A combined prognostic factor for improved risk stratification of patients with oral cancer

Factors influencing lymph node yield in head and neck squamous cell carcinoma: A scoping review

OBJECTIVES

In head and neck cancer surgery, several studies have demonstrated the prognostic significance of lymph node yield (LNY). To our knowledge, no review has evaluated both the contributing factors to LNY and its impact on survival outcomes across all head and neck squamous cell carcinoma (HNSCC) subsites.

MATERIALS AND METHODS

A scoping review of LNY in HNSCC was conducted according to The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework to answer the following research questions: 1) Which surgeon, pathologist, and patient characteristics influence LNY? 2) Which anatomic subsites does LNY impact survival?

RESULTS

Surgeon experience and variation in pathology protocols and personnel can impact LNY. Extent of nodal dissection, advanced tumor characteristics, and treatment at an academic facility are associated with increased LNY. Patient characteristics such as age <40, male gender, and BMI > 30 are associated with increased LNY. In the oral cavity, LNY > 18 is an independent predictor of improved overall survival (OS), disease free survival (DFS), and disease-specific survival (DSS). In the oropharynx, published studies show mixed results with regards to the impact of LNY on OS, DFS, and DSS. LNY has not been associated with OS or DFS in the larynx, irrespective of nodal threshold.

CONCLUSIONS

Provider and patient characteristics may impact LNY. LNY ≥ 18 is associated with a survival benefit in the oral cavity and HNSCC overall. Further investigation of LNY particularly in prospective clinical trials is required prior to its adoption as a quality metric in HNSCC.

Analysis of prognostic factors through survival rate analysis of oral squamous cell carcinoma patients treated at the National Cancer Center: 20 years of experience

OBJECTIVES

This study aimed to analyze the clinicopathological prognostic factors affecting the survival of patients with oral squamous cell carcinoma (OSCC).

MATERIALS AND METHODS

A retrospective study was conducted on patients with OSCC who received treatment at the Oral Oncology Clinic of the National Cancer Center (NCC) from June 2001 to December 2020. The patients' sex, age, primary site, T stage, node metastasis, TNM staging, perineural invasion (PNI), lymphovascular invasion (LVI), differentiation, surgical resection margin, smoking, and drinking habits were investigated to analyze risk factors. For the univariate analysis, a Kaplan-Meier survival analysis and log-rank test were used. Additionally, for the multivariable analysis, a Cox proportional hazard model analysis was used. For both analyses, statistical significance was considered when P<0.05.

RESULTS

During the investigation period, 407 patients were received surgical treatment at the NCC. Their overall survival rate (OS) for five years was 70.7%, and the disease-free survival rate (DFS) was 60.6%. The multivariable analysis revealed that node metastasis, PNI, and differentiation were significantly associated with poor OS. For DFS, PNI and differentiation were associated with poor survival rates.

CONCLUSION

In patients with OSCC, cervical node metastasis, PNI, and differentiation should be considered important prognostic factors for postoperative survival.

Lymph Node Ratio as a Prognostic Factor in Neck Dissection in Oral Cancer Patients: A Systematic Review and Meta-Analysis

Simple Summary

Lymph node ratio (LNR) is a well-studied prognostic factor in colorectal and breast cancer, and it has been recently evaluated as a clinically relevant biomarker in oral squamous cell carcinoma. LNR represents the ratio of positive lymph nodes extracted in a neck dissection to the total number of nodes harvested (lymph node yield, LNY). Many single-center cohort studies and a few multicenter have assessed the significance of LNR as a prognostic factor in oral cancer. In this systematic review and meta-analysis of 32 studies and 20,994 oral cancer patients, we demonstrate that LNR is an independent prognostic indicator in patients with oral squamous cell carcinoma.

Abstract

Many studies have evaluated the clinical implications of lymph node ratio (LNR) as a prognostic factor in patients with oral squamous cell carcinoma (OSCC). The main purpose of this systematic review and meta-analysis was to address LNR as a prognosticator in patients with OSCC. A systematic search was conducted in the following databases: PubMed, EMBASE, Google Scholar, OpenGrey, Cochrane library, and ClinicalTrials.gov, and studies between 2009 and 2020 were sought. The pooled relative risk was calculated along with 95% confidence intervals for the following endpoints: overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), distant metastasis-free survival (DMFS), locoregional disease-free survival (LRDFS), local recurrence-free survival (LRFS), and recurrence-free survival (RFS) according to the random-effects model (Der Simonian–Laird approach). Subgroup and meta-regression analyses were performed as well. Finally, 32 cohort studies were eligible, which included 20,994 patients with OSCC. Patients were subdivided into two categories, group YES (studies that included in their analysis only patients with positive lymph nodes) and group NO (studies that did not exclude LNR = 0 patients). In the group YES, patients with high LNR had shorter OS (RR = 1.68, 95% CI: 1.47–1.91), DFS (RR = 1.68, 95% CI: 1.42–1.99), DSS (RR = 1.94, 95% CI: 1.56–2.42), DMFS (RR = 1.83, 95% CI: 1.13–2.96), LRDFS (RR = 1.55, 95% CI: 1.10–2.20), and LRFS (RR = 1.73, 95% CI: 1.41–2.13) compared to patients with low LNR. In the group NO, patients with high LNR in comparison had shorter OS (RR = 2.38, 95% CI: 1.99–2.85), DFS (RR = 2.04, 95% CI: 1.48–2.81), and DSS (RR = 2.90, 95% CI: 2.35–3.57) compared to patients with low LNR. Based on those findings, LNR might be an independent prognostic factor for OS in patients with OSCC and could be incorporated into future classification systems for better risk stratification.

Lymph node ratio-dependent prognosis stratification and postoperative radiotherapy utilization in T1-2N1 oral cavity carcinoma

OBJECTIVE

Pathological T1-2N1 oral cavity squamous cell carcinoma (pT1-2N1 OCSCC) is a setting with intermediate prognosis whilst without consensus regarding the utilization of postoperative radiotherapy (PORT). This study aimed to investigate the prognostic value of lymph node ratio (LNR) and to further examine its clinical validity for guiding PORT in pT1-2N1 OCSCC.

METHODS

OCSCC patients who received surgery between 2010 and 2015 with at least 6 lymph nodes dissection were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Time-dependent receiver operating characteristic (ROC) analysis was used to identify the optimal cutoff of LNR. Multivariable Cox regression analysis was employed to assess the prognostic value of LNR. Impact of PORT was evaluated in respective subgroups stratified by LNR.

RESULTS

A total of 870 OCSCC patients with pT1-2N1 diseases were eligible for analysis. The 5-year overall survival (OS) and disease-specific survival (DSS) was 57.2% and 67.9% respectively. Time-dependent ROC analyses for OS and DSS concordantly revealed 5.5% as the optimal cutoff of LNR. Significantly higher risks of death (HR = 1.610, 95% CI: 1.139-2.276) and disease-specific death (HR = 1.731, 95% CI: 1.101-2.723) were unveiled in patients with LNR > 5.5%. PORT related improvement on OS (5-year rate: 57.6% vs. 47.3%, p = 0.095) and DSS (5-year rate: 71.0% vs. 53.8%, p = 0.030) was only found in LNR > 5.5% subgroup.

CONCLUSIONS

LNR > 5.5% is indicative of inferior outcome in pT1-2N1 OCSCC, warranting the utilization of PORT in this sub-setting.

Derivation and Validation of a Prognostic Scoring Model Based on Clinical and Pathological Features for Risk Stratification in Oral Squamous Cell Carcinoma Patients: A Retrospective Multicenter Study

Objective

To develop and validate a simple-to-use prognostic scoring model based on clinical and pathological features which can predict overall survival (OS) of patients with oral squamous cell carcinoma (OSCC) and facilitate personalized treatment planning.

Materials and Methods

OSCC patients (n = 404) from a public hospital were divided into a training cohort (n = 282) and an internal validation cohort (n = 122). A total of 12 clinical and pathological features were included in Kaplan-Meier analysis to identify the factors associated with OS. Multivariable Cox proportional hazards regression analysis was performed to further identify important variables and establish prognostic models. Nomogram was generated to predict the individual's 1-, 3- and 5-year OS rates. The performance of the prognostic scoring model was compared with that of the pathological one and the AJCC TNM staging system by the receiver operating characteristic curve (ROC), concordance index (C-index), calibration curve, and decision curve analysis (DCA). Patients were classified into high- and low-risk groups according to the risk scores of the nomogram. The nomogram-illustrated model was independently tested in an external validation cohort of 95 patients.

Results

Four significant variables (physical examination-tumor size, imaging examination-tumor size, pathological nodal involvement stage, and histologic grade) were included into the nomogram-illustrated model (clinical-pathological model). The area under the ROC curve (AUC) of the clinical-pathological model was 0.687, 0.719, and 0.722 for 1-, 3- and 5-year survival, respectively, which was superior to that of the pathological model (AUC = 0.649, 0.707, 0.717, respectively) and AJCC TNM staging system (AUC = 0.628, 0.668, 0.677, respectively). The clinical-pathological model exhibited improved discriminative power compared with pathological model and AJCC TNM staging system (C-index = 0.755, 0.702, 0.642, respectively) in the external validation cohort. The calibration curves and DCA also displayed excellent predictive performances.

Conclusion

This clinical and pathological feature based prognostic scoring model showed better predictive ability compared with the pathological one, which would be a useful tool of personalized accurate risk stratification and precision therapy planning for OSCC patients.

Prognostic capacity of the lymph node ratio in squamous cell carcinomas of the head and neck

INTRODUCTION AND OBJECTIVES

Lymph node density or lymph node ratio (LNR), defined as the quotient between the total number of positive lymph nodes and the total number of dissected lymph nodes, has demonstrated a prognostic capacity in several tumour models, including patients with head and neck squamous cell carcinomas (HNSCC). The aim of the present study is to analyse the prognostic value of LNR in a wide cohort of patients with HNSCC.

METHODS

We carried out a retrospective study of a cohort of 1,311 patients with HNSCC treated with unilateral or bilateral neck dissections. Of the patients included in the study, 55.0% had lymph node metastases (pN+). We proceeded to calculate the value of the LNR, and its categorization through a recursive partition analysis considering specific survival as the dependent variable.

RESULTS

Three categories were defined according to the value of the LNR with a cut-off point at the values < of .025 and .118. The 5-year specific survival for patients with an LNR less than .025 (n=654, 49.8%) was 87.2%, for patients with a LNR .025-.118 (n=394, 30.1%) it was 51.6%, and for patients with a LNR greater than .188 (n=263, 20.1%) it was 27.3% (P=.0001). According to the results of a multivariate analysis, the LNR significantly related to specific survival.

CONCLUSION

The LNR can be a prognostic variable to be considered in the pathological staging of the lymph nodes.

Deep learning-based survival prediction of oral cancer patients

The Cox proportional hazards model commonly used to evaluate prognostic variables in survival of cancer patients may be too simplistic to properly predict a cancer patient’s outcome since it assumes that the outcome is a linear combination of covariates. In this retrospective study including 255 patients suitable for analysis who underwent surgical treatment in our department from 2000 to 2017, we applied a deep learning-based survival prediction method in oral squamous cell carcinoma (SCC) patients and validated its performance. Survival prediction using DeepSurv, a deep learning based-survival prediction algorithm, was compared with random survival forest (RSF) and the Cox proportional hazard model (CPH). DeepSurv showed the best performance among the three models, the c-index of the training and testing sets reaching 0.810 and 0.781, respectively, followed by RSF (0.770/0.764), and CPH (0.756/0.694). The performance of DeepSurv steadily improved with added features. Thus, deep learning-based survival prediction may improve prediction accuracy and guide clinicians both in choosing treatment options for better survival and in avoiding unnecessary treatments.

Relationship between body mass index and outcomes for patients with oral squamous cell carcinoma

OBJECTIVE

The purpose of our study was to investigate body mass index (BMI) as a prognostic factor and to examine the relationship between pretreatment BMI and oral squamous cell carcinoma (OSCC) outcomes in northern Chinese patients.

METHODS

This retrospective study enrolled 633 patients with OSCC who underwent surgery between 2004 and 2014.

RESULTS

Most baseline characteristics (gender, sites, smoking history, comorbidity, hypertension, T stage, clinical features, perineural invasion, flap reconstruction) were differentiated by BMI groups. Overall, the Kaplan-Meier curves indicated no significant relationship between BMI and disease-free survival (DFS) or disease-specific survival (DSS). Interestingly, obese patients exhibited higher risks of recurrence and death than normal-weight patients (DFS: HR: 1.86, 95% CI: 1.16-2.96; DSS: HR: 2.04, 95% CI: 1.08-3.86). However, postoperative complications occurred more frequently in underweight patients than in normal-weight patients.

CONCLUSIONS

Obesity may contribute to a higher recurrence rate and a worse prognosis in OSCC patients than in normal-weight patients in northern China. However, underweight patients have a higher risk of postoperative complications.

Identifying a combined biomarker for bisphosphonate-related osteonecrosis of the jaw

BACKGROUND

For this study, the aim was to identify combined biomarkers associated with bisphosphonate-related osteonecrosis of the jaw (BRONJ).

MATERIALS AND METHODS

Microarray data for GSE7116 were downloaded from the Gene Expression Omnibus database, which contains 26 samples, including without ONJ, and 5 healthy volunteers. The combined biomarkers were identified using principal component analysis, and the pathway enrichment analyses were performed using the DAVID online tool.

RESULTS

Two hundred differently expressed genes between groups were detected according to the significances. From functional annotation, Y-box binding protein 1 and heterogeneous nuclear ribonucleoprotein C were found to be included in the most significant 10 pathways. Ten combined gene sets were identified that were effective in classifying multiple myeloma (MM) with ONJ and MM without ONJ.

CONCLUSION

Identifying combined gene expression profiles is expected to contribute to more personalized management of BRONJ and to improve existing therapies, and it will be helpful in finding new therapies by identifying more predictive biomarkers.