To compare survival outcomes and acute toxicities between daytime and evening radiotherapy (RT) in patients with nasopharyngeal carcinoma (NPC). We enrolled 1351 NPC patients who received definitive RT in the daytime (before 16:00; n = 625) or evening (after 16:00; n = 726) between 2015 and 2016. Optimal cutoff time was determined by receiver operating characteristic analysis. Survival outcomes and toxicities were compared between groups before and after propensity score matching (PSM). Multivariate Cox analyses were performed to identify independent prognostic factors. With a median follow-up of 63 months, evening RT showed better overall survival (OS; p = 0.020), progression-free survival (PFS; p = 0.035), and locoregional failure-free survival (LRFS; p = 0.037), compared with daytime RT, but not distant metastasis-free survival (p = 0.523). Evening RT showed a lower incidence of all-grade dermatitis (59.5% vs. 68.2%, p = 0.002). After PSM, RT time remained an independent prognostic factor for LRFS (HR = 0.601, p = 0.018), OS (HR = 0.643, p = 0.043), and PFS (HR = 0.754, p = 0.040). Subgroup analyses revealed that evening RT improved OS (p = 0.007) and PFS (p = 0.006) in females and LRFS (p = 0.035) in males, with more pronounced benefits in older females (≥45 years; OS: p = 0.027, PFS: p = 0.003) and reduced mucositis in older males (82.9% vs. 91.4%, p = 0.015). Overall, evening RT demonstrated superior survival outcomes and reduced acute toxicities in NPC patients, with distinct benefits across sex and age.

Currently, there is little evidence supporting the use of early endpoints to assess primary treatment outcomes in nasopharyngeal carcinoma (NPC). We aim to explore the relationship between 24-month progression-free survival (PFS24) and subsequent overall survival (sOS) as well as loss of lifetime (LoL) in NPC patients. sOS is defined as survival from the 24-month point or progression within 24 months leading to mortality. LoL represents the reduction in life expectancy due to NPC, compared to the general population matched by age, sex, and calendar year. The standardized mortality ratio (SMR) is defined as the ratio of observed mortality to expected mortality. The study included 6315 patients from nonendemic and endemic regions of China. Among them, 5301 patients (83.9%) achieved PFS24, with a 5-year sOS of 90.2% and an SMR of 1.0. Over a 10-year period following treatment, the mean LoL was only 0.01 months/year. For most subgroups, patients achieving PFS24 exhibited comparable sOS and LoL with the general population. However, patients failing to achieve PFS24 showed significantly worse outcomes, with 5-year sOS of 21.9%, SMR of 23.7, and LoL of 6.48 months/year. These notable outcome disparities highlight the importance of PFS24 in NPC risk stratification, patient monitoring, and study design.

The purpose of this study was to explore the potential value of the radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), compared with the clinical model mostly based on the epidermal growth factor receptor (EGFR) expression, in predicting progression-free survival (PFS) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) treated with nimotuzumab (NTZ).

A total of 136 patients with LA-NPC who received NTZ treatment between January 2018 and June 2022 were included in this study. Patients were randomly divided into training (n = 95) and validation (n = 41) groups in a 7:3 ratio. DCE-MRI radiomics, clinical, and clinical-radiomics models were built to predict PFS. The relationship between EGFR expression levels and NTZ efficacy was assessed using Kaplan-Meier curves. Model performance was assessed using the area under the curve, calibration, and DeLong tests. Decision curve analysis evaluated net clinical benefit. Patients were stratified into high- and low-risk groups based on optimal model radiomic scores, and prognoses were compared using Kaplan-Meier curves.

Univariate Cox regression analysis demonstrated that EGFR expression level was the only independent predictive factor of PFS (p < 0.05). Patients with EGFR 3+ receiving NTZ therapy had significantly longer PFS than those with EGFR 1+ (hazard ratio = 3.025, p < 0.05). The clinical-radiomics model exhibited superior predictive efficacy, compared with the radiomics and clinical models (training set: 0.887 vs. 0.845, 0.654; validation set: 0.831, 0.824 vs. 0.567, all p < 0.001).

The clinical-radiomics models using DCE-MRI and EGFR levels can effectively predict NTZ efficacy in LA-NPC patients, providing objective evidence for personalized treatment adjustments.

Question How can the response to nimotuzumab treatment in patients with locally advanced nasopharyngeal carcinoma be accurately predicted using non-invasive imaging methods? Findings A combined clinical and radiomic model using dynamic contrast-enhanced magnetic resonance imaging showed improved predictive performance for progression-free survival in patients treated with nimotuzumab. Clinical relevance The study provides evidence for using a combined clinical and radiomic approach, offering a non-invasive method to predict treatment response and guide personalized treatment strategies for patients with locally advanced nasopharyngeal carcinoma, potentially improving patient outcomes.

To investigate the prognostic value of post-chemoradiotherapy 2-[18F]FDG PET/CT in locally advanced nasopharyngeal carcinoma (LANPC) and develop an accurate prognostic model based on the 2-[18F]FDG PET/CT results.

900 LANPC patients who underwent pretreatment and post-chemoradiotherapy 2-[18F]FDG PET/CT from May 2014 to August 2022 were included in the study. We divided the patients into two distinct cohorts for the purpose of our study: a training cohort comprising 506 individuals, included from May 2008 to April 2020, and a validation cohort consisting of 394 individuals, included from May 2020 to August 2022. PET/CT were assessed using the improved Deauville score (iDS) system. Cox regression analysis was performed to select candidate variables. A prognostic model was developed by the training cohort, and validated using the independent validation cohort.

Age (HR, 2.262(1.488-3.439); p＜0.001), ECOG (HR, 2.450 (1.395-4.301); p = 0.002), post-treatment EBV DNA level (HR, 2.208 (1.289-3.784); p = 0.004) and iDS {[iDS1-2 vs iDS3-4: HR, 3.781 (1.996-7.163); p＜0.001]; [iDS1-2 vs iDS5: HR, 11.707 (5.884-23.295); p＜0.001]}were independent predictors of OS. A 4-factor prognostic model developed and subsequently validated. This innovative model demonstrated excellent discrimination (C-index: 0.862). The calibration curves revealed a close match between the predicted probabilities and the actual outcomes, and decision curve analysis (DCA) confirmed the nomogram's utility for guiding clinical decision-making.

Our study validated the predictive value of the iDS system in determining outcome for LANPC. The 4-factor prognostic model, which integrates baseline patient characteristics with iDS, demonstrated good discrimination, agreement, and clinical application potential.

The present study analyzed the impact of age on the causes of death (CODs) in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy (CRT) using machine learning approaches. A total of 2841 patients (1037 classified as older, ≥ 60 years and 1804 as younger, < 60 years) were enrolled. Variations in the CODs between the two age groups were analyzed before and after applying inverse probability of treatment weighting (IPTW). Additionally, seven different machine learning models were employed as predictive tools to identify key variables and assess the therapeutic outcomes in NPC patients receiving CRT. The younger group exhibited a significantly longer overall survival (OS) than the older group, both before the IPTW adjustment (140 vs. 50 months, P < 0.001) and after the adjustment (137 vs. 53 months, P < 0.001). After IPTW, the older group was associated with worse 5-, 10-, and 15-year cumulative incidences in terms of NPC-related deaths (30, 34, and 38% vs. 21, 27, and 30%; P < 0.001), cardiovascular disease (CVD; 4.1, 7.2, and 8.8% vs. 0.5, 1.8, and 3.0%; P < 0.001), and other causes (8.3, 17, and 24% vs. 4.1, 8.7, and 12%; P < 0.001). However, cumulative incidences of secondary malignant neoplasms were comparable between the two groups (P = 0.100). The random forest (RF) model demonstrated the highest concordance index of 0.701 among all models. Time-dependent variable importance plots indicated that age was the most influential factor affecting 3-, 5-, and 10-year survival, followed by metastasis and tumor stage. Younger patients had significantly longer OS than their older counterparts. Older patients had a higher likelihood of dying from non-NPC-related causes, particularly CVDs. The RF model showed the best predictive accuracy, identifying age as the most critical factor influencing OS in NPC patients undergoing CRT.

The therapeutic benefit of concurrent chemoradiotherapy (CCRT) in elderly nasopharyngeal carcinoma (NPC) patients remains controversial. This study aimed to investigate the efficacy and toxicity of lobaplatin-based CCRT in elderly patients with NPC.

We included stage II-IVA NPC patients aged ≥65 years who received lobaplatin concomitant with intensity-modulated radiation therapy (IMRT) between March 2019 and January 2023. Objective response rates and treatment-related toxicity were assessed. Kaplan-Meier's analysis was performed to calculate survival rates.

A total of 29 patients were included with a median age of 67 years. There were 19 patients (65.5%) who had comorbidities. All patients had serum EBV-DNA detective before treatment; the median EBV-DNA load was 236 IU/mL. There were 25 (86.2%) patients treated with induction chemotherapy, and the overall response rate was 92.0%. All patients received IMRT and concurrent chemotherapy with lobaplatin. During the CCRT, the most common adverse effect was haematological toxicity. Three patients (10.3%) had grade 3 leucopenia, three patients (10.3%) had grade 3 neutropenia, and eight patients (27.6%) had grade 3-4 thrombocytopenia. The rate of grade 3 mucositis was 34.5%. No patients had liver and kidney dysfunction. The median weight loss was 4 kg during CCRT. After three months of CCRT, the total response rate was 100%. EBV-DNA was not detected in any patients. The median follow-up was 32.1 months. The 3-year locoregional recurrence-free survival, distant metastasis-free survival, progression-free survival and overall survival were 95.8%, 85.7%, 82.5% and 100%, respectively.

Lobaplatin-based CCRT is safe and feasible for elderly NPC patients, with satisfactory short-term survival outcomes and acceptable toxicities. A phase 2 trial is ongoing to investigate the role of lobaplatin-based CCRT on long-term survival and treatment toxicities for this population.

To develop and validate a web-based nomogram for predicting new incident chronic kidney disease (CKD) within 4 years in a cohort undergoing routine physical examination from two health examination centers.

One center was utilized for training and internal validation of a nomogram model involving 6515 patients, while a separate center was employed for external validation with 3152 patients. Sixteen candidate predictors, including patient demographics, medical histories, physical examination, and laboratory test data, were included in this study to ascertain factors linked to new incident CKD. A nomogram was created to predict CKD risks using a logistic model. The nomogram's performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis.

Out of the 9667 healthy individuals included in the study with mean age of 46 years, sex ratio (male/female) of 1.69 (6075/3592), 118 (2.59%), 51 (2.61%), and 60 (1.90%) individuals developed CKD in the training (n = 4563), internal validation (n = 1952), and external validation (n = 3152) datasets, respectively. Age, history of diabetes mellitus, systolic blood pressure, serum creatinine, albumin, and triglyceride levels were used to build the nomogram, which yielded excellent discrimination ability (training cohort, AUC = 0.8806, 95% confidence interval [CI] 0.8472-0.9141; internal validation cohort, AUC = 0.8506, 95% CI 0.7856-0.9156; external validation cohort, AUC = 0.9183, 95% CI 0.8698-0.9669). We further developed a web-based calculator for convenient application (https://luochuxuan.shinyapps.io/dynnomapp/).

Our web-based nomogram accurately predicted CKD risks in Chinese health individuals and can be easily used in clinical settings.

To evaluate the diagnostic value of 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA) for nasopharyngeal carcinoma (NPC) and to develop a diagnostic model.

Genome-wide 5hmC profiles in cfDNA from 174 NPC patients and 146 non-cancer individuals were analyzed using the 5hmC-Seal technique. A cfDNA 5hmC-based diagnostic model to identify NPC patients was developed using least absolute shrinkage and selection operator (LASSO) logistic regression, and performance was evaluated with receiver operating characteristic (ROC) curves and confusion matrices.

The 5hmC-Seal data from patients with NPC showed a different genome-wide distribution than non-tumor samples. Our initial analysis revealed a 12-gene-based 5hmC marker panel to be an accurate diagnostic model effectively distinguishing between NPC samples and non-cancerous samples (training set: area under curve (AUC)= 0.97 [95 % CI: 0.94-0.99]; and test set: AUC= 0.93 [95 % CI: 0.88-0.98]) superior to EBV DNA testing. The diagnostic score performed well in differentiating the non-cancer subjects from early-stage NPC (training set: AUC=0.99 [95 % CI: 0.98-1]; test set: AUC=0.98 [95 % CI: 0.95-1]), and advanced-stage NPC (training set: AUC=0.96 [95 % CI: 0.93-0.99]; test set: AUC=0.93 [95 % CI: 0.88-0.98]). Notably, in EBV-negative patients, the diagnostic scores showed excellent capacity for distinguishing EBV-negative patients with NPC from non-cancer subjects in both the training set (AUC= 0.94 [95 % CI: 0.88-1]) and test set (AUC=0.91 [95 % CI: 0.81-1]).

5hmC modifications in cfDNA are promising noninvasive biomarkers for NPC, offering high sensitivity and specificity, particularly for early-stage and EBV-negative NPC.

Immune-related characteristics can serve as reliable prognostic biomarkers in various cancers. Herein, we aimed to construct an individualized immune prognostic signature in nasopharyngeal carcinoma (NPC).

This study retrospectively included 455 NPC samples and 39 normal healthy nasopharyngeal tissue specimens. Samples from Gene Expression Omnibus (GEO) were obtained as discovery cohort to screen candidate prognostic immune-related gene pairs based on relative expression ordering of the genes. Quantitative real-time reverse transcription-PCR was used to detect the selected genes to construct an immune-related gene pair signature in training cohort, which comprised 118 clinical samples, and was then validated in validation cohort 1, comprising 92 clinical samples, and validation cohort 2, comprising 88 samples from GEO.

We identified 26 immune-related gene pairs as prognostic candidates in discovery cohort. A prognostic immune signature comprising 11 immune gene pairs was constructed in training cohort. In validation cohort 1, the immune signature could significantly distinguish patients with high or low risk in terms of progression-free survival (PFS) (hazard ratio [HR] 2.66, 95 % confidence interval (CI) 1.17-6.02, P=0.015) and could serve as an independent prognostic factor for PFS in multivariate analysis (HR 2.66, 95 % CI 1.17-6.02, P=0.019). Similar results were obtained using validation cohort 2, in which PFS was significantly worse in high risk group than in low risk group (HR 3.02, 95 % CI 1.12-8.18, P=0.022).

The constructed immune signature showed promise for estimating prognosis in NPC. It has potential for translation into clinical practice after prospective validation.

Nasopharyngeal carcinoma (NPC) has high metastatic potential and is hard to detect early. This study aims to develop a deep learning model for NPC diagnosis using optical imagery. From April 2008 to May 2021, we analyzed 12,087 nasopharyngeal endoscopic images and 309 videos from 1,108 patients. The pretrained model was fine-tuned with stochastic gradient descent on the final layers. Data augmentation was applied during training. Videos were converted to images for malignancy scoring. Performance metrics like AUC, accuracy, and sensitivity were calculated based on the malignancy score. The deep learning model demonstrated high performance in identifying NPC, with AUC values of 0.981 (95% confidence of interval [CI] 0.965-0.996) for the Fujian Cancer Hospital dataset and 0.937 (0.905-0.970) for the Jiangxi Cancer Hospital dataset. The proposed model effectively diagnoses NPC with high accuracy, sensitivity, and specificity across multiple datasets. It shows promise for early NPC detection, especially in identifying latent lesions.

Sarcopenia, characterized by loss of muscle mass index (SMI), serves as a diagnostic indicator for malnutrition and has been shown to influence cancer treatment outcomes. The objective of this study was to investigate the prognostic significance of sarcopenia on the locally advanced nasopharyngeal carcinoma (laNPC) patients.

545 patients with stage III-IVa NPC were included in this retrospective study. Sarcopenia was defined using the skeletal muscle index (SMI) determined at the C3 level based on baseline MRI. The log-rank test and the Cox proportional hazards model were used to compare overall survival (OS) and progression-free survival (PFS).

The results of the multivariate analysis revealed that sarcopenia group (HR = 2.82, 95% CI 1.96-4.06, P < 0.01), T4 stage (HR = 1.64, 95% CI 1.24-2.15, P < 0.01), N3 stage (HR = 1.91, 95% CI 1.52-2.40, P < 0.01), comorbidities (HR = 2.08, 95% CI 1.45-2.97, P < 0.01), and any adverse event grade 3-4 (HR = 1.48, 95% CI 1.04-2.01, P = 0.03) were identified as independent risk factors that significantly impacted the OS. Additionally, sarcopenia group (HR = 2.40, 95% CI 1.73-3.33, P < 0.01), T4 stage (HR = 1.50, 95% CI 1.17-1.92, P < 0.01), N3 stage (HR = 1.80, 95% CI 1.46-2.22, P < 0.01), sarcopenia group (HR = 2.40, 95% CI 1.73-3.33, P < 0.01), and any adverse event grade 3-4 (HR = 1.45, 95% CI 1.04-2.01, P = 0.03) were found to have a significant impact on PFS.

Sarcopenia was identified as a prognostic factor for patients with laNPC. Furthermore, T stage, N stage, comorbidities, and any adverse event grade 3-4 were identified as independent prognostic factors for laNPC.

Advancements in nasopharyngeal carcinoma (NPC) treatment have led to a focus on personalized treatment. Circulating tumor cells (CTCs) are important for liquid biopsies and personalized treatment but are not being fully utilized. This study examined how pre- and post-treatment CTC counts, EMT subtypes, clinical characteristics, and patient prognosis are related in order to support the use of liquid biopsy in managing NPC.

This retrospective study included 141 patients with locally advanced NPC. All patients underwent CanPatrol™ CTC detection pre- and post-treatment and were categorized into EMT subtypes: epithelial type, mixed type, and mesenchymal type. This study analyzed CTC enumeration, EMT subtypes, and their associations with clinical characteristics and survival outcomes.

The results indicated a positive correlation between the pre-treatment detection rate of CTCs and N stage (P < 0.01), alongside a positive correlation with the TNM clinical stage (P = 0.02). Additionally, the detection rate of mesenchymal CTCs post-treatment is positively associated with the N stage (P = 0.02). The enumeration of CTCs pre- and post-treatment is negatively correlated with prognosis and has statistical significance. Additionally, an investigation into the EMT subtypes of CTCs revealed a significant association between the presence of mesenchymal CTCs pre- and post-treatment and decreased overall survival (OS) (P < 0.05). Furthermore, T stage, N stage, TNM clinical stage, and Epstein-Barr virus (EBV) DNA were also significantly correlated with OS.

The study found that mesenchymal CTCs pre- and post-treatment, as well as the number of CTCs, were linked to a poor prognosis.

To determine the predictive value of the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), neutrophil-to-eosinophil ratio (NER), lymphocyte-to-eosinophil ratio (LER), monocyte-to-eosinophil ratio (MER), systemic inflammatory response index (SIRI), and ratio of inflammatory cells before and after treatment for predicting survival in advanced nasopharyngeal carcinoma (NPC) and to provide a reference for treatment.

A retrospective review of 70 patients was performed. Serological indexes were obtained by drawing blood before and after systemic therapy. The cutoff values of these indexes were determined by receiver operating characteristic (ROC) curves. The prognostic value of the indexes for overall survival (OS) and distant metastasis free survival (DMFS) was evaluated.

Survival analysis showed that a smaller pretreatment LMR value was associated with poor OS; larger pretreatment NER, LER, MER, and SIRI values were associated with poor OS; a smaller posttreatment LMR value was associated with poor OS; larger posttreatment NLR, NER, MER, and SIRI values were associated with poor OS; a smaller pretreatment LMR value was associated with poor DMFS; larger pretreatment NLR, NER, LER, and MER values were associated with poor DMFS; and larger posttreatment NLR, NER, LER, and MER values were associated with poor DMFS. Furthermore, a larger neutrophil after treatment-to-neutrophil before treatment ratio was associated with poor OS and DMFS. Logistic regression analysis showed that pretreatment MER and posttreatment NLR were independent predictors of OS in patients with advanced NPC; moreover, pretreatment and posttreatment MER and NLR were independent prognostic factors for DMFS in patients with advanced NPC.

The NLR, NER and MER can be used to predict survival in advanced NPC patients. Eosinophils might be one of the factors for the good prognosis of NPC patients. In addition, an increased number of neutrophils after treatment may indicate a favorable prognosis.

Despite that the docectaxel-cisplatin-5-fluorouracil (TPF) induction chemotherapy has greatly improved patients' survival and became the first-line treatment for advanced nasopharyngeal carcinoma (NPC), not all patients could benefit from this therapy. The mechanism underlying the TPF chemoresistance remains unclear. Here, by analyzing gene-expression microarray data and survival of patients who received TPF chemotherapy, we identify transcription factor ATMIN as a chemoresistance gene in response to TPF chemotherapy in NPC. Mass spectrometry and Co-IP assays reveal that USP10 deubiquitinates and stabilizes ATMIN protein, resulting the high-ATMIN expression in NPC. Knockdown of ATMIN suppresses the cell proliferation and facilitates the docetaxel-sensitivity of NPC cells both in vitro and in vivo, while overexpression of ATMIN exerts the opposite effect. Mechanistically, ChIP-seq combined with RNA-seq analysis suggests that ATMIN is associated with the cell death signaling and identifies ten candidate target genes of ATMIN. We further confirm that ATMIN transcriptionally activates the downstream target gene LCK and stabilizes it to facilitate cell proliferation and docetaxel resistance. Taken together, our findings broaden the insight into the molecular mechanism of chemoresistance in NPC, and the USP10-ATMIN-LCK axis provides potential therapeutic targets for the management of NPC.

The prognosis of advanced nasopharyngeal carcinoma (NPC) patients after intensity-modulated radiotherapy (IMRT) has not been well studied. We aimed to construct prognostic models for advanced NPC patients with stage III-IV after their first treatment with IMRT by using machine learning algorithms and to identify the most important predictors.

A total of 427 patients treated in Meizhou People's Hospital in Guangdong province, China from January 1, 2013 to December 12, 2018 were enrolled in this study, with an average follow-up period of 7.16 years from July 2020 to March 2021. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. Three machine learning algorithms were applied to construct advanced NPC prognostic models: logistic regression (LR), decision tree (DT), and random forest (RF). Area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis were identified and ranked.

There were 50 (11.7%) NPC-related deaths observed in this study. The mean age of all participants was 49.39±11.29 years, of whom 299 (70.0%) were males. In general, RF showed the best predictive performance with the highest AUC (0.753, 95% CI: 0.609, 0.896), compared to LR (0.736, 95% confidence interval (CI): 0.590, 0.881), and DT (0.720, 95% CI: 0.520, 0.921). The six most important predictors identified by RF were Epstein-Barr virus deoxyribonucleic acid, aspartate aminotransferase, body mass index, age, blood glucose level, and alanine aminotransferase.

We proposed RF as a simple and accurate tool for the evaluation of the prognosis of advanced NPC patients after the treatment with IMRT in clinical settings.

Patients with residual nasopharyngeal carcinoma after receiving definitive treatment have poor prognoses. Although immune checkpoint therapies have achieved breakthroughs for treating recurrent and metastatic nasopharyngeal carcinoma, none of these strategies have been assessed for treating residual nasopharyngeal carcinoma. In this single-arm, phase 2 trial, we aimed to evaluate the antitumor efficacy and safety of toripalimab (anti-PD1 antibody) plus capecitabine in patients with residual nasopharyngeal carcinoma after definitive treatment (ChiCTR1900023710). Primary endpoint of this trial was the objective response rate assessed according to RECIST (version 1.1). Secondary endpoints included complete response rate, disease control rate, duration of response, progression-free survival, safety profile, and treatment compliance. Between June 1, 2020, and May 31, 2021, 23 patients were recruited and received six cycles of toripalimab plus capecitabine every 3 weeks. In efficacy analyses, 13 patients (56.5%) had complete response, and 9 patients (39.1%) had partial response, with an objective response rate of 95.7% (95% CI 78.1-99.9). The trial met its prespecified primary endpoint. In safety analyses, 21 of (91.3%) 23 patients had treatment-related adverse events. The most frequently reported adverse event was hand-foot syndrome (11 patients [47.8%]). The most common grade 3 adverse event was hand-foot syndrome (two patients [8.7%]). No grades 4-5 treatment-related adverse events were recorded. This phase 2 trial shows that combining toripalimab with capecitabine has promising antitumour activity and a manageable safety profile for patients with residual nasopharyngeal carcinoma.

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.

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.

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).

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.

3 Laryngoscope, 134:127-135, 2024.

The Cox regression model is not sufficiently accurate to predict the survival prognosis of nasopharyngeal carcinoma (NPC) patients. It is impossible to calculate and rank the importance of impact factors due to the low predictive accuracy of the Cox regression model. So, we developed a system. Using the SEER (The Surveillance, Epidemiology, and End Results) database data on NPC patients, we proposed the use of random survival forest (RSF) and survival-support vector machine (SVM) from the machine learning methods to develop a survival prediction system specifically for NPC patients. This approach aimed to make up for the insufficiency of the Cox regression model. We also used the Cox regression model to validate the development of the nomogram and compared it with machine learning methods.

A total of 1,683 NPC patients were extracted from the SEER database from January 2010 to December 2015. We used R language for modeling work, established the nomogram of survival prognosis of NPC patients by Cox regression model, ranked the correlation of influencing factors by RSF model VIMP (variable important) method, developed a survival prognosis system for NPC patients based on survival-SVM, and used C-index for model evaluation and performance comparison.

Although the Cox regression models can be developed to predict the prognosis of NPC patients, their accuracy was lower than that of machine learning methods. When we substituted the data for the Cox model, the C-index for the training set was only 0.740, and the C-index for the test set was 0.721. In contrast, the C index of the survival-SVM model was 0.785. The C-index of the RSF model was 0.729. The importance ranking of each variable could be obtained according to the VIMP method.

The prediction results from the Cox model are not as good as those of the RSF method and survival-SVM based on the machine learning method. For the survival prognosis of NPC patients, the machine learning method can be considered for clinical application.

Nasopharyngeal carcinoma (NPC) is a relatively common type of cancer in Southern China, with local recurrence or distant metastases even after radical treatment; consequently, it is critical to identify the patients at higher risk for these events beforehand. This study aimed to assess the prognostic value of regional lymph node density (RLND) associated nomograms in NPC and to evaluate the utility of nomograms in risk stratification.

A total of 610 NPC patients without distant metastases (425 in the training and 185 in the validation cohort) were enrolled. The MRI-identified nodal features and clinical characteristics were documented, and the RLND was calculated. Cox analyses were conducted to identify prognostic-associated factors. Nomograms were generated based on the multivariate analysis results. The predictive accuracy and discriminative ability of the nomogram models were determined using the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve; the results were compared with those of the tumor-node-metastasis (TNM) classification. Decision curve analysis (DCA) and C-index were used to assess the prognostic effect and added discriminative ability of RLND. We also estimated the optimal RLND-based nomogram score cut-off values for survival prediction.

RLND was an independent predictor of overall survival (OS) and disease-free survival (DFS), with hazard ratios of 1.36 and 1.30, respectively. RLND was utilized in the construction of nomograms, alongside other independent prognostic factors. The RLND-based nomogram models presented a more effective discriminative ability than the TNM classification for predicting OS (C-index, 0.711 vs. 0.680) and DFS (C-index, 0.681 vs. 0.669), with favorable calibration and consistency. The comparison of C-index values between the nomogram models with and without RLND provided substantiation of the crucial role RLND plays in these models. DCA confirmed the satisfactory clinical practicability of RLND. Moreover, the nomograms were used to categorize the patients into three groups (high-, middle-, and low-risk), and the Kaplan-Meier curves showed significant differences in prognosis between them (p < 0.05). These results were verified in the validation cohort.

RLND stands as a robust prognostic factor in NPC. The RLND-based nomograms excel in predicting survival, surpassing the TNM classification.

Accurate prognostic predictions and personalized decision-making on induction chemotherapy (IC) for individuals with locally advanced nasopharyngeal carcinoma (LA-NPC) remain challenging. This research examined the predictive function of tumor burden-incorporated machine-learning algorithms for overall survival (OS) and their value in guiding treatment in patients with LA-NPC.

Individuals with LA-NPC were reviewed retrospectively. Tumor burden signature-based OS prediction models were established using a nomogram and two machine-learning methods, the interpretable eXtreme Gradient Boosting (XGBoost) risk prediction model, and DeepHit time-to-event neural network. The models' prediction performances were compared using the concordance index (C-index) and the area under the curve (AUC). The patients were divided into two cohorts based on the risk predictions of the most successful model. The efficacy of IC combined with concurrent chemoradiotherapy was compared to that of chemoradiotherapy alone.

The 1 221 eligible individuals, assigned to the training (n = 813) or validation (n = 408) set, showed significant respective differences in the C-indices of the XGBoost, DeepHit, and nomogram models (0.849 and 0.768, 0.811 and 0.767, 0.730 and 0.705). The training and validation sets had larger AUCs in the XGBoost and DeepHit models than the nomogram model in predicting OS (0.881 and 0.760, 0.845 and 0.776, and 0.764 and 0.729, P < 0.001). IC presented survival benefits in the XGBoost-derived high-risk but not low-risk group.

This research used machine-learning algorithms to create and verify a comprehensive model integrating tumor burden with clinical variables to predict OS and determine which patients will most likely gain from IC. This model could be valuable for delivering patient counseling and conducting clinical evaluations.

To investigate the prevalence and predictive factors of xerostomia during induction chemotherapy (IC) in patients with nasopharyngeal carcinoma (NPC).

We prospectively enrolled NPC patients who received IC between October 2020 and October 2021. The Visual Analogue Scale (VAS) and Xerostomia Inventory (XI) were used to evaluate the condition of xerostomia. The volume of the submandibular gland (SMG) was also calculated before and after IC.

Fifty-two patients were enrolled in this study. Of these patients, 32.7% (n = 17) experienced xerostomia before IC. There were 32 (61.5%) patients suffered from xerostomia after IC, including 21 (40.4%) patients with newly diagnosed xerostomia after IC and 11 (21.1%) patients complained their xerostomia aggravated in those with xerostomia before IC. The median XI scores increased from 11 (standard deviation [SD], 2.930) to 18 (SD 3.995), 16 (SD 3.605), and 17 (SD 4.331) after the first, second, and third cycles of IC, respectively. The median score of VAS also increased from 0 to 4 during the following three cycles of IC. In those with IC-related xerostomia, the SMG volume after IC was significantly decreased compared with those without IC-related xerostomia (P = 0.001). The reduction of the SMG volume after IC was the independent risk factor for xerostomia (P = 0.002).

Approximately two-thirds of NPC patients suffered from IC-related xerostomia and patients with a reduction of SMG volume after IC had a higher risk of xerostomia.

This research aimed to evaluate the prognostic significance of baseline prognostic nutritional index (PNI) and lactate dehydrogenase (LDH) for the outcome of individuals diagnosed with non-metastatic nasopharyngeal carcinoma (NPC).

A retrospective analysis was conducted on data from 810 patients with non-metastatic NPC who underwent intensity-modulated radiation therapy (IMRT) with or without chemotherapy. The best cut-offs for PNI and LDH were identified by X-tile software to be 48.5 and 150, respectively. To find the independent prognostic factors for survival outcomes, univariate and multivariate regression analyses were conducted, and AUCs were used to compare their prognostic values.

Multivariate analysis revealed that patients with PNI > 48.5 had better overall survival (OS) (HR: 0.502, P < 0.001), progression-free survival (PFS) (HR: 0.618, P < 0.001), and distant metastasis-free survival (DMFS) (HR: 0.637, P = 0.005). Higher LDH was associated with poorer OS (HR: 1.798, P < 0.001), PFS (HR: 1.671, P < 0.001), and DMFS (HR: 1.756, P < 0.001). The combination of low PNI and high LDH in non-metastatic NPC patients was correlated with poor OS (P < 0.001), PFS (P < 0.001), and DMFS (P < 0.001). The combination of PNI and LDH had the highest AUCs for predicting OS, PFS, and DMFS.

PNI and LDH might become valuable predictors of the prognosis of non-metastatic NPC patients undergoing IMRT with or without chemotherapy. Prognostic accuracy can be enhanced by combining PNI and LDH.

This study aimed to assess the prognostic and predictive value of a circulating hematological signature (CHS) and to develop a CHS-based nomogram for predicting prognosis and guiding individualized chemotherapy in non-metastatic nasopharyngeal carcinoma (NPC) patients.

NPC patients were recruited between January 2014 and December 2017 at the Jiangxi Cancer Hospital. The CHS was constructed based on a series of hematological indicators. The nomogram was developed by CHS and clinical factors.

A total of 779 patients were included. Three biomarkers were selected by least absolute shrinkage and selection operator regression, including prognostic nutritional index, albumin-to-fibrinogen ratio, and prealbumin-to-fibrinogen ratio, were used to construct the CHS. The patients in the low-CHS group had better 5-year DMFS and OS than those in the high-CHS group in the training (DMFS: 85.0% vs 56.6%, p<0.001; OS: 90.3% vs 65.4%, p<0.001) and validation cohorts (DMFS: 92.3% vs 43.6%, p<0.001; OS: 92.1% vs 65.5%, p<0.001). The nomogram_CHS showed better performance than clinical stage in predicting distant metastasis (concordance index: 0.728 vs 0.646). In the low-TRS (total risk scores) group, the patients received RT alone, CCRT and IC plus CCRT had similar 5-year DMFS and OS (p>0.05). In the middle-TRS group, the patients received RT alone had worse 5-year DMFS (58.7% vs 80.8% vs 90.8%, p=0.002) and OS (75.0% vs 94.1% vs 95.0%, p=0.001) than those received CCRT or IC plus CCRT. In the high-TRS group, the patients received RT alone and CCRT had worse 5-year DMFS (18.6% vs 31.3% vs 81.5%, p<0.001) and OS (26.9% vs 53.2% vs 88.8%, p<0.001) than those received IC plus CCRT.

The developed nomogram_CHS had satisfactory prognostic accuracy in NPC patients and may individualize risk estimation to facilitate the identification of suitable IC candidates.

Metastasis remains the major cause of treatment failure in patients with nasopharyngeal carcinoma (NPC), in which sustained activation of the Notch signaling plays a critical role. N6-Methyladenosine (m6A)-mediated post-transcriptional regulation is involved in fine-tuning the Notch signaling output; however, the post-transcriptional mechanisms underlying NPC metastasis remain poorly understood. In the present study, we report that insulin-like growth factor 2 mRNA-binding proteins 3 (IGF2BP3) serves as a key m6A reader in NPC. IGF2BP3 expression was significantly upregulated in metastatic NPC and correlated with poor prognosis in patients with NPC. IGF2BP3 overexpression promoted, while IGF2BP3 downregulation inhibited tumor metastasis and the stemness phenotype of NPC cells in vitro and in vivo. Mechanistically, IGF2BP3 maintains NOTCH3 mRNA stability via suppression of CCR4-NOT complex-mediated deadenylation in an m6A-dependent manner, which sustains Notch3 signaling activation and increases the transcription of stemness-associated downstream genes, eventually promoting tumor metastasis. Our findings highlight the pro-metastatic function of the IGF2BP3/Notch3 axis and revealed the precise role of IGF2BP3 in post-transcriptional regulation of NOTCH3, suggesting IGF2BP3 as a novel prognostic biomarker and potential therapeutic target in NPC metastasis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Mean platelet volume (MPV), as a marker of platelet activity, has been shown to be an efficient prognostic biomarker in several types of cancer. Using MPV, this study aimed to create and validate a prognostic nomogram to the overall survival in esophageal squamous cell carcinoma (ESCC) patients.

The nomogram was constructed and tested using data from a retrospective study of 1893 patients who were randomly assigned to the training and testing cohorts with a 7:3 randomization. In order to screen out the optimal predictors for overall survival (OS), we conducted the LASSO-cox regression, univariate, and multivariate cox regression analyses. Subsequently, the predictive accuracy of the nomogram was validated in both the training and the testing cohorts. Finally, decision curve analysis (DCA) was used to confirm clinical validity.

Age, MPV, nerve invasion, T stage, and N stage were found as independent prognostic variables for OS and were further developed into a nomogram. The nomogram's prediction accuracy for 1-, 3-, and 5-year OS was 0.736, 0.749, 0.774, and 0.724, 0.719, 0.704 in the training and testing cohorts, respectively. Furthermore, DCA results indicated that nomograms outperformed the AJCC 8th and conventional T, N staging systems in both the training and testing cohorts.

The nomogram, in conjunction with MPV and standard clinicopathological markers, could improve the accuracy of prediction of OS in ESCC patients.

This paper is a transcript of the 29 th Eugene N. Myers, MD International Lecture on Head and Neck Cancer presented at the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) in 2020. By the end of the 19 th century, the survival rate in treated patients was 10%. With the improvements in surgical techniques, currently, about two thirds of patients survive for > 5 years. Teamwork and progress in surgical reconstruction have led to advancements in ablative surgery; the associated adjuvant treatments have further improved the prognosis in the last 30 years. However, prospective trials are lacking; most of the accumulated knowledge is based on retrospective series and some real-world data analyses. Current knowledge on prognostic factors plays a central role in an efficient treatment decision-making process. Although the influence of most tumor- and patient-related prognostic factors in head and neck cancer cannot be changed by medical interventions, some environmental factors-including treatment, decision-making, and quality-can be modified. Ideally, treatment strategy decisions should be taken in dedicated multidisciplinary team meetings. However, evidence suggests that surgeons and hospital volume and specialization play major roles in patient survival after initial or salvage head and neck cancer treatment. The metrics of surgical quality assurance (surgical margins and nodal yield) in neck dissection have a significant impact on survival in head and neck cancer patients and can be influenced by the surgeon's expertise. Strategies proposed to improve surgical quality include continuous performance measurement, feedback, and dissemination of best practice measures.

Plasma Epstein-Barr virus (EBV) DNA is an established biomarker for endemic nasopharyngeal carcinoma. However, existing real-time quantitative PCR (qPCR) assays are limited by poor interlaboratory reproducibility. This is a barrier to biomarker integration into staging systems and management. It was hypothesized that EBV digital PCR (dPCR) would have similar sensitivity but improved precision relative to qPCR. Using the World Health Organization EBV standard and patient specimens, the NRG-HN001 BamHI-W qPCR, two commercial EBNA-1 qPCR assays, and two laboratory-developed dPCR assays amplifying the BamHI-W, EBNA-1, and EBER targets were compared. Testing was conducted in the North American reference laboratory for the NRG-HN001 randomized trial. The EBV dPCR assays achieved similar performance compared with qPCR. Although dPCR does not require quantitation standards, different dPCR thresholding algorithms yielded significant qualitative and quantitative variation. This was most evident with low levels of EBV DNA. No-template control-informed thresholding (ddpcRquant) mitigated false-positive/false-negative findings. The NRG-HN001 BamHI-W qPCR and laboratory-developed BamHI-W droplet dPCR offered higher sensitivity, lower limit of blank, higher precision at low plasma EBV DNA levels (≤1500 IU/mL), and higher overall agreement with clinical specimens versus single-copy qPCR/dPCR targets (EBNA-1/EBER). These data confirm the rationale for using the BamHI-W target to define prognostic thresholds and indicate that both qPCR and dPCR methods harmonized to the World Health Organization standard can provide the necessary analytical performance.

To establish and validate radiomic models for response prediction to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC) using the radiomic features from pretreatment MRI.

This retrospective analysis included 184 consecutive NPC patients, 132 in the primary cohort and 52 in the validation cohort. Radiomic features were derived from contrast-enhanced T1-weighted imaging (CE-T1) and T2-weighted imaging (T2-WI) for each subject. The radiomic features were then selected and combined with clinical characteristics to build radiomic models. The potential of the radiomic models was evaluated based on its discrimination and calibration. To measure the performance of these radiomic models in predicting the treatment response to IC in NPC, the area under the receiver operating characteristic curve (AUC), and sensitivity, specificity, and accuracy were used.

Four radiomic models were constructed in the present study including the radiomic signature of CE-T1, T2-WI, CE-T1 + T2-WI, and the radiomic nomogram of CE-T1. The radiomic signature of CE-T1 + T2-WI performed well in distinguishing response and non-response to IC in patients with NPC, which yielded an AUC of 0.940 (95% CI, 0.885-0.974), sensitivity of 83.1%, specificity of 91.8%, and accuracy of 87.1% in the primary cohort, and AUC of 0.952 (95% CI, 0.855-0.992), sensitivity of 74.2%, specificity of 95.2%, and accuracy of 82.7% in the validation cohort.

MRI-based radiomic models could be helpful for personalised risk stratification and treatment in NPC patients receiving IC.

To analysis the clinical outcomes of concurrent chemoradiotherapy (CCRT) alone based on 10-year results for loco-regionally advanced nasopharyngeal carcinoma (LANPC), so as to provide evidence for individualized treatment strategy and designing appropriate clinical trial for different risk LANPC patients.

Consecutive patients with stage III-IVa (AJCC/UICC 8th) were enrolled in this study. All patients received radical intensity-modulated radiotherapy (IMRT) and concurrent cisplatin chemotherapy (CDDP). The hazard ratios (HRs) of death risk in patients with T3N0 was used as baseline, relative HRs were calculated by a Cox proportional hazard model to classify different death risk patients. Survival curves for the time-to-event endpoints were analyzed by the Kaplan-Meier method and compared using the log-rank test. All statistical tests were conducted at a two-sided level of significance of 0.05.

A total of 456 eligible patients were included. With 12-year median follow-up, 10-year overall survival (OS) was 76%. 10-year loco-regionally failure-free survival (LR-FFS), distant failure-free survival (D-FFS) and failure-free survival (FFS) were 72%, 73% and 70%, respectively. Based on the relative hazard ratios (HRs) of death risk, LANPC patients were classified into 3 subgroups, low-risk group (T1-2N2 and T3N0-1) contained 244 patients with HR < 2; medium-risk group (T3N2 and T4N0-1) contained 140 patients with HR of 2 - 5; high-risk group (T4N2 and T1-4N3) contained 72 patients with HR > 5. The 10-year OS for patients in low-, medium-, and high-risk group were 86%, 71% and 52%, respectively. Significantly differences of OS rates were found between each of the two groups (low-risk group vs. medium-risk group, P < 0.001; low-risk group vs. high-risk group, P < 0.001; and medium-risk group vs. high-risk group, P = 0.002, respectively). Grade 3-4 late toxicities included deafness/otitis (9%), xerostomia (4%), temporal lobe injury (5%), cranial neuropathy (4%), peripheral neuropathy (2%), soft tissue damage (2%) and trismus (1%).

Our classification criteria demonstrated that significant heterogeneity in death risk among TN substages for LANPC patients. IMRT plus CDDP alone maybe suitable for low-risk LANPC (T1-2N2 or T3N0-1), but not for medium- and high-risk patients. These prognostic groupings provide a practicable anatomic foundation to guide individualized treatment and select optimal targeting in the future clinical trials.

To compare the short-term treatment response and survival of the three induction chemotherapy (IC) regimens, including gemcitabine and cisplatin (GP), docetaxel and cisplatin (TP), and docetaxel, cisplatin, and fluoropyrimidines (TPF) in locally advanced nasopharyngeal carcinoma (LANPC).

We included stage III-IVA NPC patients who received ≥3 cycles of IC in this study. The chi-square test, multivariate logistic regression analysis, and Kaplan-Meier method were used for statistical analysis.

A total of 227 patients were included. The overall response rate (ORR) of the primary nasopharyngeal tumors after IC with GP, TP, and TPF was 91.9%, 83.8%, and 91.7%, respectively (P=0.729), and the ORR of the cervical lymph nodes was 94.6%, 72.3%, and 85.0%, respectively (P<0.001). For the primary nasopharyngeal tumor, there was no significant difference in the ORR among the three IC regimens. For cervical lymph nodes, patients treated with GP had significantly higher ORR compared to those treated with the TP regimen (P=0.014), and comparable ORR was found between TPF and GP regimens (P=0.161). Similar progression-free survival (PFS) (P=0.501) and overall survival (OS) (P=0.504) were found among three IC regimens. There were comparable PFS (P=0.123) and OS (P=0.478) among those with complete response (CR), partial response (PR), and stable disease (SD)/progressive disease (PD) in the primary nasopharyngeal tumors. However, patients who had CR in the primary nasopharyngeal tumor (P=0.014) and the cervical lymph nodes (P=0.022) had better PFS compared to those who had PR or SD/PD.

GP and TPF regimens are equivalent to the TP regimen in the response to primary nasopharyngeal tumors after IC, but with better ORR in the cervical lymph nodes than the TP regimen. The response to IC may be a powerful indicator for predicting prognosis and developing individualized follow-up and treatment strategies for LANPC patients.

This study was intended to construct a brand new prognostic nomogram after combine clinical and pathological characteristics to increases prognostic value in patients with esophageal squamous cell carcinoma.

A total of 1,634 patients were included. Subsequently, the tumor tissues of all patients were prepared into tissue microarrays. AIPATHWELL software was employed to explore tissue microarrays and calculate the tumor-stroma ratio. X-tile was adopted to find the optimal cut-off value. Univariate and multivariate Cox analyses were used to screen out remarkable characteristics for constructing the nomogram in the total populations. A novel prognostic nomogram with clinical and pathological characteristics was constructed on the basis of the training cohort (n=1,144). What's more performance was validated in the validation cohort (n=490). Clinical-pathological nomogram were assessed by concordance index, time-dependent receiver operating characteristic, calibration curve and decision curve analysis.

The patients can divide into two groups with cut-off value of 69.78 for the tumor-stroma ratio. It is noteworthy that the survival difference was noticeable (P<0.001). A clinical-pathological nomogram was constructed by combining clinical and pathological characteristics to predict the overall survival. In comparison with TNM stage, the concordance index and time-dependent receiver operating characteristic of the clinical-pathological nomogram showed better predictive value (P<0.001). High quality of calibration plots in overall survival was noticed. As demonstrated by the decision curve analysis, the nomogram has better value than the TNM stage.

As evidently revealed by the research findings, tumor-stroma ratio is an independent prognostic factor in patients with esophageal squamous cell carcinoma. The clinical-pathological nomogram has an incremental value compared TNM stage in predicting overall survival.