Prognostic nomogram for refining the prognostication of the proposed 8th edition of the AJCC/UICC staging system for nasopharyngeal cancer in the era of intensity-modulated radiotherapy

Lobaplatin-based concurrent chemoradiotherapy in elderly nasopharyngeal carcinoma

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Circulating tumor cells: a valuable indicator for locally advanced nasopharyngeal carcinoma

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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

Neutrophil-to-lymphocyte ratio and monocyte-to-eosinophil ratio as prognostic indicators for advanced nasopharyngeal carcinoma

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Transcription factor ATMIN facilitates chemoresistance in nasopharyngeal carcinoma

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.

Using machine learning algorithms to predict the prognosis of advanced nasopharyngeal carcinoma after intensity-modulated radiotherapy

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Toripalimab plus capecitabine in the treatment of patients with residual nasopharyngeal carcinoma: a single-arm phase 2 trial

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.

The Detection of Nasopharyngeal Carcinomas Using a Neural Network Based on Nasopharyngoscopic Images

OBJECTIVE

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:127-135, 2024.

Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients

Background

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.

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.

Results

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.

Conclusions

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.

Regional lymph node density-based nomogram predicts prognosis in nasopharyngeal carcinoma patients without distant metastases

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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

Predictive function of tumor burden-incorporated machine-learning algorithms for overall survival and their value in guiding management decisions in patients with locally advanced nasopharyngeal carcinoma

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

Xerostomia: An easily ignored symptom induced by induction chemotherapy in patients with nasopharyngeal carcinoma

BACKGROUND

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

METHODS

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.

RESULTS

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).

CONCLUSION

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.

Prognostic nutritional index and serum lactate dehydrogenase predict the prognosis of nasopharyngeal carcinoma patients who received intensity-modulated radiation therapy

PURPOSE

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

A Novel Prognostic Model Predicts Outcomes in Non-Metastatic Nasopharyngeal Carcinoma Based on Inflammation, Nutrition, and Coagulation Signature

Purpose

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.

Patients and Methods

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.

Results

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.

Conclusion

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

The m6A reader IGF2BP3 preserves NOTCH3 mRNA stability to sustain Notch3 signaling and promote tumor metastasis in nasopharyngeal carcinoma

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.

Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Prognostic value of primary tumor and lymph node volumetric metabolic parameters at pre-treatment F-18 FDG PET/CT in nasopharyngeal carcinoma

BACKGROUND

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.

MATERIAL AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

A prognostic nomogram that includes MPV in esophageal squamous cell carcinoma

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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

Eugene Nicholas Myers' Lecture on Head and Neck Cancer, 2020: The Surgeon as a Prognostic Factor in Head and Neck Cancer Patients Undergoing Surgery

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.

Comparison of Real-Time PCR and Digital PCR for Detection of Plasma Epstein-Barr Virus DNA in Nasopharyngeal Carcinoma

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.

Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients

AIM

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

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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

Efficacy of concurrent chemoradiotherapy alone for loco-regionally advanced nasopharyngeal carcinoma: long-term follow-up analysis

BACKGROUND

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.

METHODS

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.

RESULTS

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%).

CONCLUSIONS

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.

An overall survival predictive nomogram to identify high-risk patients among locoregionally advanced nasopharyngeal carcinoma: Developed based on the SEER database and validated institutionally

Objective

Locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, even at the same stage, have different prognoses. We aim to construct a prognostic nomogram for predicting the overall survival (OS) to identify the high-risk LA-NPC patients.

Materials and methods

Histologically diagnosed WHO type II and type III LA-NPC patients in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled as the training cohort (n= 421), and LA-NPC patients from Shantou University Medical College Cancer Hospital (SUMCCH) served as the external validation cohort (n= 763). Variables were determined in the training cohort through Cox regression to form a prognostic OS nomogram, which was verified in the validation cohort, and compared with traditional clinical staging using the concordance index (C-index), Kaplan–Meier curves, calibration curves and decision curve analysis (DCA). Patients with scores higher than the specific cut-off value determined by the nomogram were defined as high-risk patients. Subgroup analyses and high-risk group determinants were explored.

Results

Our nomogram had a higher C-index than the traditional clinical staging method (0.67 vs. 0.60, p&lt;0.001). Good agreement between the nomogram-predicted and actual survival were shown in the calibration curves and DCA, indicating a clinical benefit of the nomogram. High-risk patients identified by our nomogram had worse prognosis than the other groups, with a 5-year overall survival (OS) of 60.4%. Elderly patients at advanced stage and without chemotherapy had a tendency for high risk than the other patients.

Conclusions

Our OS predictive nomogram for LA-NPC patients is reliable to identify high-risk patients.

Assessment of Response to Different Induction Chemotherapy Regimens in Locally Advanced Nasopharyngeal Carcinoma

Purpose

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).

Methods

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.

Results

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.

Conclusion

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.

Increases prognostic value of clinical-pathological nomogram in patients with esophageal squamous cell carcinoma

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Dynamic contrast-enhanced magnetic resonance imaging-based radiomics for the prediction of progression-free survival in advanced nasopharyngeal carcinoma

To establish a multidimensional nomogram model for predicting progression-free survival (PFS) and risk stratification in patients with advanced nasopharyngeal carcinoma (NPC). This retrospective cross-sectional study included 156 patients with advanced NPC who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomic features were extracted from the efflux rate constant (Ktrans ) and extracellular extravascular volume (Ve ) mapping derived from DCE-MRI. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied for feature selection. The Radscore was constructed using the selected features with their respective weights in the LASSO Cox regression analysis. A nomogram model combining the Radscore and clinical factors was built using multivariate Cox regression analysis. The C-index was used to assess the discrimination power of the Radscore and nomogram. The Kaplan-Meier method was used for survival analysis. Of the 360 radiomic features, 28 were selected (7, 6, and 15 features extracted from Ktrans , Ve, and Ktrans +Ve images, respectively). The combined Radscore k trans +Ve (C-index, 0.703, 95% confidence interval [CI]: 0.571-0.836) showed higher efficacy in predicting the prognosis of advanced NPC than Radscore k trans (C-index, 0.693; 95% CI, 0.560-0.826) and Radscore Ve (C-index, 0.614; 95% CI, 0.481-0.746) did. Multivariable Cox regression analysis revealed clinical stage, T stage, and treatment with nimotuzumab as risk factors for PFS. The nomogram established by Radscore k trans +Ve and risk factors (C-index, 0.732; 95% CI: 0.599-0.864) was better than Radscore k trans +Ve in predicting PFS in patients with advanced NPC. A lower Radscore k trans +Ve (HR 3.5584, 95% CI 2.1341-5.933), lower clinical stage (hazard ratio [HR] 1.5982, 95% CI 0.5262-4.854), lower T stage (HR 1.4365, 95% CI 0.6745-3.060), and nimotuzumab (NTZ) treatment (HR 0.7879, 95% CI 0.4899-1.267) were associated with longer PFS. Kaplan-Meier analysis showed a lower PFS in the high-risk group than in the low-risk group (p<0.0001). The nomogram based on combined pretreatment DCE-MRI radiomics features, NTZ, and clinicopathological risk factors may be considered as a noninvasive imaging marker for predicting individual PFS in patients with advanced NPC.

Radiomics based on pretreatment MRI for predicting distant metastasis of nasopharyngeal carcinoma: A preliminary study

Objective

To explore the feasibility of predicting distant metastasis (DM) of nasopharyngeal carcinoma (NPC) patients based on MRI radiomics model.

Methods

A total of 146 patients with NPC pathologically confirmed, who did not exhibit DM before treatment, were retrospectively reviewed and followed up for at least one year to analyze the DM risk of the disease. The MRI images of these patients including T2WI and CE-T1WI sequences were extracted. The cases were randomly divided into training group (n=116) and validation group (n=30). The images were filtered before radiomics feature extraction. The least absolute shrinkage and selection operator (LASSO) regression was used to develop the dimension of texture parameters and the logistic regression was used to construct the prediction model. The ROC curve and calibration curve were used to evaluate the predictive performance of the model, and the area under curve (AUC), accuracy, sensitivity, and specificity were calculated.

Results

72 patients had DM and 74 patients had no DM. The AUC, accuracy, sensitivity and specificity of the model were 0. 80 (95% CI: 0.72~0. 88), 75.0%, 76.8%, 73.3%. and0.70 (95% CI: 0.51~0.90), 66.7%, 72.7%, 63.2% in training group and validation group, respectively.

Conclusion

The radiomics model based on logistic regression algorithm has application potential for evaluating the DM risk of patients with NPC.

Prediction of 5-year progression-free survival in advanced nasopharyngeal carcinoma with pretreatment PET/CT using multi-modality deep learning-based radiomics

Objective

Deep learning-based radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year progression-free survival (PFS) in advanced nasopharyngeal carcinoma (NPC) using pretreatment PET/CT images.

Methods

A total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. The TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1,456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of six feature selection methods and nine classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature.

Results

Our multi-modality DLR model using both PET and CT achieved higher prognostic performance (area under the receiver operating characteristic curve (AUC) = 0.842 ± 0.034 and 0.823 ± 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 ± 0.033 and 0.782 ± 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 ± 0.029 and 0.796 ± 0.009) or only CT (AUC = 0.657 ± 0.055 and 0.645 ± 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high- and low-risk patient groups in both the internal and external cohorts (p < 0.001), while the clinical signature failed in the external cohort (p = 0.177).

Conclusion

Our study identified potential prognostic tools for survival prediction in advanced NPC, which suggests that DLR could provide complementary values to the current TNM staging.

MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma

Background

The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT).

Methods

Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients.

Results

A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05).

Conclusion

The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09832-6.

Function of AXL and molecular mechanisms in regulation of nasopharyngeal carcinoma

OBJECTIVES

Nasopharyngeal carcinoma (NPC) is a highly invasive epithelial malignant tumor with unique geographical and ethnic distribution characteristics. NPC is mostly found in south China and Southeast Asia, and its treatment mainly depends on radiotherapy and chemotherapy. However, NPC is usually found in the late stage, and local recurrence and distant metastasis are common, leading to poor prognosis. The receptor tyrosine kinase AXL is up-regulated in various tumors and it is involved in tumor proliferation, migration, invasion, and other processes, which are associated with poor prognosis of tumors. This study aims to detect the expression of AXL in NPC cell lines and tissues, and to investigate its biological function of AXL and the underlying molecular mechanisms in regulation of NPC.

METHODS

The expression levels of AXL in normal nasopharyngeal epithelial tissues and NPC tissues were analyzed by GSE68799, GSE12452, and GSE53819 data sets based on Gene Expression Omnibus (GEO) database. The Cancer Genome Atlas (TCGA) database was used to analyze the relationship between AXL and prognosis of head and neck squamous cell carcinoma (HNSC). The indicators of prognosis included overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI). Western blotting assay was used to detect the AXL protein expression levels in normal nasopharyngeal epithelial cell line and NPC cell lines. Immunohistochemical method was used to detect AXL expression levels in normal nasopharyngeal epithelial tissues and NPC tissues. Cell lines with stable AXL knockdown were established by infecting 5-8F and Fadu cells with lentivirus interference vector, and cell lines with stable AXL overexpression were established by infecting C666-1 and HK-1 cells with lentivirus expression vector. Real-time PCR and Western blotting were used to detect the efficiency of knockdown and overexpression in stable cell lines. The effects of AXL knockdown or overexpression on proliferation, migration, and invasion of NPC cells were detected by CCK-8, plate colony formation, and Transwell assays, and the effect of AXL knockdown on tumor growth in nude mice was detected by subcutaneous tumor formation assay. The sequence of AXL upstream 2.0 kb promoter region was obtained by UCSC online database. The PROMO online database was used to predict AXL transcription factors with 0% fault tolerance, and the JASPAR online database was used to predict the binding sites of ETS1 to AXL. Real-time PCR and Western blotting were used to detect the effect of ETS1 on AXL protein and mRNA expression. The AXL upstream 2.0 kb promoter region was divided into 8 fragments, each of which was 250 bp in length. Primers were designed for 8 fragments. The binding of ETS1 to AXL promoter region was detected by chromatin immuno-precipitation (ChIP) assay to determine the direct regulatory relationship between ETS1 and AXL. Rescue assay was used to determine whether ETS1 affected the proliferation, migration, and invasion of NPC cells through AXL.

RESULTS

Bioinformatics analysis showed that AXL was highly expressed in NPC tissues (P<0.05), and AXL expression was positively correlated with OS, DFI, DSS, and PFI in HNSC patients. Western blotting and immunohistochemical results showed that AXL was highly expressed in NPC cell lines and tissues compared with the normal nasopharyngeal epithelial cell line and tissues. Real-time PCR and Western blotting results showed that knockdown and overexpression efficiency in the stable cell lines met the requirements of subsequent experiments. The results of CCK-8, plate colony formation, Transwell assays and subcutaneous tumor formation in nude mice showed that down-regulation of AXL significantly inhibited the proliferation, migration, invasion of NPC cells and tumor growth (all P<0.05), and the up-regulation of AXL significantly promoted the proliferation, migration, and invasion of NPC cells (all P<0.05).As predicted by PROMO and JASPAR online databases, ETS1 was a transcription factor of AXL and had multiple binding sites in the AXL promoter region. Real-time PCR and Western blotting results showed that knockdown or overexpression of ETS1 down-regulated or up-regulated AXL protein and mRNA expression levels. ChIP assay result showed that ETS1 bound to AXL promoter region and directly regulate AXL expression. Rescue assay showed that AXL rescued the effects of ETS1 on proliferation, migration and invasion of NPC cells (P<0.05).

CONCLUSIONS

AXL is highly expressed in NPC cell lines and tissues, which can promote the malignant progression of NPC, and its expression is regulated by transcription factor ETS1.

A prediction model for xerostomia in locoregionally advanced nasopharyngeal carcinoma patients receiving radical radiotherapy

Background

This study was to evaluate the predictors of xerostomia and Grade 3 xerostomia in locoregionally advanced nasopharyngeal carcinoma (NPC) patients receiving radical radiotherapy and establish prediction models for xerostomia and Grade 3 xerostomia based on the predictors.

Methods

Totally, 365 patients with locoregionally advanced NPC who underwent radical radiotherapy were randomly divided into the training set (n = 255) and the testing set (n = 110) at a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator regression to screen out the potential predictors for xerostomia as well as the Grade 3 xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The random forest (RF), a decision tree classifier (DTC), and extreme-gradient boosting (XGB) models were constructed. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC) and accuracy were analyzed to evaluate the predictive performance of the models.

Results

In the RF model for predicting xerostomia, the sensitivity was 1.000 (95%CI 1.000–1.000), the PPV was 0.990 (95%CI 0.975–1.000), the NPV was 1.000 (95%CI 1.000–1.000), the AUC was 0.999 (95%CI 0.997–1.000) and the accuracy was 0.992 (95%CI 0.981–1.000) in the training set. The sensitivity was 0.933 (95%CI 0.880–0.985), the PPV was 0.933 (95%CI 0.880–0.985), and the AUC was 0.915 (95%CI 0.860–0.970) in the testing set. Hypertension, age, total radiotherapy dose, dose at 50% of the left parotid volume, mean dose to right parotid gland, mean dose to oral cavity, and course of induction chemotherapy were important variables associated with the risk of xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The AUC of DTC model for predicting xerostomia was 0.769 (95%CI 0.666–0.872) in the testing set. The AUC of the XGB model for predicting xerostomia was 0.834 (0.753–0.916) in the testing set. The RF model showed the good predictive ability with the AUC of 0.986 (95%CI 0.972–1.000) in the training set, and 0.766 (95%CI 0.626–0.905) in the testing set for identifying patients who at high risk of Grade 3 xerostomia in those with high risk of xerostomia.

Conclusions

An RF model for predicting xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy and an RF model for predicting Grade 3 xerostomia in those with high risk of xerostomia showed good predictive ability.

DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients With Advanced Nasopharyngeal Carcinoma Using Pretreatment PET/CT

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e.g., local lymph node metastasis and adjacent tissue invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation in advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background information. In addition, we also introduce a cascaded survival network to capture the prognostic information existing out of primary tumors and further leverage the global tumor information (e.g., tumor size, shape, and locations) derived from the segmentation backbone. Our experiments with two clinical datasets demonstrate that our DeepMTS can consistently outperform traditional radiomics-based survival prediction models and existing deep survival models.

A lncRNA signature associated with tumor immune heterogeneity predicts distant metastasis in locoregionally advanced nasopharyngeal carcinoma

Increasing evidence has revealed the roles of long noncoding RNAs (lncRNAs) as tumor biomarkers. Here, we introduce an immune-associated nine-lncRNA signature for predicting distant metastasis in locoregionally advanced nasopharyngeal carcinoma (LA-NPC). The nine lncRNAs are identified through microarray profiling, followed by RT–qPCR validation and selection using a machine learning method in the training cohort (n = 177). This nine-lncRNA signature classifies patients into high and low risk groups, which have significantly different distant metastasis-free survival. Validations in the Guangzhou internal (n = 177) and Guilin external (n = 150) cohorts yield similar results, confirming that the signature is an independent risk factor for distant metastasis and outperforms anatomy-based metrics in identifying patients with high metastatic risk. Integrative analyses show that this nine-lncRNA signature correlates with immune activity and lymphocyte infiltration, which is validated by digital pathology. Our results suggest that the immune-associated nine-lncRNA signature can serve as a promising biomarker for metastasis prediction in LA-NPC.

Long noncoding RNAs (lncRNAs) can be used for the development of prognostic signatures to predict tumour metastasis. Here the authors identify an immune-associated nine-lncRNA signature for predicting metastasis in a multicentre cohort of locoregionally advanced nasopharyngeal cancer patients.

RNF38 suppress growth and metastasis via ubiquitination of ACTN4 in nasopharyngeal carcinoma

Background

Accumulated evidence suggests that RING finger proteins (RNFs) are involved in the carcinogenesis of cancers. However, RNF38, a member of the RNF protein family, has not been studied in nasopharyngeal carcinoma (NPC).

Methods

RNF38 expression was analyzed by RT-PCR, Western blotting and Immunohistochemistry. Biological functions of RNF38 were evaluated by cell growth, colony formation, apoptosis, migration and invasion assays in vitro. Xenograft growth and lung metastasis models were conducted to investigate the effect of RNF38 in vivo. Liquid chromatography coupled with tandem mass spectrometry, co-immunoprecipitation, and CHX assay were implemented to detect the interaction among RNF38 and ACTN4.

Results

RNF38 was significantly downregulated in NPC cells and tissues. Immunohistochemistry implied that loss of RNF38 was an independent prognostic factor for poor outcomes of NPC patients. Gain- and loss-of-function experiments showed that RNF38 inhibited proliferation and metastasis in NPC in vitro and in vivo. Upregulation of RNF38 promoted apoptosis of NPC cells to etoposide but not cisplatin. ACTN4 was upregulated in NPC and negatively correlated with RNF38. Mechanistic investigations suggested that RNF38 inactivates the NF-𝛋B and ERK1/2 signaling pathways by inducing ubiquitination and degradation of ACTN4. RNF38 suppress the development of NPC by interacting with ACTN4.

Conclusions

RNF38 plays a potential cancer suppressor gene role in NPC tumorigenesis and is a prognostic biomarker in NPC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09641-x.

Mutational landscape of nasopharyngeal carcinoma based on targeted next-generation sequencing: implications for predicting clinical outcomes

Background

The aim of this study was to draw a comprehensive mutational landscape of nasopharyngeal carcinoma (NPC) tumors and identify the prognostic factors for distant metastasis-free survival (DMFS).

Methods

A total of forty primary nonkeratinizing NPC patients underwent targeted next-generation sequencing of 450 cancer-relevant genes. Analysis of these sequencing and clinical data was performed comprehensively. Univariate Cox regression analysis and multivariate Lasso-Cox regression analyses were performed to identify factors that predict distant metastasis and construct a risk score model, and seventy percent of patients were randomly selected from among the samples as a validation cohort. A receiver operating characteristic (ROC) curve and Harrell’s concordance index (C-index) were used to investigate whether the risk score was superior to the TNM stage in predicting the survival of patients. The survival of patients was determined by Kaplan–Meier curves and log-rank tests.

Results

The twenty most frequently mutated genes were identified, such as KMT2D, CYLD, and TP53 et al. Their mutation frequencies of them were compared with those of the COSMIC database and cBioPortal database. N stage, tumor mutational burden (TMB), PIK3CA, and SF3B1 were identified as predictors to build the risk score model. The risk score model showed a higher AUC and C-index than the TNM stage model, regardless of the training cohort or validation cohort. Moreover, this study found that patients with tumors harboring PI3K/AKT or RAS pathway mutations have worse DMFS than their wild-type counterparts.

Conclusions

In this study, we drew a mutational landscape of NPC tumors and established a novel four predictor-based prognostic model, which had much better predictive capacity than TNM stage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00479-4.

An Exploratory Study of Refining TNM-8 M1 Categories and Prognostic Subgroups Using Plasma EBV DNA for Previously Untreated De Novo Metastatic Nasopharyngeal Carcinoma

(1) Background: NPC patients with de novo distant metastasis appears to be a heterogeneous group who demonstrate a wide range of survival, as suggested by growing evidence. Nevertheless, the current 8th edition of TNM staging (TNM-8) grouping all these patients into the M1 category is not able to identify their survival differences. We sought to identify any anatomic and non-anatomic subgroups in this study. (2) Methods: Sixty-nine patients with treatment-naive de novo M1 NPC (training cohort) were prospectively recruited from 2007 to 2018. We performed univariable and multivariable analyses (UVA and MVA) to explore anatomic distant metastasis factors, which were significantly prognostic of overall survival (OS). Recursive partitioning analysis (RPA) with the incorporation of significant factors from MVA was then performed to derive a new set of RPA stage groups with OS segregation (Set 1 Anatomic-RPA stage groups); another run of MVA was performed with the addition of pre-treatment plasma EBV DNA. A second-round RPA with significant prognostic factors of OS identified in this round of MVA was performed again to derive another set of stage groups (Set 2 Prognostic-RPA stage groups). Both sets were then validated externally with an independent validation cohort of 67 patients with distant relapses of their initially non-metastatic NPC (rM1) after radical treatment. The performance of models in survival segregation was evaluated by the Akaike information criterion (AIC) and concordance index (C-index) under 1000 bootstrapping samples for the validation cohort; (3) Results: The 3-year OS and median follow-up in the training cohort were 36.0% and 17.8 months, respectively. Co-existence of liver-bone metastases was the only significant prognostic factor of OS in the first round UVA and MVA. Set 1 RPA based on anatomic factors that subdivide the M1 category into two groups: M1a (absence of co-existing liver-bone metastases; median OS 28.1 months) and M1b (co-existing liver-bone metastases; median OS 19.2 months, p = 0.023). When pre-treatment plasma EBV DNA was also added, it became the only significant prognostic factor in UVA (p = 0.001) and MVA (p = 0.015), while co-existing liver-bone metastases was only significant in UVA. Set 2 RPA with the incorporation of pre-treatment plasma EBV DNA yielded good segregation (M1a: EBV DNA ≤ 2500 copies/mL and M1b: EBV DNA > 2500 copies/mL; median OS 44.2 and 19.7 months, respectively, p < 0.001). Set 2 Prognostic-RPA groups (AIC: 228.1 [95% CI: 194.8−251.8] is superior to Set 1 Anatomic-RPA groups (AIC: 278.5 [254.6−301.2]) in the OS prediction (p < 0.001). Set 2 RPA groups (C-index 0.59 [95% CI: 0.54−0.67]) also performed better prediction agreement in the validation cohort (vs. Set 1: C-index 0.47 [95% CI: 0.41−0.53]) (p < 0.001); (4) Conclusions: Our Anatomic-RPA stage groups yielded good segregation for de novo M1 NPC, and prognostication was further improved by incorporating plasma EBV DNA. These new RPA stage groups for M1 NPC can be applied to countries/regions regardless of whether reliable and sensitive plasma EBV DNA assays are available or not.

Increased prognostic value of clinical–reproductive model in Chinese female patients with esophageal squamous cell carcinoma

BACKGROUND

In China, it has been well recognized that some female patients with esophageal squamous cell carcinoma (ESCC) have different overall survival (OS) time, even with the same tumor-node-metastasis (TNM) stage, challenging the prognostic value of the TNM system alone. An effective predictive model is needed to accurately evaluate the prognosis of female ESCC patients.

AIM

To construct a novel prognostic model with clinical and reproductive data for Chinese female patients with ESCC, and to assess the incremental prognostic value of the full model compared with the clinical model and TNM stage.

METHODS

A new prognostic nomogram incorporating clinical and reproductive features was constructed based on univariatie and Cox proportional hazards survival analysis from a training cohort (n = 175). The results were recognized using the internal (n = 111) and independent external (n = 85) validation cohorts. The capability of the clinical–reproductive model was evaluated by Harrell’s concordance index (C-index), Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC), calibration curve and decision curve analysis. The correlations between estrogen response and immune-related pathways and some gene markers of immune cells were analyzed using the TIMER 2.0 database.

RESULTS

A clinical–reproductive model including incidence area, age, tumor differentiation, lymph node metastasis (N) stage, estrogen receptor alpha (ESR1) and beta (ESR2) expression, menopausal age, and pregnancy number was constructed to predict OS in female ESCC patients. Compared to the clinical model and TNM stage, the time-dependent ROC and C-index of the clinical–reproductive model showed a good discriminative ability for predicting 1-, 3-, and 5-years OS in the primary training, internal and external validation sets. Based on the optimal cut-off value of total prognostic scores, patients were classified into high- and low-risk groups with significantly different OS. The estrogen response was significantly associated with p53 and apoptosis pathways in esophageal cancer.

CONCLUSION

The clinical–reproductive prognostic nomogram has an incremental prognostic value compared with the clinical model and TNM stage in predicting OS in Chinese female ESCC patients.

Nomogram for distant metastasis-free survival in patients with locoregionally advanced nasopharyngeal carcinoma

OBJECTIVE

To develop and validate a nomogram to predict distant metastasis-free survival of patients with locoregionally advanced nasopharyngeal carcinoma.

METHODS

We collected the total clinical data of 820 nasopharyngeal carcinoma (NPC) patients, of whom 482 formed the training cohort from one hospital and 328 made up the validation cohort from another hospital. By analyzing the prognosis of all patients after intensity-modulated radiotherapy by univariate and multivariate Cox regression models, a nomogram related to DMFS was created in the training cohort. The discriminatory and calibration power of the nomogram was successively assessed in the training and validation cohorts by the C‑index and calibration curve. The predictive ability for 3‑year DMFS was compared between the nomogram and TNM stage using ROC curves. Patients were divided into different risk groups based on scores calculated from the nomogram.

RESULTS

Age, lymph node gross tumor volume (GTVnd), and gross tumor volume of the nasopharynx (GTVnx) were the factors included in the nomogram. The C‑index of the nomogram was 0.721 in the training cohort and 0.750 in the validation cohort. The calibration curves were satisfactory. Patients in the high-risk group were more likely to develop metastases.

CONCLUSION

A nomogram incorporating age, GTVnd, and GTVnx showed good performance for predicting DMFS in patients with locoregionally advanced NPC.

The immune modulation effects of gemcitabine plus cisplatin induction chemotherapy in nasopharyngeal carcinoma

Background

Studies are trying to add immunotherapy to gemcitabine and cisplatin (GP) induction chemotherapy, the standard therapy, in nasopharyngeal carcinoma (NPC) patients with locoregionally advanced disease. However, how the immune system responds to GP remains unknown.

Method

We examined the dynamic changes of circulating immune cells and plasma cytokines in NPC patients administered with GP.

Result

After GP administration, immunosuppressive myeloid cells, including CD11b+CD14+ monocytes, CD33+ myeloid cells, CD33+CD11+ myeloid cells, total MDSCs (CD33+CD11+HLA‐DR−/low), monocytic MDSCs, and granulocytic MDSCs decreased significantly. The regulatory T cells and B cells, two important suppressive lymphocyte subpopulations, also decreased. On the other hand, the levels of CD3+ T cells, total B cells, central memory CD4+ T cells, and pro‐inflammatory cytokines (including Interleukin [IL]‐1β, IL‐6, IL‐2, IL‐5, and IL‐8) increased significantly after GP administration. Besides, GP chemotherapy did not weaken the cytotoxic activity and proliferative capacity of T cells.

Conclusion

Our results showed the immune modulation effect of GP induction chemotherapy in locoregionally advanced NPC, providing a solid basis for its combination with immunotherapy.

Refining TNM-8 M1 categories with anatomic subgroups for previously untreated de novo metastatic nasopharyngeal carcinoma

PURPOSE

To propose a refined M1 classification in de novo metastatic nasopharyngeal carcinoma (NPC) based on pooled data from two academic institutions.

METHODS

Previously untreated de novo M1 NPC patients prospectively treated at The University of Hong Kong (N = 69) and Fujian Cancer Hospital (N = 114) between 2007 and 2016 were recruited and randomized in a 2:1 ratio to generate training (N = 120) and validation (N = 63) cohorts, respectively. Multivariable analysis (MVA) was performed for the training and validation cohorts to identify anatomic prognostic factors for overall survival (OS). Recursive partitioning analysis (RPA) was performed which incorporated the anatomic prognostic factors identified in the MVA to derive Anatomic-RPA groups which stratified OS in the training cohort, and were then validated in the validation cohort.

RESULTS

Median follow-up for the training and validation cohorts was 27.2 and 30.2 months with 3-year OS of 51.6% and 51.1%, respectively. MVA revealed that co-existing liver-bone metastases was the only factor prognostic for OS in both the training and validation cohorts. Anatomic-RPA separated M1 disease into M1a (no co-existing liver-bone metastases) and M1b (co-existing liver-bone metastases) with median OS 39.5 and 23.7 months, respectively (p = 0.004) in the training cohort. RPA for the validation cohort also confirmed good segregation with co-existing liver-bone metastases with median OS 47.7 and 16.0 months, respectively (p = 0.008).

CONCLUSION

Our proposal to subdivide de novo M1 NPC into M1a (no co-existing liver-bone metastases) vs. M1b (co-existing liver-bone metastases) provides better OS segregation.

Prognostic Value of Oral Epstein–Barr Virus DNA Load in Locoregionally Advanced Nasopharyngeal Carcinoma

Background: Plasma Epstein–Barr virus (EBV) DNA load has been widely used for nasopharyngeal carcinoma (NPC) prognostic risk stratification. However, oral EBV DNA load, a non-invasive biomarker that reflects the EBV lytic replication activity, has not been evaluated for its prognostic value in NPC yet.

Methods: A total number of 1,194 locoregionally advanced NPC (LA-NPC) patients from south China were included from a prospective observational cohort (GARTC) with a median follow-up of 107.3 months. Pretreatment or mid-treatment mouthwashes were collected for EBV DNA detection by quantitative polymerase chain reaction (qPCR). The difference of pre- and mid-treatment oral EBV DNA load was tested by the Wilcoxon signed-rank test. The associations of oral EBV DNA load with overall survival (OS), progression-free survival (PFS), distant metastasis–free survival (DMFS), and locoregional relapse-free survival (LRFS) were assessed using the log-rank test and multivariate Cox regression.

Results: The high level of the oral EBV DNA load (>2,100 copies/mL) was independently associated with worse OS (HR = 1.45, 95% CI: 1.20–1.74, p < 0.001), PFS (HR = 1.38, 95% CI: 1.16–1.65, p < 0.001), DMFS (HR = 1.66, 95% CI: 1.25–2.21, p = 0.001), and LRFS (HR = 1.43, 95% CI: 1.05–1.96, p = 0.023). Similar and robust associations between oral EBV DNA load and prognosis were observed for patients in both the pretreatment and mid-treatment stages. The detection rate (71.7 vs. 48.6%, p < 0.001) and the median load of oral EBV DNA (13,368 vs. 382 copies/mL, p < 0.001) for patients in the pretreatment stage were significantly higher than those in the mid-treatment stage. The combination of the oral EBV DNA load and TNM staging provided a more precise risk stratification for the LA-NPC patients.

Conclusion: Oral EBV DNA load was an alternative non-invasive predictor of prognosis and may facilitate risk stratification for the LA-NPC patients.

Development and Validation of Prognostic Survival Nomograms for Patients with Anal Canal Cancer: A SEER-Based Study

Objective

Anal canal cancer is a rare malignancy with increasing incidence in recent times. This study aimed to develop two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with anal canal cancer.

Methods

Information of patients with anal canal cancer from 2004 to 2015 was extracted from the surveillance, epidemiology, and end results (SEER) database. Cox analysis was used to select the risk factors for prognosis, and nomograms were constructed using the R software. The C-index, area under the curve (AUC) of time-dependent receiver operating characteristic (ROC) curves, calibration plot and decision curve analysis (DCA) were used to assess the clinical utility of the nomograms.

Results

A total of 2458 patients with malignant tumours of the anal canal were screened out. Sex, age, marital status, histological type, grade, tumour size, AJCC stage, SEER stage and chemotherapy were independent prognostic factors for OS, whereas sex, age, race, histological type, grade, tumour size, AJCC stage, SEER stage and radiotherapy were independent prognostic factors for CSS. In the training cohort, the C-index value for OS nomogram was 0.73 (95% CI, 0.69-0.77), and the AUC values that predicted the 1-, 3- and 5-year survival rates were 0.764, 0.758 and 0.760, respectively, whereas the C-index value for CSS nomogram model was 0.74 (95% CI, 0.69-0.79), and the AUC values were 0.763, 0.769 and 0.763, respectively. The calibration plot and DCA curves demonstrated good prediction performance of the model in both the training and validation cohorts.

Conclusion

The established nomogram is a visualisation tool that can effectively predict the OS and CSS of patients with anal canal cancer.

Development and validation of radiologic scores for guiding individualized induction chemotherapy in T3N1M0 nasopharyngeal carcinoma

OBJECTIVES

We aimed to develop and validate radiologic scores from [¹⁸F]FDG PET/CT and MRI to guide individualized induction chemotherapy (IC) for patients with T3N1M0 nasopharyngeal carcinoma (NPC).

METHODS

A total of 542 T3N1M0 patients who underwent pretreatment [¹⁸F]FDG PET/CT and MRI were enrolled in the training cohort. A total of 174 patients underwent biopsy of one or more cervical lymph nodes. Failure-free survival (FFS) was the primary endpoint. The radiologic score, which was calculated according to the number of risk factors from the multivariate model, was used for risk stratification. The survival difference of patients undergoing concurrent chemoradiotherapy (CCRT) with or without IC was then compared in risk-stratified subgroups. Another cohort from our prospective clinical trial (N = 353, NCT03003182) was applied for validation.

RESULTS

The sensitivity of [¹⁸F]FDG PET/CT was better than that of MRI (97.7% vs. 87.1%, p < 0.001) for diagnosing histologically proven metastatic cervical lymph nodes. Radiologic lymph node characteristics were independent risk factors for FFS (all p < 0.05). High-risk patients (n = 329) stratified by radiologic score benefited from IC (5-year FFS: IC + CCRT 83.5% vs. CCRT 70.5%; p = 0.0044), while low-risk patients (n = 213) did not. These results were verified again in the validation cohort.

CONCLUSIONS

T3N1M0 patients were accurately staged by both [¹⁸F]FDG PET/CT and MRI. The radiologic score can correctly identify high-risk patients who can gain additional survival benefit from IC and it can be used to guide individualized treatment of T3N1M0 NPC.

KEY POINTS

• [¹⁸F]FDG PET/CT was more accurate than MRI in diagnosing histologically proven cervical lymph nodes. • Radiologic lymph node characteristics were reliable independent risk factors for FFS in T3N1M0 nasopharyngeal carcinoma patients. • High-risk patients identified by the radiologic score based on [¹⁸F]FDG PET/CT and MRI could benefit from the addition of induction chemotherapy.

OUP accepted manuscript

Background

Nasopharyngeal carcinoma is an Epstein-Barr virus (EBV)-associated tumor that is highly common in southern China. Our previous sequencing data demonstrated that the EBV-encoded microRNA BART8-3p was most upregulated in nasopharyngeal carcinoma (NPC) and was closely associated with the metastasis of NPC. However, the values of plasma BART8-3p in NPC patients have not yet been well characterized.

Material and Methods

We quantified plasma BART8-3p expression by quantitative real-time PCR in 205 newly diagnosed NPC patients. Kaplan-Meier analysis was used to compare overall survival (OS), distant metastasis-free survival (DMFS), and locoregional relapse-free survival (LRRFS) between the groups.

Results

Plasma pretreatment BART8-3p was highly expressed in NPC patients compared with healthy controls. Pretreatment BART8-3p yielded a 92% predictive value for detecting NPC. Importantly, BART8-3p decreased dramatically after therapy relative to pretreatment levels. High levels of pretreatment or post-treatment BART8-3p were associated with worse OS, DMFS, and LRRFS. Multivariate analysis showed that high pretreatment or post-treatment BART8-3p was an independent unfavorable prognostic marker for OS (HR 3.82, 95% CI 1.77-8.24, P = .001 or HR 2.74, 95% CI 1.27-5.91, P = .010), DMFS (HR 2.82, 95% CI 1.36-5.85, P = .005 or HR 3.27, 95% CI 1.57-6.81, P = .002), and LRRFS (HR 1.94, 95% CI 1.12-3.35, P = .018 or HR 2.03, 95% CI 1.14-3.62, P = .016) in NPC. Subgroup analysis revealed that for patients with locally advanced NPC with high levels of pretreatment BART8-3p (n = 58), more cycles of chemotherapy (≥6 cycles) tended to prolong OS (P = .070). Over 50% (6/11) patients with high levels of post-treatment BART8-3p presented distant metastasis.

Conclusion

Plasma BART8-3p is a promising biomarker for the detection and prognosis of NPC.

Predictive Value of a Combined Model Based on Pre-Treatment and Mid-Treatment MRI-Radiomics for Disease Progression or Death in Locally Advanced Nasopharyngeal Carcinoma

Purpose

A combined model was established based on the MRI-radiomics of pre- and mid-treatment to assess the risk of disease progression or death in locally advanced nasopharyngeal carcinoma.

Materials and Methods

A total of 243 patients were analyzed. We extracted 10,400 radiomics features from the primary nasopharyngeal tumors and largest metastatic lymph nodes on the axial contrast-enhanced T1 weighted and T2 weighted in pre- and mid-treatment MRI, respectively. We used the SMOTE algorithm, center and scale and box-cox, Pearson correlation coefficient, and LASSO regression to construct the pre- and mid-treatment MRI-radiomics prediction model, respectively, and the risk scores named P score and M score were calculated. Finally, univariate and multivariate analyses were used for P score, M score, and clinical data to build the combined model and grouped the patients into two risk levels, namely, high and low.

Result

A combined model of pre- and mid-treatment MRI-radiomics successfully categorized patients into high- and low-risk groups. The log-rank test showed that the high- and low-risk groups had good prognostic performance in PFS (P<0.0001, HR: 19.71, 95% CI: 12.77–30.41), which was better than TNM stage (P=0.004, HR:1.913, 95% CI:1.250–2.926), and also had an excellent predictive effect in LRFS, DMFS, and OS.

Conclusion

Risk grouping of LA-NPC using a combined model of pre- and mid-treatment MRI-radiomics can better predict disease progression or death.

Prognostic Value of Pretreatment Lymphocyte-to-Monocyte Ratio and Development of a Nomogram in Breast Cancer Patients

Purpose

The objective of this study was to explore the prognostic significance of pretreatment hematologic parameters in predicting disease-free survival (DFS) of breast cancer patients.

Materials and Methods

The medical records of 440 breast cancer patients in Shandong Cancer Hospital and Institute from 2003 to 2013 were analyzed retrospectively. Through the results of blood routine before treatment, the absolute lymphocyte count (ALC), absolute neutrophil count (ANC), absolute monocyte count (AMC), and absolute platelet count (APC) in peripheral blood were collected. The lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-monocyte ratio (NMR) were calculated. Cox proportional hazard model was used for univariate and multivariate analysis. The DFS was compared using Kaplan–Meier method. The prognostic nomogram of patients with breast cancer was developed.

Results

The median DFS for all patients was 64.10 months. Univariate analysis showed that the DFS was associated with surgical approach, TNM stage, molecular subtype, neoadjuvant chemotherapy, radiotherapy, and LMR (p < 0.05). TNM stage, molecular subtype, and LMR were independent prognostic factors of breast cancer in multivariate analysis (p < 0.05). According to the Kaplan–Meier survival curve analysis, patients with higher LMR (≥4.85) were associated with longer median DFS (median DFS, 85.83 vs. 60.90, p < 0.001). The proposed nomogram that incorporated LMR, TNM stage, and molecular subtype got a concordance index (c-index) of 0.69 in predicting 5-year DFS.

Conclusion

In breast cancer patients, higher LMR was associated with longer median DFS and the nomogram including LMR, TNM stage, and molecular subtype could accurately predict the prolonged 5-year DFS of breast cancer patients.

Second Primary Malignancy in Patients with Hypopharyngeal Carcinoma: A SEER-Based Study

Background

A population-based analysis of the risk of secondary primary malignancy (SPM) in patients with hypopharyngeal carcinoma (HPC) has been lacking in the literature. Therefore, we conducted this study to determine the risk factors and assess the effects of SPM on the overall survival (OS) and cancer-specific survival (CSS) of patients with HPC.

Methods

Data on selected patients diagnosed with HPC from the Surveillance, Epidemiology and End Results (SEER) database between 1973 and 2015 were examined through logistic regression, Cox regression and nomogram methods.

Results

The overall risk of SPM in patients with HPC was higher than that in the general population (SIR: 2.77; P < 0.05). The specific-site, including the oral cavity, pharynx, digestive system, respiratory system and endocrine system, had a relatively higher risk of SPM. The overall risks of the subgroup of people 55–75 years of age and all subgroups of sex, race and latency were significantly elevated. In addition, patients with HPC were more likely to have been diagnosed in 2010–2015 (vs 2004–2009; P = 0.002), to be unmarried (vs married; P = 0.008), to have distant metastasis (vs no metastasis; P = 0.016) and to have had no surgery for the first tumor (vs surgery for the first tumor; P = 0.021), and these aspects were associated with a significantly elevated risk of developing SPM. SPM was independently associated with better OS and CSS. The OS and CSS in patients with HPC with SPM were better than those in patients without SPM (log rank P < 0.0001). The C indexes of the nomogram constructed with ten influencing factors including SPM were 0.681:0.699 for OS and 0.705:0.724 for CSS (training cohort:validation cohort).

Conclusion

Although the overall risk of SPM in patients with HPC was elevated, SPM did not decrease the OS and CSS in patients with HPC. This finding is inconsistent with clinical observations and thus requires further research and exploration. It possibly because HPC might have a shorter survival time, or the follow-up time was not long enough.

Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma

BACKGROUND

Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the predictive performance of the computed tomography (CT)-based model using deep learning features for identification of responders and nonresponders to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC).

MATERIALS AND METHODS

All eligible patients were included retrospectively between January 2012 and December 2018, and assigned to the training (n = 208) or the testing cohort (n = 89). We extracted deep learning features of six pretrained convolutional neural networks (CNNs) via transfer learning method, and handcrafted radiomics features manually. Support vector machine (SVM) was adopted as the classifier. All predictive models were evaluated using the area under the receiver operating characteristics curve (AUC), by which an optimal model was selected. We also built clinical and clinical-radiological models for comparison.

RESULTS

The model with features extracted from ResNet50 (RN-SVM) had optimal performance among all models with features extracted from pretrained CNNs with an AUC of 0.811, accuracy of 68.54%, sensitivity of 61.54%, specificity of 87.50%, positive predictive value (PPV) of 93.02%, and negative predictive value (NPV) of 45.65% in the testing cohort. The handcrafted radiomics model was slightly inferior to the RN-SVM model with an AUC of 0.663 and accuracy of 60.67% in the testing cohort. All the imaging-derived models had better predictive performance than the clinical model.

CONCLUSION

The noninvasive deep learning method could provide efficient prediction of treatment response to IC in locally advanced NPC and might be a practicable approach in therapeutic strategy decision-making.

A Five-MicroRNA Signature Predicts the Prognosis in Nasopharyngeal Carcinoma

Background

There is no effective prognostic signature that could predict the prognosis of nasopharyngeal carcinoma (NPC).

Methods

We constructed a prognostic signature based on five microRNAs using random forest and Least Absolute Shrinkage And Selection Operator (LASSO) algorithm on the GSE32960 cohort (N = 213). We verified its prognostic value using three independent external validation cohorts (GSE36682, N = 62; GSE70970, N = 246; and TCGA-HNSC, N = 523). Through principal component analysis, receiver operating characteristic curve analysis, and C-index calculation, we confirmed the predictive accuracy of this prognostic signature.

Results

We calculated the risk score based on the LASSO algorithm and divided the patients into high- and low-risk groups according to the calculated optimal cutoff value. The patients in the high-risk group tended to have a worse prognosis outcome and chemotherapy response. The time-dependent receiver operating characteristic curve showed that the 1-year overall survival rate of the five-microRNA signature had an area under the curve of more than 0.83. A functional annotation analysis of the five-microRNA signature showed that the patients in the high-risk group were usually accompanied by activation of DNA repair and MYC-target pathways, while the patients in the low-risk group had higher immune-related pathway signals.

Conclusions

We constructed a five-microRNA prognostic signature, which could accurately predict the prognosis of nasopharyngeal carcinoma, and constructed a nomogram that could conveniently predict the overall survival of patients.