐13.68) and single/bilateral carotid sheath (HR = 7.78, 95% CI = 3.06‐19.76). T3 stage with the invasion of the above‐mentioned bones was reclassified as T2, and T2 stage with the invasion of the above‐mentioned soft‐tissue structures was reclassified as T3. Survival analysis showed a significant difference between the reclassified T2 and T3 stages (P, Hazard ratio was lower for AJCC T3 with bone than AJCC T2 with soft tissue invasion ·The 5‐year OS and PFS were higher for re‐classified T2 than T3 stage NPC patients·Modified staging system for T2 and T3 NPC may result in better management.

BACKGROUND Nodal (N) stage is one of the most important predictors for distant metastasis in nasopharyngeal carcinoma (NPC) patients. It may ignore potentially useful nodal features, such as nodal matting (three or more lymph nodes abutting together with the absence of intervening fat planes). PURPOSE To explore the prognostic value of nodal matting in NPC patients and construct a nomogram with nodal matting for predicting distant metastasis-free survival (DMFS). STUDY TYPE Retrospective. POPULATION In all, 792 NPC patients treated with intensity modulated radiation therapy from 2010 to 2013 were enrolled with 2:1 training (n = 527) and validation (n = 65) cohorts. FIELD STRENGTH/SEQUENCE T1 - and T2 -weighted imaging at 1.5 or 3.0T. ASSESSMENT Nodal matting and other nodal characteristics were assessed with MRI. MR images were evaluated separately by three radiologists. The association between nodal matting and DMFS was analyzed. STATISTICAL TESTS Univariate and multivariate analyses were performed using the Cox proportional hazards regression model. Nomograms were constructed from a multivariate logistic regression model with and without nodal matting. The predictive accuracy and discriminative ability of the nomograms were determined by concordance index (C-index) and calibration curves. The results were validated using bootstrap resampling and validation cohort. RESULTS The incidence of nodal matting was 24.6% (195/792) in all patients. In the training cohort, nodal matting was independently associated with DMFS (hazard ratio [HR] = 1.97 [1.05-3.69], P

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification system for NPC, superior to the current clinical staging system. Between January 2010 and January 2013, 792 patients with recent diagnosis of NPC, who had MR image data, were enrolled in the study. The AutoML algorithm was used and all statistical analyses were based on the 10-fold test. Primary endpoints included the probabilities of overall survival (OS), distant metastasis-free survival (DMFS), and local-region relapse-free survival (LRFS), and their sum was recorded as the final voting score, representative of progression-free survival (PFS) for each patient. The area under the receiver operating characteristic (ROC) curve generated from the MR image data-based model compared with the tumor, node, and metastasis (TNM) system-based model was 0.796 (P=0.008) for OS, 0.752 (P=0.053) for DMFS, and 0.721 (P=0.025) for LRFS. The Kaplan-Meier (KM) test values for II/I, III/II, IV/III groups in our new machine learning-based scoring system were 0.011, 0.010, and P<0.05), whereas the P values of curves obtained from the TNM/AJCC system, between II/I and III/II, were 0.118 and 0.121, respectively, without a significant difference. In conclusion, the AutoML algorithm demonstrated better prognostic performance than the TNM/AJCC system for NPC. The algorithm showed a good potential for clinical application and may aid in improving counseling and facilitate the personalized management of patients with NPC. The clinical application of our new scoring and staging system may significantly improve precision medicine.

The carotid space is an integral part of the parapharyngeal space, with ambiguous prognostic value for patients with nasopharyngeal carcinoma (NPC). This study aimed to investigate the prognostic significance of carotid space involvement (CSI) and propose a treatment strategy.This retrospective study enrolled 792 patients with biopsy-confirmed, non-distant metastatic NPC staged by magnetic resonance imaging before treatment. We used multivariable Cox regression models and Kaplan-Meier methods to assess the association between the variables and survival outcomes. A matched-pair method (1:1) was used to compare the survival differences between the patients with CSI treated with induction chemotherapy (ICT)and that of those who were not.The incidence rate of CSI was 21.7 % (172/792). Multivariate analysis revealed that CSI was not an independent prognostic factor for survival outcomes in the 792 patients with NPC; however, the Chi-square test showed a different distribution of treatment strategies with ICT for patients with and without CSI. After stratification by ICT, CSI was an independent prognostic factor for overall survival (OS) (p = 0.049) in patients without ICT, but not for distant metastasis-free, local recurrence-free, or progression-free survival (p˃0.05). Additionally, ICT improved OS in patients with CSI (hazard ratio, 0.42; p = 0.019). Matched pair analysis showed that patients with CSI gained prolonged OS from ICT compared with the non-ICT group (88.4 % vs 69.4 %, p = 0.028).CSI was an independent negative prognostic factor for OS in patients with NPC without ICT and might be an imaging marker for identifying eligible candidates for ICT.

This study aimed to assess the prognostic value of quantitative cervical nodal necrosis (CNN) burden in N staging risk stratification in patients with nasopharyngeal carcinoma.Univariate and multivariate Cox regression models evaluated the association between lymph node variables based on MRI images and survival. Revisions for the N classification system were proposed and compared to the 8th edition AJCC staging system using Harrell's concordance index (C-index). The survival outcomes of induction chemotherapy plus concurrent chemoradiotherapy (CCRT) and CCRT alone in patients with multiple CNNs were compared.In 1319 patients enrolled, CNN was not an independent prognostic factor for the main survival outcomes, but multiple CNNs (three or more necrotic nodes) were independent prognostic factors for distant metastasis-free survival (DMFS) (adjusted hazard ratio [HR], 2.05; p = 0.020) and progression-free survival (PFS) (HR, 1.78; p = 0.004), surpassing other nodal variables. On upgrading patients with multiple CNNs to revised N3 disease, the proposed N staging widened the differences in DMFS and PFS between N2 and N3 disease. The overall survival of patients with multiple CNNs who received CCRT plus induction chemotherapy was improved compared to that of those who received CCRT alone (76.1% vs. 55.7%; adjusted p = 0.030).Upgrading patients with multiple CNNs to stage N3 may improve prognostication of the current AJCC staging system. Multiple CNNs might be a potential marker for stratifying patients who would benefit from induction chemotherapy.• Quantitatively assessed the prognostic value of CNN burden in patients with NPC. • Upgrading patients with multiple CNNs to stage N3 may improve prognostication. • Multiple CNNs may be used as a stratification marker for induction chemotherapy.

To investigate the prognostic significance of paranasal sinus invasion in nasopharyngeal carcinoma (NPC) patients.Patients with NPC after intensity-modulated radiotherapy from 2010 to 2013 were identified (n = 1225). Clinical features and magnetic resonance images were analyzed.Paranasal sinus invasion was identified in 182/1225 patients (14.9%). Multivariate analysis showed that paranasal sinus invasion was an independent factor for overall survival, progression-free survival, distant metastasis-free survival, and local recurrence-free survival (P .05 for all). T3 NPC patients with paranasal sinus invasion had a poorer prognosis than those without (P .05), and there was no significant survival difference compared with T4 patients, regardless of involvement of inner structures (P .05 for all). Upgrading NPC with paranasal sinus invasion to T4 disease achieved better predictive abilities.Paranasal sinus invasion is an independent prognostic factor for NPC. It may be appropriate to upgrade the T classification.

Background Accurately predicting the risk of death, recurrence, and metastasis of patients with nasopharyngeal carcinoma (NPC) is potentially important for personalized diagnosis and treatment. Survival outcomes of patients vary greatly in distinct stages of NPC. Prognostic models of stratified patients may aid in prognostication. Purpose To explore the prognostic performance of MRI-based radiomics signatures in stratified patients with NPC. Study type Retrospective. Population Seven hundred and seventy-eight patients with NPC (T1-2 stage: 298, T3-4 stage: 480; training cohort: 525, validation cohort: 253). Field strength/sequence Fast-spin echo (FSE) axial T1-weighted images, FSE axial T2-weighted images, contrast-enhanced FSE axial T1-weighted images at 1.5 T or 3.0 T. Assessment Radiomics signatures, clinical nomograms, and radiomics nomograms combining the radiomic score (Radscore) and clinical factors for predicting progression-free survival (PFS) were constructed on T1-2 stage patient cohort (A), T3-4 stage patient cohort (B), and the entire dataset (C). Statistical tests Least absolute shrinkage and selection operator (LASSO) method was applied for radiomics modeling. Harrell's concordance indices (C-index) were employed to evaluate the predictive power of each model. Results Among 4,410 MRI-extracted features, we selected 16, 16, and 14 radiomics features most relevant to PFS for Models A, B, and C, respectively. Only 0, 1, and 4 features were found overlapped between models A/B, A/C, and B/C, respectively. Radiomics signatures constructed on T1-2 stage and T3-4 stage patients yielded C-indices of 0.820 (95% confidence interval [CI]: 0.763-0.877) and 0.726 (0.687-0.765), respectively, which were larger than those on the entire validation cohort (0.675 [0.637-0.713]). Radiomics nomograms combining Radscore and clinical factors achieved significantly better performance than clinical nomograms (P Data conclusion The selected radiomics features and prognostic performance of radiomics signatures differed per the type of NPC patients incorporated into the models. Radiomics models based on pre-stratified tumor stages had better prognostic performance than those on unstratified dataset. Level of evidence 4 Technical Efficacy Stage: 5.

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets.

To identify the prognosis of parapharyngeal space involvement (PPSI) based on the number of subspaces involved (pre-styloid space, carotid space (CS), areas outside the CS) and explore its significance for current T-staging in patients with nasopharyngeal carcinoma (NPC). PPSI was retrospectively identified in 1224 patients with non-disseminated NPC at two centers on MRI and separated into four invasion patterns: pattern A (only post-styloid space), pattern B (post-styloid space, CS extension), pattern C (post-styloid space, pre-styloid space extension), and pattern D (all spaces). The Kaplan–Meier analysis and multivariate Cox regression models were used. PPSI was diagnosed in 63.4% of cases, with patterns A, B, C, and D in 14.3%, 3.8%, 25.3%, and 18.6% of cases, respectively. No prognostic heterogeneity was observed between pattern B and pattern C (p > 0.05). Thus, the degree of PPSI was based on the number of subspaces involved: grade 0 (none), grade 1 (one), grade 2 (two), and grade 3 (three), which could independently predict overall survival (OS) (p < 0.001). T3 patients with grade 0/1 PPSI (slight-T3) had a better prognosis than those with grade 2/3 PPSI (severe-T3) in terms of OS, locoregional-free survival (LRFS), and progression-free survival (PFS) (all p < 0.001), whose hazard ratios were higher and lower than those with T1 and T2, respectively. Combining the T2 and slight-T3 groups as the proposed T2 provided significant differences in OS, LRFS, and PFS between T2 and T3 (all p < 0.05). The risk of death increased with the number of parapharyngeal subspaces involved. The degree of PPSI is recommended to optimize T3 heterogeneity. • Parapharyngeal space involvement was proposed to differentiate patient risk groups based on the number of involved subspaces: grade 0 (none), grade 1 (one), grade 2 (two), or grade 3 (three). • The degree of parapharyngeal space involvement was an independent negative prognosticator for OS. • The degree of parapharyngeal space involvement may influence T-staging in patients with nasopharyngeal carcinoma.

Distant metastasis is among the main reasons for treatment failure in nasopharyngeal carcinoma (NPC) patients. To identify patients with a high risk of distant metastasis is important to guide posttreatment surveillance, appropriate time treatments, and prolonging their long-term survival. In this study, we systematically examined the associations between a series of nodal-related characteristics and distant metastasis-free survival (DMFS) by detailed MRI reading and established a nomogram for DMFS in NPC patients. T-stage, age group, Epstein-Barr virus (EBV) level, central nodal necrosis, and nodal number were identified as independent risk factors for distant metastasis and were included into the final nomogram. The calibration plot showed a high agreement between the prediction by the nomogram and actual observations. Our established nomogram achieved a high C-index in predicting distant metastasis in both of the training cohort (0.737) and the validation cohort (0.718). This nomogram incorporated several readily available nodal features from the MR images, and it might be useful for guiding clinical decision and NPC patients' posttreatment surveillance. It also provides cues for how to redefine N-stage. Additional research is needed to confirm our conclusions.

Objectives Weight loss has been validated as a prognostic predictor of nasopharyngeal carcinoma (NPC); however, no global unitary indicator and criteria exist for the definition of weight loss as a prognostic factor. The aim of this study was to determine the most effective indicator for weight loss, evaluate its effect on the prognosis of NPC, and further propose a cutoff value to identify patients in need of nutritional care. Methods This retrospective cohort analysis with a median follow-up of 62.3 mo included 681 newly diagnosed patients with NPC. Principal component analysis was performed to select the best continuous variable including weight loss (kg; value of weight loss [VWL]), percent weight loss (PWL), and body mass index loss (BMIL). Multivariable Cox regression analysis and multiple correspondence analysis were performed to select the best cutoff values by different cutoff methods including the median, receiver operating characteristic curve, and threshold searching. Results PWL was the highest contributor to the prognosis of NPC compared with VWL and BMIL. Cutoff values of PWL (6.3 and 12.3%) were confirmed to be more important and were proposed to differentiate patients into low-, medium-, and high-risk NPC groups, with their 5-y progression-free survival (84.5 versus 77.9%, P = 0.046; 77.9 versus 67.3%, P = 0.046). PWL was an independent adverse prognostic factor (P = 0.002) for NPC. Conclusions PWL is a promising predictor for NPC, and cutoff values could be validated for nutritional risk grading in patients with NPC. These stratified criteria may help accelerate the extensive application of grading nutritional management in NPC therapy.

Background and Purpose The number of metastatic lymph nodes (LNs) has been demonstrated to be an important prognosticator in some head and neck cancers. This study aimed to examine its prognostic value in nasopharyngeal carcinoma (NPC). Materials and Methods Patients with newly-diagnosed, non-distant metastatic NPC from two centers were enrolled in this study. According to the radiologic criteria for involved LNs, the number of positive LNs was assessed on MRI. Univariate and multivariate models were constructed to assess the association between the number of positive LNs and clinical outcomes. Exhaustive method was used to determine the cutoff values of the number of positive LNs. Results In 1224 patients, the number of MRI-positive LNs was an independent risk factor for overall survival (OS), distant metastasis-free survival (DMFS), relapse-free survival (RFS), progression-free survival (PFS) and regional relapse-free survival (RRFS), surpassing other nodal factors. All the patients were divided into no, low (1–4 positive LNs), moderate (5–9 positive LNs) and high (>9 positive LNs) risk groups. The HRs of high-risk group exceeded the HRs of N3 classification for OS, DMFS, RFS, PFS and RRFS. On upgrading patients with more than 9 positive LNs from N2 to N3, the revised N-staging system showed a higher C-index compared to current N-staging system for predicting OS (0.747 vs. 0.741, P = 0.002), RFS (0.655 vs. 0.640, P = 0.015) and PFS (0.674 vs. 0.669, P = 0.035). Conclusions The number of MRI-positive LNs was a predominant independent prognostic factor for NPC patients’ survival. It may be incorporated into the future N-staging system to improve its accuracy in predicting outcomes.

This study aimed to evaluate the value of nodal grouping (NG), defined as the presence of at least three contiguous lymph nodes (LNs) within one LN region, in staging and management of patients with non-metastatic nasopharyngeal carcinoma (NPC). MR images were reviewed to evaluate LN variables, including NG. The Kaplan–Meier method and multivariate Cox regression models evaluated the association between the variables and survival. Harrell’s concordance index (C-index) was used to measure the performance of prognostic models. The outcome of induction chemotherapy (IC) in patients with and without NG was compared using matched-pair analysis. In 1224 patients enrolled, NG was found to be an independent prognostic factor for overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), and regional recurrence-free survival. The hazard ratio and 95% confidence interval (CI) of NG for OS (3.86, 2.09–7.12) were higher than those of stage N2 (3.54, 1.89–6.70). On upgrading patients with NG from stages N1 to N2, the revised N staging yielded a higher C-index compared to the American Joint Committee on Cancer system in predicting PFS (0.664 vs. 0.658, p = 0.022) and DMFS (0.699 vs. 0.690, p = 0.005). Results of the matched-pair analysis revealed that for patients with NG in stages N1 and N2, IC was correlated with improved OS (p = 0.022), PFS (p = 0.007), and DMFS (p = 0.021). NG is a significant prognostic factor for patients with NPC. Patients with NG may be upgraded from stages N1 to N2. NG was also a marker for identifying patients who would benefit from IC. • Nodal grouping, defined as the presence of at least three contiguous LNs within one LN region on MRI, was identified as a significant prognostic factor. • In patients with nasopharyngeal carcinoma, nodal grouping may influence lymph node staging. • Nodal grouping was a marker for identifying patients who may benefit from induction chemotherapy.

Automatic nasopharyngeal carcinoma (NPC) segmentation in magnetic resonance (MR) images remains challenging since NPC is infiltrative and typically has a small or even tiny volume, making it indiscernible from tightly connected surrounding tissues. Recent methods using deep learning models performed unsatisfactorily since the boundary between NPC and its neighbor tissues is difficult to distinguish. In this paper, a novel Convolutional Neural Network (CNN) with recurrent attention modules (RAMs) is proposed to tackle the problem. To enhance the performance of NPC segmentation, the proposed fully automatic NPC segmentation method with recurrent attention exploits the semantic features in higher layers to guide the learning of features in lower layers. Features are fed into RAMs iteratively from the higher layers to the lower ones. The lower layers are updated iteratively by the guidance of higher layers to render with discriminative capability. Our proposed method was validated in a dataset including 596 patients, experimental results demonstrate that our method outperforms state-of-the-art methods.

To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI.A total of 658 patients (training cohort: 424; validation cohort: 234) with non-metastatic NPC were enrolled in the retrospective analysis. Each slice was considered as a sample and 4863 radiomics features on the tumor region were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI. Consensus clustering and manual aggregation were performed on the training cohort to generate a baseline model and classification reference used to train a support vector machine classifier. The risk of each patient was defined as the maximum risk among the slices. Each patient in the validation cohort was assigned to the risk model using the trained classifier. Harrell's concordance index (C-index) was used to measure the prognosis performance, and differences between subgroups were compared using the log-rank test.The training cohort was clustered into four groups with distinct survival patterns. Each patient was assigned to one of the four groups according to the estimated risk. Our method gave a performance (C-index = 0.827, p .004 and C-index = 0.814, p .002) better than the T-stage (C-index = 0.815, p = .002 and C-index = 0.803, p = .024), competitive to and more stable than the TNM staging system (C-index = 0.842, p = .003 and C-index = 0.765, p = .050) in the training cohort and the validation cohort.Through investigating a large one-institutional cohort, the quantitative multi-modalities MRI image phenotypes reveal distinct survival subtypes.• Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.

To examine the morphological changes of the pituitary glands and linear growth of childhood nasopharyngeal carcinoma (NPC) cases who accepted radiotherapy. A total of 90 children (i.e., age less than 18 years) who were diagnosed as NPC at Sun Yat-sen University Cancer Center from January 2009 to January 2016 were identified by reviewing medical records. Two radiologists reviewed and measured the pre-radiation, post-radiation, and the latest available pituitary gland heights independently. Patients' current height information was collected by telephone interviews. We compared the pituitary height differences using paired t-tests and estimated the pituitary height trajectories within each sex by mixed regression models. Height-for-age Z-score was calculated for each patient using the WHO growth reference data for 5–19 years as reference. Most of the included participants were of male sex (75.6%) and over half were diagnosed at stage IV (58.4%). Among the 90 included participants, 89 had one repeated measurement of the pituitary height and 79 had two repeated measurements of the pituitary height. Seventy six of the 89 childhood NPC participants had reduced pituitary heights after radiation and accounted for 85.4% of the whole population. The means of the pituitary heights before and after radiotherapy were 6.4 ± 1.3 mm and 5.6 ± 1.2 mm (P < 0.001), respectively. The mean of height-for-age Z-score for childhood NPC cases was significantly below zero (−0.54, 95% CI = −0.74, −0.34). We concluded that childhood NPC cases had decreased pituitary heights and stunted linear growth after radiotherapy.

Purpose To establish effective prognostic nomograms using clinical features and detailed magnetic resonance imaging (MRI) findings for primary tumor and regional lymph nodes after intensity-modulated radiotherapy (IMRT) in patients with nasopharyngeal carcinoma. Method The nomogram for overall survival (OS) was based on a retrospective study of 595 patients who underwent IMRT at Sun Yat-sen University Cancer Center from 2010 to 2012. The predictive accuracy and discriminative ability of our nomogram models were determined by concordance index and calibration curve, and were compared with the nomogram models combining clinical features with tumor-node-metastasis (TNM) classification. The results were validated using bootstrap resampling and a cohort study of 241 patients. The same data cohort was used to predict the progress-free survival (PFS) of nasopharyngeal carcinoma with 3:1 training cohort (N = 558) and validation cohort (N = 278). Results The following factors were assembled into our prognostic survival nomogram models: Age, Epstein–Barr virus DNA copy number before treatment (EBV_DNA_CN), tensor veli palatini (TVP) involvement, musculus pterygoideus lateralis (MPL) involvement, prestyloid space (PS) involvement, prevertebral space (PVS) involvement, base of sphenoid bone (BOSB) involvement, paranasal sinus (PNS) involvement, the laterality of Ⅱ (Ⅱ_laterality), the laterality of retropharyngeal lymph node (RPLN_laterality), nodal grouping (NG), extranodal neoplastic spread (ENS), contrast-enhancing rim (CER) and Nodal_number. The calibration curves showed good agreement between nomogram-predicted and actual survival. Our nomogram models for OS and PFS, provided better results than the nomogram models combining clinical features with TNM classification. Results were further confirmed in the validation set. Conclusion Detailed MRI findings of primary tumor and regional lymph nodes can improve the performance of prognostic nomograms for patients with nasopharyngeal carcinoma.