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To compare the prognosis and adverse effects of induction or adjuvant chemotherapy (IC or AC) plus concurrent chemoradiotherapy (CCRT) versus CCRT alone in paediatric nasopharyngeal carcinoma (NPC) patients in the intensity-modulated radiotherapy (IMRT) era. 549 patients diagnosed from 2005 to 2021 were enrolled. Our primary endpoint was progression-free survival (PFS). The recursive partitioning analysis (RPA) was applied to derive a risk stratification system. Kaplan–Meier survival curves were used to assess the cumulative survival rates, and cox analysis was applied to evaluate the relationship between variables and endpoints. The RPA-based risk stratification identified three different risk groups. In the intermediate-risk (stage IVa and EBV<4000 copies/ml) group, patients who received IC followed by CCRT achieved a significantly better 3-year PFS rate than those treated with CCRT alone (87.35% versus 75.89%; P = 0.04). But survival benefit was not obtained from the additional IC or AC in the low-risk (stage II-III and EBV<4000 copies/ml) or high-risk (stage II-IVa and EBV≥4000 copies/ml) group. The most common grade 3 or 4 adverse events in patients treated with CCRT, IC + CCRT, and CCRT + AC were neutropenia (8.1%, 33.0% versus 36.9%, respectively) and leukopenia (14.1%, 26.8% versus 32.3%, respectively) with statistically significant difference. Paediatric NPC patients in the intermediate-risk group treated with IC followed by CCRT had significantly better PFS compared with patients treated with CCRT alone. And the overall incidence of acute adverse events in patients treated with IC or AC plus CCRT was higher than in patients treated with CCRT alone. • EBV DNA was independently associated with risk stratification in paediatric NPC. • Paediatric NPC patients with intermediate-risk could benefit from the additional IC. • Overall incidence of AE in paediatrics receiving IC/AC + CCRT was significantly higher. [ABSTRACT FROM AUTHOR]

• A total of 531 patients with local recurrence nasopharyngeal carcinoma (lrNPC) were retrospectively reviewed. • We established and validated an integrated prognostic model for lrNPC patients. • Three risk groups were derived from an RPA model that combined rT stage and EBV DNA. • Re-radiotherapy could benefit patients in the low and intermediate-risk subgroups. • No association between re-RT and survival benefit was found in the high-risk. subgroup. In this study, we aimed to establish and validate an integrated prognostic model for locally recurrent nasopharyngeal carcinoma (lrNPC) patients, and evaluate the benefit of re-radiotherapy (re-RT) in patients with different risk levels. In total, 531 patients with lrNPC were retrospectively reviewed in this study, including 271 patients from 2006 to 2012 as the training cohort and 260 patients from 2013 to 2016 as the validation cohort. Overall survival (OS) was the primary endpoint. Multivariate analysis was performed to select the significant prognostic factors (P < 0.05). A prognostic model for OS was derived by recursive partitioning analysis (RPA) combining independent predictors using the algorithm of optimized binary partition. Three independent prognostic factors (age, relapsed T [rT] stage, and Epstein-Barr virus [EBV] DNA) were identified from multivariate analysis. Five prognostic groups were derived from an RPA model that combined rT stage and EBV DNA. After further pair-wise comparisons of survival outcome in each group, three risk groups were generated. We investigated the role of re-RT in different risk groups, and found that re-RT could benefit patients in the low (P < 0.001) and intermediate-risk subgroups (P = 0.017), while no association between re-RT and survival benefit was found in the high-risk subgroup (P = 0.328). The results of risk stratification and re-RT efficacy were verified in the validation cohort. Age, rT stage and EBV DNA were identified as independent predictors for lrNPC. We established an integrated RPA-based prognostic model for OS incorporating rT stage and EBV DNA, which could guide individual treatment for lrNPC. [ABSTRACT FROM AUTHOR]

Background: Distinguishing patients at a greater risk of recurrence is essential for treating locoregional advanced nasopharyngeal carcinoma (NPC). This study aimed to explore the potential of aldo-keto reductase 1C4 (AKR1C4) in stratifying patients at high risk of locoregional relapse.Methods: A total of 179 patients with locoregionally advanced NPC were grouped by different strategies; they were: (a) divided into two groups according to AKR1C4 expression level, and (b) classified into three clusters by integrating AKR1C4 and Epstein-Barr virus (EBV) DNA. The Kaplan-Meier method was used to calculate locoregional relapse-free survival (LRFS), overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS). The Cox proportional hazards model was used to determine potential prognostic factors, and a nomogram was generated to predict 3-year and 5-year LRFS.Results: A significant difference in the 5-year LRFS was observed between the high and low AKR1C4 expression groups (83.3% vs. 92.7%, respectively; p = 0.009). After integrating AKR1C4 expression and EBV DNA, the LRFS (84.7%, 84.5%, 96.9%, p = 0.014) of high-, intermediate-, and low- AKR1C4 and EBV DNA was also significant. Multivariate analysis indicated that AKR1C4 expression (p = 0.006) was an independent prognostic factor for LRFS. The prognostic factors incorporated into the nomogram were AKR1C4 expression, T stage, and EBV DNA, and the concordance index of the nomogram for locoregional relapse was 0.718.Conclusions: In conclusion, high AKR1C4 expression was associated with a high possibility of relapse in NPC patients, and integrating EBV DNA and AKR1C4 can stratify high-risk patients with locoregional recurrence. [ABSTRACT FROM AUTHOR]

Purpose: How to discriminate different risks of recurrent nasopharyngeal carcinoma (rNPC) patients and guide individual treatment has become of great importance. This study aimed to explore the associations between deep learning signatures and biological functions as well as survival in (rNPC) patients. Methods: A total of 420 rNPC patients with PET/CT imaging and follow-up of overall survival (OS) were retrospectively enrolled. All patients were randomly divided into a training set (n = 269) and test set (n = 151) with a 6:4 ratio. We constructed multi-modality deep learning signatures from PET and CT images with a light-weighted deep convolutional neural network EfficienetNet-lite0 and survival loss DeepSurvLoss. An integrated nomogram was constructed incorporating clinical factors and deep learning signatures from PET/CT. Clinical nomogram and single-modality deep learning nomograms were also built for comparison. Furthermore, the association between biological functions and survival risks generated from an integrated nomogram was analyzed by RNA sequencing (RNA-seq). Results: The C-index of the integrated nomogram incorporating age, rT-stage, and deep learning PET/CT signature was 0.741 (95% CI: 0.688–0.794) in the training set and 0.732 (95% CI: 0.679–0.785) in the test set. The nomogram stratified patients into two groups with high risk and low risk in both the training set and test set with hazard ratios (HR) of 4.56 (95% CI: 2.80–7.42, p < 0.001) and 4.05 (95% CI: 2.21–7.43, p < 0.001), respectively. The C-index of the integrated nomogram was significantly higher than the clinical nomogram and single-modality nomograms. When stratified by sex, N-stage, or EBV DNA, risk prediction of our integrated nomogram was valid in all patient subgroups. Further subgroup analysis showed that patients with a low-risk could benefit from surgery and re-irradiation, while there was no difference in survival rates between patients treated by chemotherapy in the high-risk and low-risk groups. RNA sequencing (RNA-seq) of data further explored the mechanism of high- and low-risk patients from the genetic and molecular level. Conclusion: Our study demonstrated that PET/CT-based deep learning signatures showed satisfactory prognostic predictive performance in rNPC patients. The nomogram incorporating deep learning signatures successfully divided patients into different risks and had great potential to guide individual treatment: patients with a low-risk were supposed to be treated with surgery and re-irradiation, while for high-risk patients, the application of palliative chemotherapy may be sufficient. [ABSTRACT FROM AUTHOR]

• 548 nasopharyngeal carcinoma (NPC) patients were enrolled in this study to establish a multivariable normal tissue complication probability (NTCP) model for acquired nasal cavity stenosis and atresia (ANCSA) after radical radiotherapy. • The analysis resulted in a model with five highly predictive factors, including choanal invasion, white blood cell count, C-reactive protein level, serum amyloid A level before treatment and V70 of the nasal cavity. • For individual cases, scores higher than 46 in the prediction model should be considered as high-risk group of ANCSA, and the importance of regular endoscopic examination for early detection should be stressed. Curative radiotherapy for nasopharyngeal carcinoma (NPC) can lead to acquired nasal cavity stenosis and atresia (ANCSA). As the first study to investigate risk factors of ANCSA in a large cohort of NPC patients, this article aims to develop and validate a multivariate normal tissue complication probability (NTCP) model to predict the development of ANCSA and to establish a nomogram for clinical use. The retrospective cohort was comprised of 548 NPC patients treated with radical radiotherapy. The cohort was randomly divided into training and validation groups. Least absolute shrinkage and selection operator regression was performed for variable selection from the clinical and dosimetric characteristics in the training group. A multivariate NTCP model and a nomogram were established for the prediction of ANCSA development. Discrimination and calibration were tested using receiver operating characteristic (ROC) curves and calibration tests, respectively, for both groups. ANCSA was observed in 132 (24.1%) of 548 patients with NPC who underwent radical radiotherapy. The median time to ANCSA detection after treatment was 2.8 months (range, 0.0–57.7 months). Five potential predictors, including choanal invasion, low white blood cell count, high C-reactive protein level, high serum amyloid A level, and high V70Gy of the nasal cavity, were selected to develop the NTCP model based on 365 patients in the training group. The model had a fairly good discriminative power according to the ROC analysis in both the training (area under ROC curve = 0.79, 95%CI: 0.73–0.84) and validation (0.73, 0.64–0.82) groups. The calibration power was tested using the calibration test in the training (E-max = 0.069, E-avg = 0.015, p = 0.977) and validation (E-max = 0.057, E-avg = 0.032, p = 0.747) groups. We developed and successfully validated an NTCP model for early prediction of ANCSA in patients with NPC after radical radiotherapy. This could help clinicians assess the risk of ANCSA before the initiation of follow-ups and ensure appropriate and timely management of this complication. [ABSTRACT FROM AUTHOR]