Background and Purpose: In 2007, the WHO classification of brain tumors was extended by three new entities of glioneuronal tumors: papillary glioneuronal tumor (PGNT), rosette-forming glioneuronal tumor of the fourth ventricle (RGNT) and glioneuronal tumor with neuropil-like islands (GNTNI). Focusing on clinical characteristics and outcome, the authors performed a comprehensive individual patient data (IPD) meta-analysis of the cases reported in literature until December 2012. Methods: PubMed, Embase and Web of Science were searched for peer-reviewed articles reporting on PGNT, RGNT, and GNTNI using predefined keywords. Results: 95 publications reported on 182 patients (PGNT, 71; GNTNI, 26; RGNT, 85). Median age at diagnosis was 23 years (range 4–75) for PGNT, 27 years (range 6–79) for RGNT, and 40 years (range 2–65) for GNTNI. Ninety-seven percent of PGNT and 69% of GNTNI were located in the supratentorial region, 23% of GNTNI were in the spinal cord, and 80% of RGNT were localized in the posterior fossa. Complete resection was reported in 52 PGNT (73%), 36 RGNT (42%), and 7 GNTNI (27%) patients. Eight PGNT, 3 RGNT, and 12 GNTNI patients were treated with chemo- and/or radiotherapy as the primary postoperative treatment. Follow-up data were available for 132 cases. After a median follow-up time of 1.5 years (range 0.2–25) across all patients, 1.5-year progression-free survival rates were 52±12% for GNTNI, 86±5% for PGNT, and 100% for RGNT. The 1.5-year overall-survival were 95±5%, 98±2%, and 100%, respectively. Conclusions: The clinical understanding of the three new entities of glioneuronal tumors, PGNT, RGNT and GNTNI, is currently emerging. The present meta-analysis will hopefully contribute to a delineation of their diagnostic, therapeutic, and prognostic profiles. However, the available data do not provide a solid basis to define the optimum treatment approach. Hence, a central register should be established.

Background and Purpose: In 2007, the WHO classification of brain tumors was extended by three new entities of glioneuronal tumors: papillary glioneuronal tumor (PGNT), rosette-forming glioneuronal tumor of the fourth ventricle (RGNT) and glioneuronal tumor with neuropil-like islands (GNTNI). Focusing on clinical characteristics and outcome, the authors performed a comprehensive individual patient data (IPD) meta-analysis of the cases reported in literature until December 2012. Methods: PubMed, Embase and Web of Science were searched for peer-reviewed articles reporting on PGNT, RGNT, and GNTNI using predefined keywords. Results: 95 publications reported on 182 patients (PGNT, 71; GNTNI, 26; RGNT, 85). Median age at diagnosis was 23 years (range 4–75) for PGNT, 27 years (range 6–79) for RGNT, and 40 years (range 2–65) for GNTNI. Ninety-seven percent of PGNT and 69% of GNTNI were located in the supratentorial region, 23% of GNTNI were in the spinal cord, and 80% of RGNT were localized in the posterior fossa. Complete resection was reported in 52 PGNT (73%), 36 RGNT (42%), and 7 GNTNI (27%) patients. Eight PGNT, 3 RGNT, and 12 GNTNI patients were treated with chemo- and/or radiotherapy as the primary postoperative treatment. Follow-up data were available for 132 cases. After a median follow-up time of 1.5 years (range 0.2–25) across all patients, 1.5-year progression-free survival rates were 52±12% for GNTNI, 86±5% for PGNT, and 100% for RGNT. The 1.5-year overall-survival were 95±5%, 98±2%, and 100%, respectively. Conclusions: The clinical understanding of the three new entities of glioneuronal tumors, PGNT, RGNT and GNTNI, is currently emerging. The present meta-analysis will hopefully contribute to a delineation of their diagnostic, therapeutic, and prognostic profiles. However, the available data do not provide a solid basis to define the optimum treatment approach. Hence, a central register should be established.

Krebs ist die zweithäufigste Todesursache weltweit und zählt damit zu den größten Plagen der Menschheit. Jährlich sterben Menschen an den Folgen bösartiger Tumore und stellen Ärzte vor scheinbar unlösbare Aufgaben. Um Krebsgeschwüre effizient bekämpfen oder sogar vollständig beseitigen zu können, ist es enorm wichtig diese früh genug zu diagnostizieren. Oft stellt jedoch genau das in der Praxis ein großes Problem dar und Tumore werden erst dann als solche erkannt, wenn das Zellwachstum schon sehr weit fortgeschritten ist. Eine große Chance für die frühzeitige Erkennung von Krebs bieten unterdessen Deep Learning Algorithmen. Die vorliegende Seminararbeit stellt diese Verfahren und ihre Anwendung in der Krebsdiagnostik vor. Es wird hierbei genauer auf Convolutional Neural Networks eingegangen, die besonders gut geeignet für die Analyse von Gewebebildern sind und unter anderem auch im System von Google's DeepMind zum Einsatz kommen. Die Arbeit analysiert Chancen und Risiken des Einsatzes von Deep Neural Networks bei der Diagnose von bösartigen Tumoren und verschafft dem Leser damit einen ganzheitlichen Überblick über die Anwendung von Deep Neural Networks im Bereich der Onkologie.:1 Einleitung 2 Vom Neuronalen Netz zum Deep Learning Algorithmus 2.1 Grundlagen Künstlicher Neuronaler Netze 2.1.1 Allgemeiner Aufbau 2.1.2 Das Neuron als Grundbaustein 2.1.3 Lernen in neuronalen Netzen 2.1.4 Loss Function und Optimizer 2.2 Convolutional Neural Networks 2.2.1 Convolutional Layer 2.2.2 Pooling Layer 2.2.3 Fully Connected Layer 2.2.4 Lernen und Aktivierung in CNN’s 3 DeepMind als Deep Learning Multitalent 3.1 Bisherige Erfolge 3.2 DeepMind Health 4 Chancen und Risiken in der Krebsdiagnostik 4.1 Aktueller Stand der Brustkrebsdiagnostik 4.2 Chancen von Deep Learning Algorithmen 4.3 Ethische Risiken 4.3.1 False Positives 4.3.2 False Negatives 4.4 Fazit der Risikoanalyse 5 Ausblick