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

Abstract

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.

Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning

“…The first step was to identify and prepare the relevant data. Two datasets, LMU’s local tumor documentation dataset (CREDOS—cancer retrieval evaluation and documentation system) [ 9 , 10 ], as well as a custom MTB database, served as source data.…”

Early mortality in German patients with lung cancer: risk factors associated with 30-and 60-day mortality

“…The dataset consists of structured, manually collected data for all tumor entities, detailing the whole local (in terms of the LMU) disease history centered around a tumor, split into the main categories first-assessment, administered therapies, as well as follow-up (up to 2000 different data fields). The following information was extracted from the registry by utilizing the local analytics framework MOCCA [ 9 ]: date of diagnosis, date of birth, date of death or last follow-up, sex, tumor stage, metastases, and histology, as well as therapies and therapy intent of oncological patients. Inclusion criteria were a newly documented diagnosis of a thoracic malignancy, defined as C34* ICD-10-GM, between 2015 and 2019.…”