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Background: Lymph node characteristics markedly affect nasopharyngeal carcinoma (NPC) prognosis. Matted node (MN), an important characteristic for lymph node, lacks explored MRI‐based prognostic implications. Purpose: Investigate MRI‐determined MNs' prognostic value in NPC, including 5‐year overall survival (OS), distant metastasis‐free survival (DMFS), local recurrence‐free survival (LRFS), progression‐free survival (PFS), and its role in induction chemotherapy (IC). Study Type: Retrospective cohort survival study. Population: Seven hundred ninety‐two patients with non‐metastatic NPC (female: 27.3%, >45‐year old: 50.1%) confirmed by biopsy. Field Strength/Sequence: 5‐T/3.0‐T, T1‐, T2‐ and post‐contrast T1‐weighted fast spin echo sequences acquired. Assessment: MNs were defined as ≥3 nodes abutting with intervening fat plane replaced by extracapsular nodal spread (ENS). Patients were observed every 3 months for 2 years and every 6 months for 5 years using MRI. Follow‐up extended from treatment initiation to death or final follow‐up. MNs were evaluated by three radiologists with inter‐reader reliability calculated. A 1:1 matched‐pair method compared survival differences between MN‐positive patients with or without IC. Primary endpoints (OS, DMFS, LRFS, PFS) were calculated from therapy initiation to respective event. Statistical Tests: Kappa values assessed inter‐reader reliability. Correlation between MN, ENS, and LNN was studied through Spearman's correlation coefficient. Clinical characteristics were calculated via Fisher's exact, Chi‐squared, and Student's t‐test. Kaplan–Meier curves and log‐rank tests analyzed all time‐to‐event data. Confounding factors were included in Multivariable Cox proportional hazard models to identify independent prognostic factors. P‐values <0.05 were considered statistically significant. Results: MNs incidence was 24.6%. MNs independently associated with decreased 5‐year OS, DMFS, and PFS; not LRFS (P = 0.252). MN‐positive patients gained significant survival benefit from IC in 5‐year OS (88.4% vs. 66.0%) and PFS (76.4% vs. 53.5%), but not DMFS (83.1% vs. 69.9%, P = 0.145) or LRFS (89.9% vs. 77.8%, P = 0.140). Data Conclusion: MNs may independently stratify NPC risk and offer survival benefit from IC. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Purpose: To investigate the prognostic significance of MR-detected mandibular nerve involvement (MNI) and its value for induction chemotherapy (IC) administration in patients with nasopharyngeal carcinoma (NPC) and T4 disease. Methods: This retrospective study enrolled 792 non-metastatic, biopsy-proven NPC patients. Univariate and multivariate analysis were used to evaluate potential prognosticators. The inter-observer agreement was assessed by the kappa values. Results: MR-detected MNI was observed in 141 (72.3%) patients among 195 patients with T4 disease, with excellent agreement between the readers (kappa = 0.926). Patients with MR-detected MNI presented better 5-year overall survival (OS) (hazard ratio [HR], 0.40; P = 0.006) than those with MR-negative MNI. Of these patients, IC treatment was verified as an independent factor (HR: 0.35; P = 0.014) with preferable effect on OS. Conclusion: MR-detected MNI could serve as an independent favorable prognostic predictor for OS in NPC patients with stage T4, which should be considered for stratifying these patients for IC administration. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Traditional prognostic studies utilized different cut-off values, without evaluating potential information contained in inflammation-related hematological indicators. Using the interpretable machine-learning algorithm RuleFit, this study aimed to explore valuable inflammatory rules reflecting prognosis in nasopharyngeal carcinoma (NPC) patients. Patients and Methods: In total, 1706 biopsy-proven NPC patients treated in two independent hospitals (1320 and 386) between January 2010 and March 2014 were included. RuleFit was used to develop risk-predictive rules using hematological indicators with no distributive difference between the two centers. Time-event-dependent hematological rules were further selected by stepwise multivariate Cox analysis. Combining high-efficiency hematological rules and clinical predictors, a final model was established. Models based on other algorithms (AutoML, Lasso) and clinical predictors were built for comparison, as well as a reported nomogram. Area under the receiver operating characteristic curve (AUROC) and concordance index (C-index) were used to verify the predictive precision of different models. A site-based app was established for convenience. Results: RuleFit identified 22 combined baseline hematological rules, achieving AUROCs of 0.69 and 0.64 in the training and validation cohorts, respectively. By contrast, the AUROCs of the optimal contrast model based on AutoML were 1.00 and 0.58. For overall survival, the final model had a much higher C-index than the base model using TN staging in two cohorts (0.769 vs 0.717, P< 0.001; 0.752 vs 0.688, P< 0.001), and showing great generalizability in training and validation cohorts. The two models based on RuleFit rules performed best, compared with other models. As for other endpoints, the final model showed a similar trend. Kaplan–Meier curve exhibited 22.9% (390/1706) patients were "misclassified" by AJCC staging, but the final model could assess risk classification accurately. Conclusion: The proposed final models based on inflammation-related rules based on RuleFit showed significantly elevated predictive performance. [ABSTRACT FROM AUTHOR]

Objectives: 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). Methods: 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. Results: 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). Conclusions: The risk of death increased with the number of parapharyngeal subspaces involved. The degree of PPSI is recommended to optimize T3 heterogeneity. Key Points: • 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. [ABSTRACT FROM AUTHOR]

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 < 0.05 for all). 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 [ABSTRACT FROM AUTHOR]

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 < 0.05 and 82.9% vs. 72.5%, P < 0.05, respectively) and between moderate and severe groups (81.7% vs. 70.4%, P < 0.05 and 72.5% vs. 61.2%, P < 0.05, respectively) revealed significant differences. The C-index of the nomogram with STI grading was higher compared with current T-classification (OS 0.641 vs. 0.604, P < 0.05 and PFS 0.605 vs. 0.581, P < 0.05, respectively). Significant OS differences were observed between patients with severe STI who underwent induction chemotherapy (IC) and those who did not (84.5% vs. 70.7%, P < 0.05).Data Conclusion: STI grading was an independent prognostic factor for OS and PFS in NPC patients and it may be help to improve the accuracy in predicting survival outcomes. Patients with severe STI might benefit from IC to improve OS.Level Of Evidence: 4 TECHNICAL EFFICACY: Stage 2. [ABSTRACT FROM AUTHOR]

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 < 0.05). N1 patients with nodal matting had worse DMFS than N1 patients without (P < 0.05). However, no significant difference was observed when comparing N1 patients with nodal matting to N2 patients (P = 0.464). The C‐index of the nomogram with nodal matting was higher than the nomogram without (0.717 vs. 0.699, P = 0.084). Data Conclusion: Nodal matting was an independent prognostic factor for DMFS in NPC patients. It may help to select patients at high risk of distant metastasis. [ABSTRACT FROM AUTHOR]

• The developed MLNs counting model demonstrated acceptable ability in NPC. • LNNs counted by AMLNC showed favorable discriminatory in predicting prognostic. • AMLNC may serve as a reference tool for younger radiologists to evaluate MLNs. The number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. We retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). In the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1–4, 5–9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. The AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection. [ABSTRACT FROM AUTHOR]

• Rulefit enables the utilization of logical associations behind structured MRI report for nasopharyngeal carcinoma in the forms of prognostic rules. • The Rules model using Rulefit achieved a better accuracy and generalizability than common models for the prediction of overall survival among advanced nasopharyngeal carcinoma, with the highest C-index of 0.711 in external testing cohort. • Significant survival benefit was achieved in high-risk group stratified by Rules model with additional induction chemotherapy treatment. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

• The SeqSeg addresses the problem of background dominance to achieve accurate NPC detection and segmentation. • Intelligent deep Q-learning-based nasopharyngeal carcinoma detection model is proposed. • Experiments on a large dataset from multi-device and multi-center demonstrate the reliability of the proposed method. • Efficient reward function and exploration strategy boost NPC detectio performance. • Recurrent attention and dilated border weighted loss function foster NPC segmentation. [Display omitted] 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. [ABSTRACT FROM AUTHOR]