Frank R. Datema

Impact of comorbidity on short-term mortality and overall survival of head and neck cancer patients.

In 2001, we presented a Cox regression model that is able to predict survival of the newly diagnosed patient with head and neck squamous cell carcinoma (HNSCC). This model is based on the TNM classification and other important clinical variables such as age at diagnosis, sex, primary tumor site, and prior malignancies. We aim to improve this model by including comorbidity as an extra prognostic variable. Accurate prediction of the prognosis of the newly diagnosed patient with head and neck cancer can assist the physician in patient counseling, clinical decision‐making, and quality maintenance.

Impact of severe malnutrition on short-term mortality and overall survival in head and neck cancer

BACKGROUND Basic patient and tumor characteristics impact overall survival of head and neck squamous cell carcinoma patients. Severe malnutrition, defined as weight loss > 10% in 6 months preceding primary tumor diagnosis, impacts overall survival as well. Little attention has been paid to the interaction between severe malnutrition and other relevant prognostic covariables. This study investigates the impact of malnutrition on short-term mortality and overall survival, together with the covariables age, tumor site, gender, TNM-classification, comorbidity and prior tumors. METHODS 383 consecutive primary HNSCC patients, diagnosed and treated between 1995 and 1999 were followed until January 2010. Impact of covariables on short-term mortality and overall survival was studied univariately with Kaplan-Meier curves and the log-rank test. Cox-regression and binary logistic regression were used for multivariate analyses. RESULTS 28 (7.3%) patients were severely malnourished. All covariables, except gender and prior tumors had significant impact on overall survival. The relative risk of severe malnutrition was 1.8 and is comparable to the impact of a T2 tumor, a N1 neck or moderate comorbidity. A univariate relationship between severe malnutrition and short-term mortality was established. CONCLUSIONS Severe malnutrition has an independent impact on overall survival of primary HNSCC patients. There is a clear distinction between patients with and without severe malnutrition from moment of diagnosis until 10 years after. This emphasizes the importance of identification and optimal treatment of malnutrition before, during and after cancer treatment.

INCIDENCE AND PREDICTION OF MAJOR CARDIOVASCULAR COMPLICATIONS IN HEAD AND NECK SURGERY

Patients with head and neck squamous cell carcinoma (HNSCC) usually have a history of tobacco and alcohol abuse. These 2 intoxications not only are main oncologic risk factors but also show a strong causal relationship with certain comorbid conditions. Examples are coronary artery disease, stroke, renal dysfunction, and heart failure, which are all proven major risk factors for an adverse postoperative outcome after stressful noncardiac surgery. Preoperative identification of these conditions could lead to preventive measures in patients with HNSCC that undergo extensive surgery. Preventing morbidity and mortality is of medical and economical importance.

NOVEL HEAD AND NECK CANCER SURVIVAL ANALYSIS APPROACH: RANDOM SURVIVAL FORESTS VERSUS COX PROPORTIONAL HAZARDS REGRESSION

Electronic patient files generate an enormous amount of medical data. These data can be used for research, such as prognostic modeling. Automatization of statistical prognostication processes allows automatic updating of models when new data is gathered. The increase of power behind an automated prognostic model makes its predictive capability more reliable. Cox proportional hazard regression is most frequently used in prognostication. Automatization of a Cox model is possible, but we expect the updating process to be time‐consuming. A possible solution lies in an alternative modeling technique called random survival forests (RSFs). RSF is easily automated and is known to handle the proportionality assumption coherently and automatically. Performance of RSF has not yet been tested on a large head and neck oncological dataset. This study investigates performance of head and neck overall survival of RSF models. Performances are compared to a Cox model as the “gold standard.” RSF might be an interesting alternative modeling approach for automatization when performances are similar.

Update and external validation of a head and neck cancer prognostic model

The purpose of this study was to update and external validation of a prognostic model that is able to predict the survival probability of newly diagnosed patients with head and neck cancer.

Cytology and histology have limited added value in prognostic models for salivary gland carcinomas

Univariate analyses on malignant salivary gland tumors report a strong relation of histological subtypes and prognosis. However, multivariate analyses with sufficient patients and reflecting the broad spectrum of putative prognostic factors are rare. In order to study the prognostic value of cytology and histology in salivary carcinoma we performed multivariate analyses on 666 newly diagnosed patients. In multivariate analyses sex, tumor size, N- and M-staging, localization, comorbidity, skin involvement and pain were independent predictors of survival. Histology was an independent prognostic factor, mainly because acinic cell carcinoma acted differently from the other histological subtypes. However, a simple prognostic model without cytology and/or histology has similar predictive power compared to more elaborate models. The added prognostic value of cytology and/or histology factors in salivary carcinoma is limited, largely due to the combined prognostic value of other prognostic factors such as tumor size, N- and M-classification and comorbidity.

Patient satisfaction in Caucasian and Mediterranean open rhinoplasty using the tongue-in-groove technique: prospective statistical analysis of change in subjective body image in relation to nasal appearance following aesthetic rhinoplasty.

Tongue‐in‐groove (TIG) is a conservative but powerful surgical suture technique to control shape, rotation, and projection of the nasal tip. In this study, statistical analyses were performed to determine the aesthetical and functional effectiveness of TIG rhinoplasty.

Prediction of survival with alternative modeling techniques using pseudo values.

Background The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo values enable statistically appropriate analyses of survival outcomes when used in seven alternative modeling techniques. Methods In this case study, we analyzed survival of 1282 Dutch patients with newly diagnosed Head and Neck Squamous Cell Carcinoma (HNSCC) with conventional Kaplan-Meier and Cox regression analysis. We subsequently calculated pseudo values to reflect the individual survival patterns. We used these pseudo values to compare recursive partitioning (RPART), neural nets (NNET), logistic regression (LR) general linear models (GLM) and three variants of support vector machines (SVM) with respect to dichotomous 60-month survival, and continuous pseudo values at 60 months or estimated survival time. We used the area under the ROC curve (AUC) and the root of the mean squared error (RMSE) to compare the performance of these models using bootstrap validation. Results Of a total of 1282 patients, 986 patients died during a median follow-up of 66 months (60-month survival: 52% [95% CI: 50%−55%]). The LR model had the highest optimism corrected AUC (0.791) to predict 60-month survival, followed by the SVM model with a linear kernel (AUC 0.787). The GLM model had the smallest optimism corrected RMSE when continuous pseudo values were considered for 60-month survival or the estimated survival time followed by SVM models with a linear kernel. The estimated importance of predictors varied substantially by the specific aspect of survival studied and modeling technique used. Conclusions The use of pseudo values makes it readily possible to apply alternative modeling techniques to survival problems, to compare their performance and to search further for promising alternative modeling techniques to analyze survival time.

The Tongue-in-Groove Technique in Primary and Revision Rhinoplasty

The tongue-in-groove (TIG) is a conservative but powerful surgical suture technique to control tip rotation and projection, to set an appropriate alar-columellar relationship, to straighten a caudal septal deviation, and to stabilize the nasal base. TIG is suitable in primary and most revision cases and is easily combined with other surgical techniques. Seventeen years after its popularization, the TIG technique is embedded in modern-day rhinoplasty literature and teaching material. This article describes indications and considerations during external primary and revision rhinoplasty, based on clinical experience and the available literature.

Bilateral Auricular Pseudocyst: Recognizing and Treating

An auricular pseudocyst is a benign cystic lesion of the auricular cartilage. When not recognized, it is a clinical presentation that can easily be misdiagnosed and subsequently be mistreated leading to unsatisfactory esthetical results or disease recurrence. A patient was presented with bilateral pseudocysts, which were surgically excised. The aim of the treatment of a pseudocyst is to have recurrence-free resolution and to restore the original auricular architecture while removing the cystic lesion. Three alternatives to surgery are described in the literature and all seem not to be sufficient. When the pseudocyst is treated at an early stage, surgical excision shows high success rates and preservation of the auricular architecture. According to the success rate described in the literature combined with the preservation of the auricular architecture, we recommend surgical excision for the management of auricular pseudocysts.