Development and external validation of nomograms predictive of response to radiation therapy and overall survival in nasopharyngeal cancer patients

Development and validation of a dynamic nomogram for acute kidney injury prediction in ICU patients with acute heart failure

Objective

Developing and validating a simple and clinically useful dynamic nomogram for predicting early acute kidney injury (AKI) in patients with acute heart failure (AHF) admitted to the intensive care unit (ICU).

Methods

Clinical data from patients with AHF were obtained from the Medical Information Mart for Intensive Care IV database. The patients with AHF were randomly allocated into derivation and validation sets. The independent predictors for AKI development in AHF patients were identified through least absolute shrinkage and selection operator and multivariate logistic regression analyses. A nomogram was developed based on the results of the multivariable logistic regression to predict early AKI onset in AHF patients, which was subsequently implemented as a web-based calculator for clinical application. An evaluation of the nomogram was conducted using discrimination, calibration curves, and decision curve analyses (DCA).

Results

After strict screening, 1,338 patients with AHF were included in the derivation set, and 3,129 in the validation set. Sepsis, use of human albumin, age, mechanical ventilation, aminoglycoside administration, and serum creatinine levels were identified as predictive factors for AKI in patients with AHF. The discrimination of the nomogram in both the derivation and validation sets was 0.81 (95% confidence interval: 0.78-0.83) and 0.79 (95% confidence interval: 0.76-0.83). Additionally, the calibration curve demonstrated that the predicted outcomes aligned well with the actual observations. Ultimately, the DCA curves indicated that the nomogram exhibited favorable clinical applicability.

Conclusion

The nomogram that integrates clinical risk factors and enables the personalized prediction of AKI in patients with AHF upon admission to the ICU, which has the potential to assist in identifying AHF patients who would derive the greatest benefit from interventions aimed at preventing and treating AKI.

Nuclear protein NOP2 serves as a poor-prognosis predictor of LUAD and aggravates the malignancy of lung adenocarcinoma cells

Recent studies have shown that NOP2, a nucleolar protein, is up-regulated in various cancers, suggesting a potential link to tumor aggressiveness and unfavorable outcomes. This study examines NOP2's role in lung adenocarcinoma (LUAD), a context where its implications remain unclear. Utilizing bioinformatics, we assessed 513 LUAD and 59 normal tissue samples from The Cancer Genome Atlas (TCGA) to explore NOP2's diagnostic and prognostic significance in LUAD. Additionally, in vitro experiments compared NOP2 expression between Beas-2b and A549 cells. Advanced databases and analytical tools, including LINKEDOMICS, STRING, and TISIDB, were employed to further elucidate NOP2's association with LUAD. Our findings indicate a significantly higher expression of NOP2 mRNA and protein in A549 cells compared to Beas-2b cells (P < 0.001). In LUAD, elevated NOP2 levels were linked to decreased Overall Survival (OS) and advanced clinical stages. Univariate Cox analysis revealed that high NOP2 expression correlated with poorer OS in LUAD (P < 0.01), a finding independently supported by multivariate Cox analysis (P < 0.05). The relationship between NOP2 expression and LUAD risk was presented via a Nomogram. Additionally, Gene Set Enrichment Analysis (GSEA) identified seven NOP2-related signaling pathways. A focal point of our research was the interplay between NOP2 and tumor-immune interactions. Notably, a negative correlation was observed between NOP2 expression and the immune infiltration levels of macrophages, neutrophils, mast cells, Natural Killer (NK) cells, and CD8 + T cells in LUAD. Moreover, the expression of NOP2 was related to the sensitivity of various chemotherapeutic drugs. In vitro, we found that downregulating NOP2 can decrease the proliferation, migration and invasion of A549 cells. Furthermore, NOP2 can regulate Caspase3-mediated apoptosis. Collectively, particularly regarding prognosis, immune infiltration and vitro experiments, these findings suggest NOP2's potential of serving as a poor-prognostic biomarker for LUAD and aggravating the malignancy of lung adenocarcinoma cells.

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.

Two web-based dynamically interactive nomograms and risk stratification systems for predicting survival outcomes and guiding treatment in non-metastatic nasopharyngeal carcinoma

BACKGROUND

A nomogram is a valuable and easily accessible tool for individualizing cancer prognosis. This study aims to establish and validate two prognostic nomograms for long-term overall survival (OS) and cancer-specific survival (CSS) in non-metastatic nasopharyngeal carcinoma (NPC) patients and to investigate the treatment options for the nomogram-based risk stratification subgroups.

METHODS

A total of 3959 patients with non-metastatic NPC between 2004 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated to the training and validation cohorts in a 7:3 ratio. Prognostic nomograms were constructed to estimate OS and CSS by integrating significant variables from multivariate Cox regression employing a backward stepwise method. We examined the correlation indices (C-index) and areas under the curves (AUC) of time-dependent receiver operating characteristic curves to assess the discriminative ability of our survival models. The comprehensive enhancements of predictive performance were evaluated with net reclassification operating improvement (NRI) and integrated discrimination improvement (IDI). Reliability was validated using calibration plots. Decision curve analysis (DCA) was used to estimate clinical efficacy and capability. Finally, the nomogram-based risk stratification system used Kaplan-Meier survival analysis and log-rank tests to examine differences between subgroups.

RESULTS

The following independent parameters were significant predictors for OS: sex, age, race, marital status, histological type, median household income, AJCC stage tumor size, and lymph node size. Except for the race variables mentioned above, the rest were independent prognostic factors for CSS. The C-index, AUC, NRI, and IDI indicated satisfactory discriminating properties. The calibration curves exhibited high concordance with the exact outcomes. Moreover, the DCA demonstrated performed well for net benefits. The prognosis significantly differed between low- and high-risk patients (p < 0.001). In a treatment-based stratified survival analysis in risk-stratified subgroups, chemotherapy benefited patients in the high-risk group compared to radiotherapy alone. Radiotherapy only was recommended in the low-risk group.

CONCLUSIONS

Our nomograms have satisfactory performance and have been validated. It can assist clinicians in prognosis assessment and individualized treatment of non-metastatic NPC patients.

High GTSE1 expression promotes cell proliferation, metastasis and cisplatin resistance in ccRCC and is associated with immune infiltrates and poor prognosis

Background: Clear cell renal cell carcinoma is the most common and fatal form of kidney cancer, accounting for 80% of new cases. Although it has been reported that GTSE1 is highly expressed in a variety of tumors and associated with malignant progression and poor clinical prognosis, its clinical significance, correlations with immune cell infiltration and biological function in ccRCC are still poorly understood.

Methods: The gene expression, clinicopathological features, and clinical significance of GTSE1 were analyzed using multiple databases, including TCGA, GEO, TIMER, and UALCAN Kaplan–Meier survival analysis, gene set enrichment analysis gene ontology enrichment Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed. Tumor-infiltrating immune cells and immunomodulators were extracted and analyzed using TCGA-KIRC profiles. Protein‒protein interactions were built using the STRING website. The protein level of GTSE1 in ccRCC patients was detected by immunohistochemistry using a ccRCC tissue chip. Finally, MTT assays, colony-formation assays, cell flow cytometry analyses, EdU-staining assays, wound-healing assays, and transwell migration and invasion assays were conducted to assess the biological function of GTSE1 in vitro.

Results: GTSE1 was overexpressed in ccRCC tissues and cells, and GTSE1 overexpression was associated with adverse clinical-pathological factors and poor clinical prognosis. Meanwhile, the functional enrichment analysis indicated that GTSE1 and its coexpressed genes were mainly related to the cell cycle, DNA replication, and immunoreaction, such as T-cell activation and innate immune response, through multiple signaling pathways, including the P53 signaling pathway and T-cell receptor signaling pathway. Furthermore, we observed a significant relationship between GTSE1 expression and the levels of infiltrating immune cells in ccRCC. Biological functional studies demonstrated that GTSE1 could promote the malignant progression of ccRCC by promoting cell proliferation, cell cycle transition, migration, and invasion capacity and decreasing the sensitivity of ccRCC cells to cisplatin.

Conclusion: Our results indicate that GTSE1, serving as a potential oncogene, can promote malignant progression and cisplatin resistance in ccRCC. Additionally, high GTSE1 expression contributes to an increased level of immune cell infiltration and is associated with a worse prognosis, providing a potential target for tumor therapy in ccRCC.

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.

BCHE as a Prognostic Biomarker in Endometrial Cancer and Its Correlation with Immunity

Background

In developed countries, the most common gynecologic malignancy is endometrial carcinoma (EC), making the identification of EC biomarkers extremely essential. As a natural enzyme, butyrylcholinesterase (BCHE) is found in hepatocytes and plasma. There is a strong correlation between BCHE gene mutations and cancers and other diseases. The aim of this study was to analyze the role of BCHE in patients with EC.

Methods

A variety of analyses were conducted on The Cancer Genome Atlas (TCGA) data, including differential expression analysis, enrichment analysis, immunity, clinicopathology, and survival analysis. The Gene Expression Omnibus (GEO) database was used to validate outcomes. Using R tools, Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) analyses revealed the potential mechanisms of BCHE in EC. Sangerbox tools were used to delve into the relations between BCHE expression and tumor microenvironment, including microsatellite instability (MSI), tumor neoantigen count (TNC), and tumor mutation burden (TMB). BCHE's genetic alteration analysis was conducted by cBioPortal. In addition, the Human Protein Atlas (HPA) was used to validate the outcomes by immunohistochemistry, and an analysis of the protein-protein interaction network (PPI) was performed with the help of the STRING database.

Results

Based on our results, BCHE was a significant independent prognostic factor for patients with EC. The prognosis with EC was affected by age, stage, grade, histological type, and BCHE. GSEA showed that BCHE was closely related to pathways regulating immune response, including transforming growth factor-β (TGF-β) signaling pathways and cancer immunotherapy through PD1 blockade pathways. The immune analysis revealed that CD4+ regulatory T cells (Tregs) were negatively correlated with BCHE expression and the immune checkpoint molecules CD28, ADORA2A, BTNL2, and TNFRSF18 were all significantly related to BCHE. BCHE expression was also associated with TMB by genetic alteration analysis.

Conclusions

Identifying BCHE as a biomarker for EC might help predict its prognosis and could have important implications for immunotherapy.

Utility of diffusion-weighted magnetic resonance imaging in predicting the treatment response of nasopharyngeal carcinoma

OBJECTIVE

Predicting the treatment response in patients with nasopharyngeal carcinoma (NPC) is challenging. This study evaluated the utility of diffusion-weighted imaging (DWI) in predicting the treatment response in patients with NPC.

METHODS

We prospectively enrolled 33 patients with newly diagnosed NPC who underwent magnetic resonance imaging with the propeller DWI and apparent diffusion coefficient (ADC) map before and at 5 weeks after chemoradiation. The following ADC values of the primary tumor were calculated: pre-treatment ADC (pre-ADC), pre-treatment ADC ratio (pre-ADC ratio), ADC change (▵ADC), ADC change ratio (▵ADC ratio), and percentage of ADC change (▵%ADC). The correlations between these parameters and treatment outcomes were explored, and the patients were classified as good responders (complete response) and poor responders (stable disease, partial response, or progressive disease) based on the Response Evaluation Criteria in Solid Tumors, version 1.1.

RESULTS

The ▵ADC, ▵ADC ratio, and ▵%ADC were significantly lower in the poor-responder group (n = 12) than in the good-responder group (n = 21; p = 0.001, p = 0.002, and p = 0.004, respectively). There was no significant difference between groups in the pre-ADC and pre-ADC ratios (p = 0.602 and p = 0.685, respectively). The optimal ▵ADC, ▵ADC ratio, and ▵%ADC cutoff values for predicting poor response were >0.65 mm²/sec, 0.28, and 60%, respectively (sensitivity: 83.3%, 75%, and 83.3%; specificity: 71.4%, 85.7%, and 71.4%, respectively).

CONCLUSION

The ▵ADC, ▵ADC ratio, and ▵%ADC obtained during the pre-treatment and mid-treatment periods could be potential biomarkers for predicting treatment response in patients with NPC.

Nucleolar protein NOP2 could serve as a potential prognostic predictor for clear cell renal cell carcinoma

As an indispensable part for cancer precision medicine, biomarkers and signatures for predicting cancer prognosis and therapeutic benefits were urgently required. The purpose of this study was to investigate the prognostic roles of NOP2 in renal clear cell carcinoma (ccRCC) for overall survival (OS) and its relationships with immunity. NOP2-related gene expression matrix associated with clinical information was obtained from the Cancer Genome Atlas (TCGA) ccRCC dataset and NOP2-related pathways were identified by gene set enrichment analysis (GSEA). Associations among the NOP2 expression and MSI, TMB, TNB, and immunity were also explored. Both the NOP2 mRNA and protein/phosphoprotein had a higher expression in ccRCC tumor tissues than in normal kidney tissues (both P < 0.001) and elevated NOP2 expression was associated with poor OS (P < 0.001). Logistic regression analysis revealed the NOP2 expression was significantly linked to stage, age, grade, N stage, T stage, and M stage (all P < 0.05). Univariate/multivariate Cox hazard regression analysis results indicated that NOP2 was an independent prognostic factor for OS in ccRCC and GSEA revealed five NOP2-related signaling pathways. Nomogram based on NOP2 and eight clinical characteristic parameters (grade, age, stage, gender, T stage, race, M stage, N stage) was constructed and carefully evaluated. Furthermore, NOP2 gene expression was also found to be significantly related to MSI, TMB, and immunity. Our findings revealed that NOP2 might be a potential prognostic factor for OS in ccRCC and it was significantly associated with immunity, MSI, and TMB.

A novel model with nutrition-related parameters for predicting overall survival of cancer patients

BACKGROUND

Increasing evidence indicates that nutritional status could influence the survival of cancer patients. This study aims to develop and validate a nomogram with nutrition-related parameters for predicting the overall survival of cancer patients.

PATIENTS AND METHODS

A total of 8749 patients from the multicentre cohort study in China were included as the primary cohort to develop the nomogram, and 696 of these patients were recruited as a validation cohort. Patients' nutritional status were assessed using the PG-SGA. LASSO regression models and Cox regression analysis were used for factor selection and nomogram development. The nomogram was then evaluated for its effectiveness in discrimination, calibration, and clinical usefulness by the C-index, calibration curves, and decision curve analysis. Kaplan-Meier survival curves were used to compare the survival rate.

RESULTS

Seven independent prognostic factors were identified and integrated into the nomogram. The C-index was 0.73 (95% CI, 0.72 to 0.74) and 0.77 (95% CI, 0.74 to 0.81) for the primary cohort and validation cohort, which were both higher than 0.59 (95% CI, 0.58 to 0.61) of the TNM staging system. DCA demonstrated that the nomogram was higher than the TNM staging system and the TNM staging system combined with PG-SGA. Significantly median overall survival differences were found by stratifying patients into different risk groups (score < 18.5 and ≥ 18.5) for each TNM category (all Ps < 0.001).

CONCLUSION

Our study screened out seven independent prognostic factors and successfully generated an easy-to-use nomogram, and validated and shown a better predictive validity for the overall survival of cancer patients.

Construction and validation of a nomogram for predicting cancer-specific survival in hepatocellular carcinoma patients

The prognosis of patients with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a research hotspot. This study aimed to incorporate important factors obtained from SEER database to construct and validate a nomogram for predicting the cancer-specific survival (CSS) of patients with HCC and ICC. We obtained patient data from SEER database. The nomogram was constructed base on six prognostic factors for predicting CSS rates in HCC patients. The nomogram was validated by concordance index (C-index), the receiver operating characteristic (ROC) curve and calibration curves. A total of 3227 patients diagnosed with HCC (3038) and ICC (189) between 2010 and 2015 were included in this study. The C-index of the nomogram for HCC patients was 0.790 in the training cohort and 0.806 in the validation cohort. The 3- and 5-year AUCs were 0.811 and 0.793 in the training cohort. The calibration plots indicated that there was good agreement between the actual observations and predictions. In conclusion, we constructed and validated a nomogram for predicting the 3- and 5-year CSS in HCC patients. We have confirmed the precise calibration and excellent discrimination power of our nomogram.

Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer

BACKGROUND

The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling.

OBJECTIVES

This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER) program database. The comparison is necessary to provide the clinicians with a comprehensive, practical, and most accurate assistive system to predict overall survival of this patient population.

METHODS

The data set used included the records of 7596 tongue cancer patients. The considered machine learning algorithms were logistic regression, support vector machine, Bayes point machine, boosted decision tree, decision forest, and decision jungle. These algorithms were mainly evaluated in terms of the areas under the receiver-operating characteristic (ROC) curve (AUC) and accuracy values. The performance of the algorithm that produced the best result was compared with a nomogram to predict overall survival in tongue cancer patients.

RESULTS

The boosted decision-tree algorithm outperformed other algorithms. When compared with a nomogram using external validation data, the boosted decision tree produced an accuracy of 88.7% while the nomogram showed an accuracy of 60.4%. In addition, it was found that age of patient, T stage, radiotherapy, and the surgical resection were the most prominent features with significant influence on the machine learning model's performance to predict overall survival.

CONCLUSION

The machine learning model provides more personalized and reliable prognostic information of tongue cancer than the nomogram. However, the level of transparency offered by the nomogram in estimating patients' outcomes seems more confident and strengthened the principle of shared decision making between the patient and clinician. Therefore, a combination of a nomogram - machine learning (NomoML) predictive model may help to improve care, provides information to patients, and facilitates the clinicians in making tongue cancer management-related decisions.

Development of a nomogram to predict prognosis in ovarian cancer: a SEER-based study

Background

Ovarian cancer remains the most lethal gynecologic malignancy. In this study, we aimed to identify the specific risk factors affecting overall survival (OS) and develop a nomogram for prognostic prediction of ovarian cancer patients based on data from the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

Information from the SEER database on ovarian cancer between 2004 and 2016 was screened and retrieved. Cases were randomly divided into the training cohort hand the validation cohort at a 7:3 ratio. The prognostic effects of individual variables on survival were evaluated via Kaplan-Meier method and Cox proportional hazards regression model using data from the training cohort. A nomogram was formulated to predict the 3- and 5-year OS rates of patients with ovarian cancer, and then validated both in the training cohort and the validation cohort.

Results

A total of 28,375 patients were selected from 75,921 samples (19,862 in training cohort and 8,513 in validation cohort). Cox regression analysis identified race, age laterality, histology, stage, grade, surgery, chemotherapy, radiotherapy, and marital status as independent risk factors for ovarian cancer prognosis. A nomogram was developed based on the results of multivariate analysis and validated using an internal bootstrap resampling approach, which demonstrated a sufficient level of discrimination according to the C-index (0.752, 95% CI: 0.746–0.758 in the training cohort, 0.755, 95% CI: 0.746–0.764).

Conclusions

We developed a nomogram valuable for accurate prediction of 3- and 5-year OS rates of ovarian cancer patients based on individual characteristics.

Applying nomograms based on the surveillance, epidemiology and end results database to predict long-term overall survival and cancer-specific survival in patients with oropharyngeal squamous cell carcinomas: A case-control research

Few models regarding to the individualized prognosis assessment of oropharyngeal squamous cell carcinoma (OPSCC) patients were documented. The purpose of this study was to establish nomogram model to predict the long-term overall survival (OS) and cancer-specific survival (CSS) of OPSCC patients. The detailed clinical data for the 10,980 OPSCC patients were collected from the surveillance, epidemiology and end results (SEER) database. Furthermore, we applied a popular and reasonable random split-sample method to divide the total 10,980 patients into 2 groups, including 9881 (90%) patients in the modeling cohort and 1099 (10%) patients in the external validation cohort. Among the modeling cohort, 3084 (31.2%) patients were deceased at the last follow-up date. Of those patients, 2188 (22.1%) patients died due to OPSCC. In addition, 896 (9.1%) patients died due to other causes. The median follow-up period was 45 months (1-119 months). We developed 2 nomograms to predict 5- and 8- year OS and CSS using Cox Proportional Hazards model. The nomograms' accuracy was evaluated through the concordance index (C-index) and calibration curves by internal and external validation. The C-indexes of internal validation on the 5- and 8-year OS and CSS were 0.742 and 0.765, respectively. Moreover, the C-indexes of external validation were 0.740 and 0.759, accordingly. Based on a retrospective cohort from the SEER database, we succeeded in constructing 2 nomograms to predict long-term OS and CSS for OPSCC patients, which provides reference for surgeons to develop a treatment plan and individual prognostic evaluations.

Establishment and Validation of a Nomogram for Nasopharyngeal Carcinoma Patients Concerning the Prognostic Effect of Parotid Lymph Node Metastases

Purpose

The prognosis of nasopharyngeal carcinoma (NPC) patients with parotid lymph node (PLN) metastasis remains unclear. This study was performed to investigate the prognostic significance and optimal staging category of PLN metastasis and develop a nomogram for estimating individual risk.

Materials and Methods

Clinical data of 7,084 non-metastatic NPC patients were retrospectively reviewed. Overall survival (OS) was the primary endpoint. A nomogram was established based on the Cox proportional hazards regression model. The accuracy and calibration ability of this nomogram was evaluated by C-index and calibration curves with bootstrap validation.

Result

Totally, 164/7,084 NPC patients (2.3%) presented with PLNs. Multivariate analyses showed that PLN metastasis was a negative prognostic factor for OS, progression-free survival (PFS), distant metastasis-free survival (DMFS), and locoregional relapse-free survival (LRFS). Patients with PLN metastasis had a worse prognosis than N3 disease. Five independent prognostic factors were included in the nomogram, which showed a C-index of 0.743. The calibration curves for probability of 3- and 5-year OS indicated satisfactory agreement between nomogram-based prediction and actual observation. All results were confirmed in the validation cohort.

Conclusion

NPC patient with PLN metastasis had poorer survival outcome (OS, PFS, DMFS, and LRFS) than N3 disease. We developed a nomogram to provide individual prediction of OS for patients with PLN metastasis.

Establishment and validation of a nomogram with intratumoral heterogeneity derived from 18F-FDG PET/CT for predicting individual conditional risk of 5-year recurrence before initial treatment of nasopharyngeal carcinoma

Background

Intratumoral heterogeneity has an enormous effect on patient treatment and outcome. The purpose of the current study was to establish and validate a nomogram with intratumoral heterogeneity derived from ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for prognosis of 5-Year progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC).

Methods

A total of 171 NPC patients who underwent pretreatment ¹⁸F-FDG PET/CT were retrospectively enrolled. Data was randomly divided into training cohort (n = 101) and validation cohort (n = 70). The clinicopathologic parameters and the following PET parameters were analyzed: maximum and mean standardized uptake value (SUVmax, SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI, SUVmax/SUVmean) for primary tumor and maximal neck lymph node. Cox analyses were performed on PFS in the training cohort. A prognostic nomogram based on this model was developed and validated.

Results

For the primary tumor, MTV-2.5, TLG-2.5, MTV-70%, and TLG-70% were significantly correlated with PFS. For the maximal neck lymph node, short diameter and HI were significantly correlated with PFS. Among the clinicopathologic parameters, M stage was a significant prognostic factor for recurrence. In multivariate analysis, M stage (P = 0.006), TLG-T-70% (P = 0.002), and HI-N (P = 0.018) were independent predictors. Based on this prognostic model, a nomogram was generated. The C-index of this model was 0.74 (95% CI: 0.63–0.85). For the cross validation, the C-index for the model was 0.73 (95% CI: 0.62–0.83) with the validation cohort. Patients with a risk score of ≥111 had poorer survival outcomes than those with a risk score of 0–76 and 77–110.

Conclusions

Intratumoral heterogeneity derived from ¹⁸F-FDG PET/CT could predict long-term outcome in patients with primary NPC. A combination of PET parameters and the TNM stage enables better stratification of patients into subgroups with different PFS rates.

Development and validation of a nomogram for predicting long-term overall survival in nasopharyngeal carcinoma: A population-based study

We aimed to develop a nomogram based on a population-based cohort to estimate the individualized overall survival (OS) for patients with nasopharyngeal carcinoma (NPC) and compare its predictive value with that of the traditional staging system.Data for 3693 patients with NPC were extracted from the Surveillance, Epidemiology, and End Results dataset and randomly divided into two sets: training (n = 2585) and validation (n = 1108). On the basis of multivariate Cox regression analysis, a nomogram was constructed to predict the 3-, 5-, and 10-year survival probability for a patient. The performance of the nomogram was quantified with respect to discrimination, calibration, and clinical utility.In the training set, age, sex, race, marital status, histological type, T stage, N stage, M stage, radiotherapy, and chemotherapy were selected to develop a nomogram for predicting the OS probability based on the multivariate Cox regression model. The nomogram was generally more discriminative compared with the American Joint Committee on Cancer 7th staging system. Calibration plots exhibited an excellent consistency between the observed probability and the nomogram's prediction. Categorical net classification improvement and integrated discrimination improvement suggested that the predictive accuracy of the nomogram exceeded that of the classic staging system. With respect to decision curve analyses, the nomogram exhibited preferable net benefit gains than the staging system across a wide range of threshold probabilities.This proposed nomogram exhibits an excellent performance with regard to its predictive accuracy, discrimination capability, and clinical utility, and thus can be used as a convenient and reliable tool for prognosis prediction in patients with NPC.

Nomogram predicting long-term overall survival and cancer-specific survival of lip carcinoma patients based on the SEER database: A retrospective case-control study

Our study was designed to construct nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of lip carcinoma patients.A search of the Surveillance, Epidemiology, and End Results (SEER) database provided us with detailed clinical data of the 1780 lip carcinoma patients. On the basis of the credible random split-sample method, the 1780 patients were placed into 2 groups, with 890 patients in the modeling group and 890 patients in the counterpart's group (proportion = 1:1). By employing Kaplan-Meier univariate and Cox multivariate survival analyses based on the modeling cohort, the nomograms were developed and then used to divide the modeling cohort into low-risk cohort and high-risk cohort. The survival rates of the 2 groups were calculated. Internal and external evaluation of nomogram accuracy was performed by the concordance index (C-index) and calibration curves.With regard to 5- and 8-year OS and CSS, the C-indexes of internal validation were 0.762 and 0.787, whereas those of external validation reached 0.772 and 0.818, respectively. All the C-indexes were higher than 0.7. The survival curves of the low-risk cohort were obviously better than those of the high-risk cohort.Credible nomograms have been established based on the SEER large-sample population research. We believe these nomograms can contribute to the design of treatment plans and evaluations of individual prognosis.

Nomograms for predicting long-term overall survival and cancer-specific survival in lip squamous cell carcinoma: A population-based study

BACKGROUND

The goal of this study was to establish and validate two nomograms for predicting the long-term overall survival (OS) and cancer-specific survival (CSS) in lip squamous cell carcinoma (LSCC).

METHODS

This study selected 4175 patients who were diagnosed with LSCC between 2004 and 2015 in the SEER (Surveillance, Epidemiology, and End Results) database. The patients were allocated randomly to a training cohort and validation cohort. Variables were selected using a backward stepwise method in a Cox regression model. Based on the predictive model with the identified prognostic factors, nomograms were established to predict the 3-, 5-, and 8-year survival OS and CSS rates of LSCC patients. The accuracy of the nomograms was evaluated based on the consistency index (C-index), while their prediction accuracy was evaluated using calibration plots. Decision curve analyses (DCAs) were used to evaluate the performance of our survival model.

RESULTS

The multivariate analyses demonstrated that age at diagnosis, marital status, sex, race, American Joint Committee on Cancer stage, surgery status, and radiotherapy status were risk factors for both OS and CSS. The C-index, area under the time-dependent receiver operating characteristic curve, and calibration plots demonstrated the good performance of the nomograms. DCAs of both nomograms further showed that they exhibited good 3-, 5-, and 8-year net benefits.

CONCLUSIONS

We have developed and validated LSCC prognosis nomograms for OS and CSS for the first time. These nomograms can be valuable tools for clinical practice when clinicians are helping patients to understand their survival risk for the next 3, 5, and 8 years.

Personalized prognostication in head and neck cancer: A systematic review of nomograms according to the AJCC precision medicine core (PMC) criteria

BACKGROUND

The American Joint Committee on Cancer (AJCC) Precision Medicine Core (PMC) has recognized the need for more personalized probabilistic predictions above the "TNM" staging system and has recently released a checklist of inclusion and exclusion criteria for evaluating prognostic models.

METHODS

A systematic review of articles in which nomograms were created for head and neck cancer (HNC) was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The AJCC PMC criteria were used to score the individual studies.

RESULTS

Forty-four studies were included in the final qualitative analysis. The mean number of inclusion criteria met was 9.3 out of 13, and the mean number of exclusion criteria met was 2.1 out of 3. Studies were generally of high quality, but no single study fulfilled all of the AJCC PMC criteria.

CONCLUSION

This is the first study to utilize the AJCC checklist to comprehensively evaluate the published prognostic nomograms in HNC. Future studies should attempt to adhere to the AJCC PMC criteria. Recommendations for future research are given.

SUMMARY

The AJCC recently released a set of criteria to grade the quality of prognostic cancer models. In this study, we grade all published nomograms for head and neck cancer according to the new guidelines.

Clinical and prognostic analyses of 110 patients with N3 nasopharyngeal carcinoma

OBJECTIVE

To analyze the clinical outcome and prognostic factors of N3 nasopharyngeal carcinomas (NPCs), provide a basis for rational treatment and improve the cure rate.

METHODS

A total of 110 patients with a pathologically confirmed diagnosis of N3 (NPC 2008 stage in China) NPC from our hospital were retrospectively included in the study conducted from April 2007 to July 2011. All patients received intensity-modulated radiation therapy. Some of these patients received various chemotherapies. The doses of the planning gross primary tumor and retropharyngeal lymph node volume, high-risk planning tumor volume, low-risk planning tumor volume, and gross tumor volume of neck lymph nodes were 6000 to 7600, 5400 to 6600, 5000 to 6000, and 6000 to 6996 cGy, respectively. The Kaplan-Meier analysis and logrank test were carried out to calculate and compare the survival rates of the patients, and the Statistical Package for the Social Sciences software version 17.0 was used for all analyses. Meanwhile, the Cox model was used to analyze the prognostic factors.

RESULTS

In this study, the 1-, 3-, and 5-year overall survival rates of the patients were 92.63%, 83.16%, and 70.53%, respectively. Based on the univariate analysis, T stage (P = .043) and chemotherapy (P = .003) were significant factors for survival. In the multivariate analysis, only chemotherapy influenced survival (). Recent toxicity included radioactive oral mucosa inflammation and skin injury, and difficulty opening the mouth and hearing loss were considered late adverse reactions. None of the patients died during treatment.(Table is included in full-text article.) CONCLUSIONS:: Patients with N3 NPC are at high risk of distant metastasis, and their 5-year survival rate is poor. The more important prognostic factors were T stage and chemotherapy. Patients with N3 NPC should be treated with combined chemotherapy and radiotherapy.

External validity of a prognostic nomogram for locoregionally advanced nasopharyngeal carcinoma based on the 8th edition of the AJCC/UICC staging system: a retrospective cohort study

BACKGROUND

The tumor-node-metastasis (TNM) staging system does not perform well for guiding individualized induction or adjuvant chemotherapy for patients with locoregionally advanced nasopharyngeal carcinoma (NPC). We attempted to externally validate the Pan's nomogram, developed based on the 8th edition of the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) staging system, for patients with locoregionally advanced disease. In addition, we investigated the reliability of Pan's nomogram for selection of participants in future clinical trials.

METHODS

This study included 535 patients with locoregionally advanced NPC who were treated between March 2007 and January 2012. The 5-year overall survival (OS) rates were calculated using the Kaplan-Meier method and compared with predicted outcomes. The calibration was tested using calibration plots and the Hosmer-Lemeshow test. Discrimination ability, which was assessed using the concordance index, as compared with other predictors.

RESULTS

Pan's nomogram was observed to underestimate the 5-year OS of the entire cohort by 8.65% [95% confidence interval (CI) - 9.70 to - 7.60%, P < 0.001] and underestimated the 5-year OS of each risk group. The differences between the predicted and observed 5-year OS rates were smallest among low-risk patients (< 135 points calculated using Pan's nomogram; which predicted minus observed OS, - 6.41%, 95% CI - 6.75 to - 6.07%, P < 0.001) and were largest among high-risk patients (≥ 160 points) (- 13.56%, 95% CI - 15.48 to - 11.63%, P < 0.001). The Hosmer-Lemeshow test suggested that the predicted and observed 5-year OS rates had no ideal relationship (P < 0.001). Pan's nomogram had better discriminatory ability compared with the levels of Epstein-Barr virus DNA acid (EBV DNA) and the 7th or 8th AJCC/UICC staging system, although not better compared with the combination of EBV DNA and the 8th staging system. Additionally, Pan's nomogram was marginally inferior to our predictive model, which included the 8th AJCC/UICC N-classification, age, gross primary tumor volume, lactate dehydrogenase, and body mass index.

CONCLUSIONS

Pan's nomogram underestimated the 5-year OS of patients with locoregionally advanced NPC at our cancer center, and may not be a precise tool for selecting participants for clinical trials.

Nomograms forecasting long-term overall and cancer-specific survival of patients with oral squamous cell carcinoma

Our aim was to establish a "nomogram" model to forecast the overall survival (OS) and cancer-specific survival (CSS) of oral squamous cell carcinoma (OSCC) patients. The clinicopathological data for the 10,533 OSCC patients were collected from the Surveillance, Epidemiology and End Results (SEER) database. We used a credible random split-sample method to divide 10,533 patients into two cohorts: 7046 patients in the modeling cohort and 3487 patients in the external validation cohort (split-ratio = 2:1). The median follow-up period was 32 months (1-119 months). We developed nomograms to predict 5- and 8-year OS and CSS of OSCC patients with a Cox proportional hazards model. The precision of the nomograms was assessed by the concordance index (C-index) and calibration curves through internal and external validation. The C-indexes of internal validation regarding 5- and 8-year OS and CSS were 0.762 and 0.783, respectively. In addition, the external validation's C-indexes were 0.772 and 0.800. Based on a large-sample analysis targeting the SEER database, we established two nomograms to predict long-term OS and CSS for OSCC patients successfully, which can assist surgeons in developing a more effective therapeutic regimen and conducting personalized prognostic evaluations.

Nomograms for predicting long-term overall survival and cancer-specific survival in patients with major salivary gland cancer: a population-based study

In this study, we aimed to develop and validate nomograms for predicting long-term overall survival (OS) and cancer-specific survival (CSS) in major salivary gland cancer (MSGC) patients. These nomograms were developed using a retrospective cohort (N=4218) from the Surveillance, Epidemiology, and End Results (SEER) database, and externally validated using an independent data cohort (N=244). We used univariate, and multivariate analyses, and cumulative incidence function to select the independent prognostic factors of OS and CSS. Index of concordance (c-index) and calibration plots were used to estimate the nomograms’ predictive accuracy. The median follow-up period was 34 months (1–119 months). Of 4218 MSGC patients, 1320 (31.3%) died by the end of the follow-up; of these 1320 patients, 883 (20.9%) died of MSGC. The OS nomogram, which had a c-index of 0.817, was based on nine variables: age, sex, tumor site, tumor grade, surgery performed, radiation therapy and TNM classifications. The CSS nomogram, which had a c-index of 0.829, was based on the same nine variables plus race. External validation c-indexes were 0.829 and 0.807 for OS and CSS, respectively. Based on SEER database, we have developed nomograms predicting five- and eight-years OS and CSS for MSGC patients with perfect accuracy. These nomograms will help clinicians customize treatment and monitoring strategies in MSGC patients.

Nomograms for predicting long-term survival in patients with non-metastatic nasopharyngeal carcinoma in an endemic area

Purpose

Nomogram for predicting more than a 5-year survival for non-metastatic nasopharyngeal carcinoma (NPC) was lacking. This study aimed to develop the new nomograms to predict long-term survival in these patients.

Results

The median follow-up time for training set and test set was 95.2 months and 133.3 months, respectively. The significant predictors for death were age, gender, body mass index (BMI), T stage, N stage, lactate dehydrogenase (LDH), and radiotherapy techniques. For predicting recurrence, age, gender, T stage, LDH, and radiotherapy techniques were significant predictors, whereas age, gender, BMI, T stage, N stage and LDH were significant predictors for distant metastasis. The calibration curves showed the good agreements between nomogram-predicted and actual survival. The c-indices for predicting death, recurrence, and distant metastases between nomograms and the TNM staging system were 0.767 VS.0.686 (P<0.001), 0.655 VS.0.585 (P<0.001), and 0.881 VS.0.754 (P<0.001), respectively. These results were further confirmed in the test set.

Methods

On the basis of a retrospective study of 1593 patients (training set) who received radiotherapy alone or concurrent chemoradiotherapy from 2000 to 2004, significant predictors were identified and incorporated to build the nomograms. The calibration curves of nomogram-predicted survival versus the actual survival were plotted and reviewed. Bootstrap validation was performed to calculate the concordance index (c-index). These models were further validated in an independent prospective trial (test set, n=400).

Conclusion

The established nomograms suggest more-accurate long-term prediction for patients with non-metastatic NPC.

Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Background and Purpose

To improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset.

Materials and Methods

Data extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre's (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort).

Results

Data mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so.

Conclusions

The technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort.

Pretreatment Nomograms for Local and Regional Recurrence after Radical Radiation Therapy for Primary Nasopharyngeal Carcinoma

Background: The aim of this study was to build nomograms to predict local recurrence (LR) and regional recurrence (RR) in patients with nasopharyngeal carcinoma (NPC) underwent intensity-modulated radiation therapy (IMRT).

Patients and Methods: A total of 1811 patients with non-metastatic NPC treated with IMRT (with or without chemotherapy) between October 2009 and February 2012 at our center were involved for building the nomograms. Nomograms for LR-free rate and RR-free rate at 3- and 5- year were generated as visualizations of Cox proportional hazards regression models, and validated using bootstrap resampling, estimating discrimination and calibration.

Results: With a median follow up of 49.50 months, the 3- and 5- year LR-free rate were 95.43% and 94.30% respectively; the 3- and 5- year RR-free rate were 95.94% and 95.41% respectively. The final predictive model for LR included age, the neutrophil/leukocyte ratio (NWR), pathological type, primary gross tumor volume, maxillary sinus invasion, ethmoidal sinus invasion and lacerated foramen invasion; the model for RR involved NWR, plasma Epstein-Barr virus (EBV) DNA copy number, cervical lymph node volume and N category. The models showed fairly good discriminatory ability with concordance indices (c-indices) of 0.76 and 0.74 for predicting LR and RR, respectively, as well as good calibration. The proposed stratification of risk groups based on the nomograms allowed significant distinction between Kaplan-Meier curves for LR and RR.

Conclusions: The proposed nomograms resulted in more-accurate prognostic prediction for LR and RR with a high concordance, hence to inform patients with high risk of recurrence on more aggressive therapy. The prognostic nomograms could better stratify patients into different risk groups.

Nomograms to estimate long-term overall survival and tongue cancer-specific survival of patients with tongue squamous cell carcinoma

The aim of this study was to construct nomograms to predict long-term overall survival (OS) and tongue cancer-specific survival (TCSS) of tongue squamous cell carcinoma (TSCC) patients based on clinical and tumor characteristics. Clinical, tumor, and treatment characteristics of 12,674 patients diagnosed with TSCC between 2004 and 2013 were collected from the Surveillance, Epidemiology, and End Results database. These patients were then divided into surgery and nonsurgery cohorts, and nomograms were developed for each of these groups. The step-down method and cumulative incidence function were used for model selection to determine the significant prognostic factors associated with OS and TCSS. These prognostic variables were incorporated into nomograms. An external cohort was used to validate the surgery nomograms. Seven variables were used to create the surgery nomograms for OS and TCSS, which had c-indexes of 0.709 and 0.728, respectively; for the external validation cohort, the c-indexes were 0.691 and 0.711, respectively. Nine variables were used to create the nonsurgery nomograms for OS and TCSS, which had c-indexes of 0.750 and 0.754, respectively. The calibration curves of the 5- and 8-year surgery and nonsurgery nomograms showed excellent agreement between the probabilities and observed values. By incorporating clinicopathological and host characteristics in patients, we are the first to establish nomograms that accurately predict prognosis for individual patients with TSCC. These nomograms ought to provide more personalized and reliable prognostic information, and improve clinical decision-making for TSCC patients.

Tumor regression patterns by follow-up duration in patients with nasopharyngeal carcinoma treated with concurrent chemoradiotherapy

The aim of this study was to describe the patterns of tumor regression with respect to follow-up duration after chemoradiotherapy in patients with nasopharyngeal carcinoma. A total of 27 patients with nasopharyngeal carcinoma were included and received definitive concurrent chemoradiotherapy. Patterns of primary tumor regression and development of locoregional recurrences were evaluated by imaging studies every 1 to 2 months. Primary tumors gradually regressed over the period of follow-up. The median time to full regression was 4.9 months (range, 1.5-19.4). In 61.5% of patients, the primary tumor continued to regress for >4 months after completion of chemoradiotherapy. Six patients experienced locoregional recurrence during follow-up, all of which occurred after full regression of the primary tumor. A patient group with delayed regression did not have poorer prognosis than a patient group with early regression. Older age, non-current-smoker status, advanced T stage, and higher daily radiation dose were significantly associated with delayed primary tumor regression. Nasopharyngeal carcinoma continued to regress for >4 months after chemoradiotherapy in a considerable number of patients. We recommend waiting for >4 months for full regression of nasopharyngeal carcinomas after chemoradiotherapy, if signs of persistent or recurrent disease are not evident on follow-up examination.

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

BACKGROUND

The objective of this study was to develop a nomogram for refining prognostication for patients with nondisseminated nasopharyngeal cancer (NPC) staged with the proposed 8th edition of the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) staging system.

METHODS

Consecutive patients who had been investigated with magnetic resonance imaging, staged with the proposed 8th edition of the AJCC/UICC staging system, and irradiated with intensity-modulated radiotherapy from June 2005 to December 2010 were analyzed. A cohort of 1197 patients treated at Fujian Provincial Cancer Hospital was used as the training set, and the results were validated with 412 patients from Pamela Youde Nethersole Eastern Hospital. Cox regression analyses were performed to identify significant prognostic factors for developing a nomogram to predict overall survival (OS). The discriminative ability was assessed with the concordance index (c-index). A recursive partitioning algorithm was applied to the survival scores of the combined set to categorize the patients into 3 risk groups.

RESULTS

A multivariate analysis showed that age, gross primary tumor volume, and lactate dehydrogenase were independent prognostic factors for OS in addition to the stage group. The OS nomogram based on all these factors had a statistically higher bias-corrected c-index than prognostication based on the stage group alone (0.712 vs 0.622, P <.01). These results were consistent for both the training cohort and the validation cohort. Patients with <135 points were categorized as low-risk, patients with 135 to <160 points were categorized as intermediate-risk, and patients with ≥160 points were categorized as high-risk. Their 5-year OS rates were 92%, 84%, and 58%, respectively.

CONCLUSIONS

The proposed nomogram could improve prognostication in comparison with the TNM stage group. This could aid in risk stratification for individual NPC patients. Cancer 2016;122:3307-3315. © 2016 American Cancer Society.

A nomogram for predicting survival of nasopharyngeal carcinoma patients with metachronous metastasis

Patients with metachronous metastatic nasopharyngeal carcinoma (NPC) differ significantly in survival outcomes. The aim of this study is to build a clinically practical nomogram incorporating known tumor prognostic factors to predict survival for metastatic NPC patients in epidemic areas.A total of 860 patients with metachronous metastatic nasopharyngeal carcinoma were analyzed retrospectively. Variables assessed were age, gender, body mass index, Karnofsky Performance Status (KPS), Union for International Cancer Control (UICC) T and N stages, World Health Organization (WHO) histology type, serum lactate dehydrogenase (sLDH) level, serum Epstein-Barr virus (EBV) level, treatment modality, specific metastatic location (lung/liver/bone), number of metastatic location(s) (isolated vs multiple), and number of metastatic lesion(s) in metastatic location(s) (single vs multiple). The independent prognostic factors for overall survival (OS) by Cox-regression model were utilized to build the nomogram.Independent prognostic factors for OS of metastatic NPC patients included age, UICC N stage, KPS, sLDH, number of metastatic locations, number of metastatic lesions, involvement of liver metastasis, and involvement of bone metastasis. Calibration of the final model suggested a c-index of 0.68 (95% confidence interval [CI], 0.65-0.69). Based on the total point (TP) by nomogram, we further subdivided the study cohort into 4 groups. Group 1 (TP < 320, 208 patients) had the lowest risk of dying. Discrimination was visualized by the differences in survival between these 4 groups (group 2/group 1: hazard ratio [HR] = 1.61, 95%CI: 1.24-2.09; group 3/group 1: HR = 2.20, 95%CI: 1.69-2.86; and group 4/group 1: HR = 3.66, 95%CI: 2.82-4.75).The developed nomogram can help guide the prognostication of patients with metachronous metastatic NPC in epidemic areas.

Development and External Validation of Nomograms for Predicting Survival in Nasopharyngeal Carcinoma Patients after Definitive Radiotherapy

The distant metastasis free survival (DMFS) and overall survival (OS) differ significantly among individuals even within the same clinical stages. The purpose of this retrospective study was to build nomograms incorporating plasma EBV DNA for predicting DMFS and OS of nasopharyngeal carcinoma (NPC) patients after definitive radiotherapy. A total of 1168 non-metastatic NPC patients from two institutions were included to develop the nomograms. Seven and six independent prognostic factors were identified to build the nomograms for OS and DMFS, respectively. The models were externally validated by a separate cohort of 756 NPC patients from the third institutions. For predicting OS, the c-index of the nomogram was significantly better than that of the TNM staging system (Training cohort, P = 0.005; validation cohort, P = 0.03). The c-index of nomogram for DMFS in the training and validation set were both higher than that of TNM classification with marginal significance (P = 0.048 and P = 0.057, respectively). The probability of 1-, 3-, and 5-year OS and DMFS showed optimal agreement between nomogram prediction and actual observation. The proposed stratification of risk groups based on the nomograms allowed significant distinction between Kaplan-Meier curves for survival outcomes. The prognostic nomograms could better stratify patients into different risk groups.