@article{MuellerKoehlerHendricksetal.2021,
  author    = {M{\"u}ller, Sophie and K{\"o}hler, Franziska and Hendricks, Anne and Kastner, Carolin and B{\"o}rner, Kevin and Diers, Johannes and Lock, Johan F. and Petritsch, Bernhard and Germer, Christoph-Thomas and Wiegering, Armin},
  title     = {Brain metastases from colorectal cancer: a systematic review of the literature and meta-analysis to establish a guideline for daily treatment},
  series = {Cancers},
  volume    = {13},
  journal   = {Cancers},
  number    = {4},
  issn      = {2072-6694},
  doi       = {10.3390/cancers13040900},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228883},
  year      = {2021},
  abstract  = {Colorectal cancer (CRC) is the third most common malignancy worldwide. Most patients with metastatic CRC develop liver or lung metastases, while a minority suffer from brain metastases. There is little information available regarding the presentation, treatment, and overall survival of brain metastases (BM) from CRC. This systematic review and meta-analysis includes data collected from three major databases (PubMed, Cochrane, and Embase) based on the key words "brain", "metastas*", "tumor", "colorectal", "cancer", and "malignancy". In total, 1318 articles were identified in the search and 86 studies matched the inclusion criteria. The incidence of BM varied between 0.1\% and 11.5\%. Most patients developed metastases at other sites prior to developing BM. Lung metastases and KRAS mutations were described as risk factors for additional BM. Patients with BM suffered from various symptoms, but up to 96.8\% of BM patients were asymptomatic at the time of BM diagnosis. Median survival time ranged from 2 to 9.6 months, and overall survival (OS) increased up to 41.1 months in patients on a multimodal therapy regimen. Several factors including age, blood levels of carcinoembryonic antigen (CEA), multiple metastases sites, number of brain lesions, and presence of the KRAS mutation were predictors of OS. For BM diagnosis, MRI was considered to be state of the art. Treatment consisted of a combination of surgery, radiation, or systemic treatment.},
  language  = {en}
}
@article{VeyKapsnerFuchsetal.2019,
  author    = {Vey, Johannes and Kapsner, Lorenz A. and Fuchs, Maximilian and Unberath, Philipp and Veronesi, Giulia and Kunz, Meik},
  title     = {A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning},
  series = {Cancers},
  volume    = {11},
  journal   = {Cancers},
  number    = {10},
  issn      = {2072-6694},
  doi       = {10.3390/cancers11101606},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193240},
  year      = {2019},
  abstract  = {The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.},
  language  = {en}
}