Leha, Andreas

[["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2020-03-27T13:37:27Z"],["dc.date.available","2020-03-27T13:37:27Z"],["dc.date.issued","2011"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63393"],["dc.title","The Emacs Org-mode: Reproducible Research and Beyond"],["dc.type","report"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","9"],["dc.bibliographiccitation.lastpage","100"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Jung, Klaus"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2020-04-02T10:43:45Z"],["dc.date.available","2020-04-02T10:43:45Z"],["dc.date.issued","2013"],["dc.identifier.doi","10.4230/OASIcs.GCB.2013.90"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63516"],["dc.relation.eisbn","978-3-939897-59-0"],["dc.relation.eventlocation","Dagstuhl"],["dc.relation.eventstart","2013"],["dc.relation.ispartof","German Conference on Bioinformatics 2013"],["dc.title","Utilization of ordinal response structures in classification with high-dimensional expression data"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","127"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Jung, Klaus"],["dc.date.accessioned","2018-11-07T08:56:54Z"],["dc.date.available","2018-11-07T08:56:54Z"],["dc.date.issued","2011"],["dc.description.abstract","Background: Discovery of biomarkers that are correlated with therapy response and thus with survival is an important goal of medical research on severe diseases, e. g. cancer. Frequently, microarray studies are performed to identify genes of which the expression levels in pretherapeutic tissue samples are correlated to survival times of patients. Typically, such a study can take several years until the full planned sample size is available. Therefore, interim analyses are desirable, offering the possibility of stopping the study earlier, or of performing additional laboratory experiments to validate the role of the detected genes. While many methods correcting the multiple testing bias introduced by interim analyses have been proposed for studies of one single feature, there are still open questions about interim analyses of multiple features, particularly of high-dimensional microarray data, where the number of features clearly exceeds the number of samples. Therefore, we examine false discovery rates and power rates in microarray experiments performed during interim analyses of survival studies. In addition, the early stopping based on interim results of such studies is evaluated. As stop criterion we employ the achieved average power rate, i.e. the proportion of detected true positives, for which a new estimator is derived and compared to existing estimators. Results: In a simulation study, pre-specified levels of the false discovery rate are maintained in each interim analysis, where reduced levels as used in classical group sequential designs of one single feature are not necessary. Average power rates increase with each interim analysis, and many studies can be stopped prior to their planned end when a certain pre-specified power rate is achieved. The new estimator for the power rate slightly deviates from the true power rate but is comparable to other estimators. Conclusions: Interim analyses of microarray experiments can provide evidence for early stopping of long-term survival studies. The developed simulation framework, which we also offer as a new R package 'SurvGenesInterim' available at http://survgenesinter.R-Forge.R-Project.org, can be used for sample size planning of the evaluated study design."],["dc.identifier.doi","10.1186/1471-2105-12-127"],["dc.identifier.isi","000290784600001"],["dc.identifier.pmid","21527044"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6343"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23260"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2105"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Sequential interim analyses of survival data in DNA microarray experiments"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Onkologie"],["dc.bibliographiccitation.volume","36"],["dc.contributor.author","Bleckmann, Annalen"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Artmann, Stephan"],["dc.contributor.author","Menck, Kerstin"],["dc.contributor.author","Binder, Claudia"],["dc.contributor.author","Pukrop, Tobias"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Klemm, E."],["dc.date.accessioned","2018-11-07T09:19:06Z"],["dc.date.available","2018-11-07T09:19:06Z"],["dc.date.issued","2013"],["dc.identifier.isi","000326360900431"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28557"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Karger"],["dc.publisher.place","Basel"],["dc.title","Identification of prognostic miRNAs in breast cancer through profiling of tumor educated macrophages"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","48"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Radiotherapy and Oncology"],["dc.bibliographiccitation.lastpage","54"],["dc.bibliographiccitation.volume","108"],["dc.contributor.author","Wolff, Hendrik Andreas"],["dc.contributor.author","Conradi, Lena-Christin"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Hohenberger, Werner"],["dc.contributor.author","Merkel, Susanne"],["dc.contributor.author","Fietkau, Rainer"],["dc.contributor.author","Raab, Hans-Rudolf"],["dc.contributor.author","Tschmelitsch, Joerg"],["dc.contributor.author","Hess, Clemens Friedrich"],["dc.contributor.author","Becker, Heinz"],["dc.contributor.author","Wittekind, Christian"],["dc.contributor.author","Sauer, Rolf"],["dc.contributor.author","Roedel, Claus"],["dc.contributor.author","Liersch, Torsten"],["dc.date.accessioned","2018-11-07T09:22:42Z"],["dc.date.available","2018-11-07T09:22:42Z"],["dc.date.issued","2013"],["dc.description.abstract","Introduction: The CAO/ARO/AIO-94 phase-III-trial demonstrated a significant improvement of preoperative chemoradiotherapy (CRT) versus postoperative CRT on local control for UICC stage II/III rectal cancer patients, but no effect on long-term survival. In this add-on evaluation, we investigated the association of gender and age with acute toxicity and outcome. Patients and methods: According to actual treatment analyses, 654 of 799 patients had received pre-(n = 406) or postoperative CRT (n = 248); in 145 patients postoperative CRT was not applied. Gender, age and clinicopathological parameters were correlated with CRT-associated acute toxicity and survival. Results: The 10-year survival was higher in women than in men, with 72.4% versus 65.6% for time to recurrence (p = 0.088) and 62.7% versus 58.4% for overall-survival (OS) (p = 0.066), as expected. For patients receiving CRT, women showed higher hematologic (p < 0.001) and acute organ toxicity (p < 0.001) in the entire cohort as well as in subgroup analyses according to pre- (p = 0.016) and postoperative CRT (p < 0.001). Lowest OS was seen in patients without acute toxicity (p = 0.0271). Multivariate analyses for OS showed that acute organ toxicity (p = 0.034) was beneficial while age (p < 0.001) was associated with worse OS. Discussion: Female gender is significantly associated with CRT-induced acute toxicity in rectal cancer. Acute toxicity during CRT may be associated with improved long-term outcome. (C) 2013 Elsevier Ireland Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.radonc.2013.05.009"],["dc.identifier.isi","000324155900007"],["dc.identifier.pmid","23768685"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11335"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29411"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Ireland Ltd"],["dc.relation.issn","0167-8140"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Gender affects acute organ toxicity during radiochemotherapy for rectal cancer: Long-term results of the German CAO/ARO/AIO-94 phase III trial"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1003"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Neuromuscular Disorders"],["dc.bibliographiccitation.lastpage","1017"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Mannil, Manoj"],["dc.contributor.author","Solari, Alessandra"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Pelayo-Negro, Ana L."],["dc.contributor.author","Berciano, Jose"],["dc.contributor.author","Schlotter-Weigel, Beate"],["dc.contributor.author","Walter, Maggie C."],["dc.contributor.author","Rautenstrauss, Bernd"],["dc.contributor.author","Schnizer, Tuuli Johanna"],["dc.contributor.author","Schenone, Angelo"],["dc.contributor.author","Seeman, Pavel"],["dc.contributor.author","Kadian, Chandini"],["dc.contributor.author","Schreiber, Olivia"],["dc.contributor.author","Angarita, Natalia G."],["dc.contributor.author","Fabrizi, Gian Maria"],["dc.contributor.author","Gemignani, Franco"],["dc.contributor.author","Padua, Luca"],["dc.contributor.author","Santoro, Lucio"],["dc.contributor.author","Quattrone, Aldo"],["dc.contributor.author","Vita, Giuseppe"],["dc.contributor.author","Calabrese, Daniela"],["dc.contributor.author","Young, Peter"],["dc.contributor.author","Laura, Matilde"],["dc.contributor.author","Haberlova, Jana"],["dc.contributor.author","Mazanec, Radim"],["dc.contributor.author","Paulus, Walter J."],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Shy, Michael E."],["dc.contributor.author","Reilly, Mary M."],["dc.contributor.author","Pareyson, Davide"],["dc.contributor.author","Sereda, Michael Werner"],["dc.date.accessioned","2018-11-07T09:32:51Z"],["dc.date.available","2018-11-07T09:32:51Z"],["dc.date.issued","2014"],["dc.description.abstract","This study evaluates primary and secondary clinical outcome measures in Charcot-Marie-Tooth disease type 1A (CMT1A) with regard to their contribution towards discrimination of disease severity. The nine components of the composite Charcot-Marie-Tooth disease Neuropathy Score and six additional secondary clinical outcome measures were assessed in 479 adult patients with genetically proven CMT1A and 126 healthy controls. Using hierarchical clustering, we identified four significant clusters of patients according to clinical severity. We then tested the impact of each of the CMTNS components and of the secondary clinical parameters with regard to their power to differentiate these four clusters. The CMTNS components ulnar sensory nerve action potential (SNAP), pin sensibility, vibration and strength of arms did not increase the discriminant value of the remaining five CMTNS components (Ulnar compound motor action potential [CMAP], leg motor symptoms, arm motor symptoms, leg strength and sensory symptoms). However, three of the six additional clinical outcome measures the 10 m-timed walking test (T10MW), 9 hole-peg test (9HPT), and foot dorsal flexion dynamometry further improved discrimination between severely and mildly affected patients. From these findings, we identified three different composite measures as score hypotheses and compared their discriminant power with that of the CMTNS. A composite of eight components CMAP, Motor symptoms legs, Motor symptoms arms, Strength of Legs, Sensory symptoms), displayed the strongest power to discriminate between the clusters. As a conclusion, five items from the CMTNS and three secondary clinical outcome measures improve the clinical assessment of patients with CMT1A significantly and are beneficial for upcoming clinical and therapeutic trials. (C) 2014 Elsevier B.V. All rights reserved."],["dc.description.sponsorship","Medical Research Council [MR/K000608/1]"],["dc.identifier.doi","10.1016/j.nmd.2014.06.431"],["dc.identifier.isi","000345733600011"],["dc.identifier.pmid","25085517"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/31838"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Pergamon-elsevier Science Ltd"],["dc.relation.issn","1873-2364"],["dc.relation.issn","0960-8966"],["dc.title","Selected items from the Charcot-Marie-Tooth (CMT) Neuropathy Score and secondary clinical outcome measures serve as sensitive clinical markers of disease severity in CMT1A patients"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","8123"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","Oncotarget"],["dc.bibliographiccitation.lastpage","8135"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Gaedcke, Jochen"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Claus, Rainer"],["dc.contributor.author","Weichenhan, Dieter"],["dc.contributor.author","Jung, Klaus"],["dc.contributor.author","Kitz, Julia"],["dc.contributor.author","Grade, Marian"],["dc.contributor.author","Wolff, Hendrik A"],["dc.contributor.author","Jo, Peter"],["dc.contributor.author","Doyen, Jerome"],["dc.contributor.author","Gérard, Jean-Pierre"],["dc.contributor.author","Johnsen, Steven Arthur"],["dc.contributor.author","Plass, Christoph"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Ghadimi, Michael B."],["dc.date.accessioned","2021-06-01T10:48:31Z"],["dc.date.available","2021-06-01T10:48:31Z"],["dc.date.issued","2014"],["dc.description.abstract","In locally advanced rectal cancer a preoperative predictive biomarker is necessary to adjust treatment specifically for those patients expected to suffer relapse. We applied whole genome methylation CpG island array analyses to an initial set of patients (n=11) to identify differentially methylated regions (DMRs) that separate a good from a bad prognosis group. Using a quantitative high-resolution approach, candidate DMRs were first validated in a set of 61 patients (test set) and then confirmed DMRs were further validated in additional independent patient cohorts (n=71, n=42). We identified twenty highly discriminative DMRs and validated them in the test set using the MassARRAY technique. Ten DMRs could be confirmed which allowed separation into prognosis groups (p=0.0207, HR=4.09). The classifier was validated in two additional cohorts (n=71, p=0.0345, HR=3.57 and n=42, p=0.0113, HR=3.78). Interestingly, six of the ten DMRs represented regions close to the transcriptional start sites of genes which are also marked by the Polycomb Repressor Complex component EZH2. In conclusion we present a classifier comprising 10 DMRs which predicts patient prognosis with a high degree of accuracy. These data may now help to discriminate between patients that may respond better to standard treatments from those that may require alternative modalities."],["dc.identifier.doi","10.18632/oncotarget.2347"],["dc.identifier.isi","000348030900013"],["dc.identifier.pmid","25261372"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11888"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85964"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Impact Journals Llc"],["dc.relation.eissn","1949-2553"],["dc.relation.issn","1949-2553"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Identification of a DNA methylation signature to predict disease-free survival in locally advanced rectal cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","42"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Genome Medicine"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Chereda, Hryhorii"],["dc.contributor.author","Bleckmann, Annalen"],["dc.contributor.author","Menck, Kerstin"],["dc.contributor.author","Perera-Bel, Júlia"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Auer, Florian"],["dc.contributor.author","Kramer, Frank"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2021-04-14T08:28:07Z"],["dc.date.accessioned","2022-08-18T12:39:45Z"],["dc.date.available","2021-04-14T08:28:07Z"],["dc.date.available","2022-08-18T12:39:45Z"],["dc.date.issued","2021-03-11"],["dc.date.updated","2022-07-29T12:18:05Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the machine learning community made recent elaborations on interpretability methods explaining data point-specific decisions of deep learning techniques. We believe that such explanations can assist the need in personalized precision medicine decisions via explaining patient-specific predictions.\r\n \r\n \r\n Methods\r\n Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g., distant metastasis in cancer, for each individual patient.\r\n \r\n \r\n Results\r\n We extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset and then apply the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression.\r\n \r\n \r\n Conclusions\r\n The developed method could be potentially highly useful on interpreting classification results in the context of different omics data and prior knowledge molecular networks on the individual patient level, as for example in precision medicine approaches or a molecular tumor board."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","Genome Medicine. 2021 Mar 11;13(1):42"],["dc.identifier.doi","10.1186/s13073-021-00845-7"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17744"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82506"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112973"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1756-994X"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","Gene expression data"],["dc.subject","Explainable AI"],["dc.subject","Personalized medicine"],["dc.subject","Precision medicine"],["dc.subject","Classification of cancer"],["dc.subject","Deep learning"],["dc.subject","Prior knowledge"],["dc.subject","Molecular networks"],["dc.title","Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","455"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Cancers"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Koerdel, Kristin"],["dc.contributor.author","Spitzner, Melanie"],["dc.contributor.author","Meyer, Thomas"],["dc.contributor.author","Engels, Niklas"],["dc.contributor.author","Krause, Florian"],["dc.contributor.author","Gaedcke, Jochen"],["dc.contributor.author","Conradi, Lena-Christin"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Johnsen, Steven A."],["dc.contributor.author","Ghadimi, B. Michael"],["dc.contributor.author","Rose-John, Stefan"],["dc.contributor.author","Grade, Marian"],["dc.contributor.author","Wienands, Jürgen"],["dc.date.accessioned","2021-04-14T08:29:46Z"],["dc.date.available","2021-04-14T08:29:46Z"],["dc.date.issued","2021"],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft"],["dc.identifier.doi","10.3390/cancers13030455"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82985"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-6694"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","NOTCH Activation via gp130/STAT3 Signaling Confers Resistance to Chemoradiotherapy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Strahlentherapie und Onkologie"],["dc.bibliographiccitation.volume","189"],["dc.contributor.author","Wolff, Hendrik Andreas"],["dc.contributor.author","Conradi, Lena-Christin"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Leha, Andreas"],["dc.contributor.author","Hohenberger, Werner"],["dc.contributor.author","Merkel, S."],["dc.contributor.author","Fietkau, Rainer"],["dc.contributor.author","Raab, H-R"],["dc.contributor.author","Tschmelitsch, Jörg"],["dc.contributor.author","Hess, Clemens Friedrich"],["dc.contributor.author","Becker, H."],["dc.contributor.author","Wittekind, Christian"],["dc.contributor.author","Sauer, R."],["dc.contributor.author","Liersch, Torsten"],["dc.contributor.author","Roedel, Claus"],["dc.date.accessioned","2018-11-07T09:25:25Z"],["dc.date.available","2018-11-07T09:25:25Z"],["dc.date.issued","2013"],["dc.format.extent","17"],["dc.identifier.isi","000317982100031"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30063"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Urban & Vogel"],["dc.publisher.place","Munich"],["dc.relation.conference","19th Annual Congress of the German-Society-for-Radiation-Oncology"],["dc.relation.eventlocation","Berlin, GERMANY"],["dc.relation.issn","0179-7158"],["dc.title","Gender influences acute Toxicity during Chemoradiotherapy in Patients with Rectal cancer: Long-term results of the CAO/ARO/AIO 94 Trial"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]