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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: 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]

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]

Objectives: 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.Materials and Methods: 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.Results: 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).Conclusion: 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. [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]