Quality of life, HPV-status and phase angle predict survival in head and neck cancer patients under (chemo)radiotherapy undergoing nutritional intervention: Results from the prospective randomized HEADNUT-trial.

PURPOSE: To analyze the impact of quality of life (QoL), nutritional and clinical indicators on overall survival in patients with head and neck squamous cell cancer (HNSCC) undergoing (chemo)radiotherapy. MATERIALS AND METHODS: At the beginning, at the end of (chemo)radiotherapy and during follow-up, QoL was prospectively assessed using the EORTC-QLQ-C30 and -QLQ-H&N35 questionnaires. Data were analyzed in 58 out of 220 screened patients, who were randomized into a control and intervention group. All patients received a nutritional assessment including bioelectrical impedance analysis (BIA), laboratory testing, and a screening for malnutrition based on the questionnaires MUST, NRS-2002 and Nutriscore at baseline and at the end of therapy. The intervention consisted of an individualized nutritional counseling every 2 weeks. RESULTS: Except for emotional functioning, dyspnea, financial difficulties, dental problems and weight gain, all other scales from the EORTC-QLQ-C30 and -H&N35 deteriorated during (chemo)radiotherapy. At first follow-up, patients of the control group experienced more nausea and vomiting compared to those of the intervention group (p = 0.02). After performing a multivariable model, dental problems at the end of therapy (HR: 1.03; 95% CI: 1-1.06; p = 0.03), HPV negativity (HR: 18.19, 95% CI: 1.61-204.17; p = 0.02), and baseline phase angle (HR: 0.09; 95% CI: 0.01-0.82; p = 0.03) were identified as predictors for overall survival. CONCLUSIONS: Factors influencing overall survival in patients with HNSCC undergoing (chemo)radiotherapy are complex and multifactorial. We were able to identify QoL-related (dental problems), clinical (HPV status) and nutritional (phase angle) factors as negative predictors for survival. This study was registered within the German Clinical Trials Register (DRKS00016862).

Optimizing the Analytical Value of Oncology-Related Data Based on an In-Memory Analysis Layer: Development and Assessment of the Munich Online Comprehensive Cancer Analysis Platform.

BACKGROUND: Many comprehensive cancer centers incorporate tumor documentation software supplying structured information from the associated centers' oncology patients for internal and external audit purposes. However, much of the documentation data included in these systems often remain unused and unknown by most of the clinicians at the sites. OBJECTIVE: To improve access to such data for analytical purposes, a prerollout of an analysis layer based on the business intelligence software QlikView was implemented. This software allows for the real-time analysis and inspection of oncology-related data. The system is meant to increase access to the data while simultaneously providing tools for user-friendly real-time analytics. METHODS: The system combines in-memory capabilities (based on QlikView software) with innovative techniques that compress the complexity of the data, consequently improving its readability as well as its accessibility for designated end users. Aside from the technical and conceptual components, the software's implementation necessitated a complex system of permission and governance. RESULTS: A continuously running system including daily updates with a user-friendly Web interface and real-time usage was established. This paper introduces its main components and major design ideas. A commented video summarizing and presenting the work can be found within the Multimedia Appendix. CONCLUSIONS: The system has been well-received by a focus group of physicians within an initial prerollout. Aside from improving data transparency, the system's main benefits are its quality and process control capabilities, knowledge discovery, and hypothesis generation. Limitations such as run time, governance, or misinterpretation of data are considered.

Dynamic Survival Analysis Using In-Memory Technology.

In the field of oncology, survival analysis is one of the most important tools in terms of measuring therapy success, evaluating risk or prognostic factors. Within this work, a variety of common survival analyses were embedded into a real-time analysis platform based on a comprehensive oncological dataset. The analysis platform utilizes an in-memory database, therefore allowing spontaneous adjustments of the survival curves to selections and stratifications. In contrast to classical statistics, where individual scripts have to be formulated for every possible request, the running system allows the deduction of instantaneous results.