Jürgen Debus

Bio: Jürgen Debus is an academic researcher from University Hospital Heidelberg. The author has contributed to research in topics: Radiation therapy & Medicine. The author has an hindex of 75, co-authored 847 publications receiving 20964 citations. Previous affiliations of Jürgen Debus include Heidelberg University & German Cancer Research Center.

Intensified Chemotherapy and Dose-Reduced Involved-Field Radiotherapy in Patients With Early Unfavorable Hodgkin's Lymphoma: Final Analysis of the German Hodgkin Study Group HD11 Trial

Abstract: Purpose Combined-modality treatment consisting of four to six cycles of chemotherapy followed by involved-field radiotherapy (IFRT) is the standard of care for patients with early unfavorable Hodgkin's lymphoma (HL). It is unclear whether treatment results can be improved with more intensive chemotherapy and which radiation dose needs to be applied. Patients and Methods Patients age 16 to 75 years with newly diagnosed early unfavorable HL were randomly assigned in a 2 × 2 factorial design to one of the following treatment arms: four cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) + 30 Gy of IFRT; four cycles of ABVD + 20 Gy of IFRT; four cycles of bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone (BEACOPPbaseline) + 30 Gy of IFRT; or four cycles of BEACOPPbaseline + 20 Gy of IFRT. Results With a total of 1,395 patients included, the freedom from treatment failure (FFTF) at 5 years was 85.0%, overall survival was 94.5%, and progression-free...

TERT Promoter Mutations and Risk of Recurrence in Meningioma

Abstract: The World Health Organization (WHO) classification and grading system attempts to predict the clinical course of meningiomas based on morphological parameters. However, because of high interobserver variation of some criteria, more reliable prognostic markers are required. Here, we assessed the TERT promoter for mutations in the hotspot regions C228T and C250T in meningioma samples from 252 patients. Mutations were detected in 16 samples (6.4% across the cohort, 1.7%, 5.7%, and 20.0% of WHO grade I, II, and III cases, respectively). Data were analyzed by t test, Fisher's exact test, log-rank test, and Cox proportional hazard model. All statistical tests were two-sided. Within a mean follow-up time in surviving patients of 68.1 months, TERT promoter mutations were statistically significantly associated with shorter time to progression (P < .001). Median time to progression among mutant cases was 10.1 months compared with 179.0 months among wild-type cases. Our results indicate that the inclusion of molecular data (ie, analysis of TERT promoter status) into a histologically and genetically integrated classification and grading system for meningiomas increases prognostic power. Consequently, we propose to incorporate the assessment of TERT promoter status in upcoming grading schemes for meningioma.

Radiogenomics of Glioblastoma: Machine Learning–based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features

Abstract: Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. Materials and Methods Retrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, RTK I "PGFRA," RTK II "classic"), MGMT promoter methylation status, and hallmark copy number variations (EGFR, PDGFRA, MDM4, and CDK4 amplification; PTEN, CDKN2A, NF1, and RB1 loss). Univariate analyses (voxel-lesion symptom mapping for tumor location, Wilcoxon test for all other MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics. Results There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR [P < .01], 61% for RTK II [P = .01]) than prediction by chance; prediction accuracy for all other molecular parameters was not significant. Conclusion The authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma. © RSNA, 2016 Online supplemental material is available for this article.

Surgery for recurrent pancreatic ductal adenocarcinoma.

Abstract: Pancreatic ductal adenocarcinoma remains difficult to treat with an incidence rate that almost equals its mortality rate. The American Cancer Society estimated an incidence rate of 33,730 and a mortality rate of 32,300 patients for the year 2006, which clearly underscores the dismal prognosis of this disease.1 Pancreatic cancer is now the fourth leading cause of cancer-related deaths in the United States. Because of the progress that has been made over the last 20 years in diagnostics, surgery, and perioperative care, the operative mortality rates have fallen to well below 5% for pancreaticoduodenectomies at major centers. Nevertheless, the 5-year survival rate of resected patients remains only 15% to 20%.2 This is partly because even after macroscopically curative tumor resection, malignant cells are observed on the edge of resected specimens in up to 50% of cases.3 This R1-like situation explains the high local recurrence rate.3 Another reason is the presence of systemic occult disease at the time of diagnosis in most of the patients, leading to occurrence of distant metastasis in the liver (50% of resected patients) and peritoneal metastasis (25%).3–6 Until now, the commonly used treatment options for nonresectable pancreatic cancer have been chemotherapy with 5-fluorouracil or gemcitabine, palliative surgical bypass procedures, endoscopic or radiologically placed stents for biliary obstruction, palliative radiation procedures, or other palliative measures.7 For recurrent pancreatic cancer, there are no established therapeutic strategies, although the pattern of recurrence is well known. Sperti et al8 analyzed this in 78 patients after resection for pancreatic ductal adenocarcinoma: 72% developed local recurrence and 62% had hepatic metastases. The median disease-free survival was 7 months for local recurrence versus 3 months for hepatic recurrence. Similarly, several other studies have shown that pancreatic cancer patients who develop local recurrence without distant metastasis after curative resection of the primary tumor, appeared to have a better prognosis.6,9,10 The efficacy of combined chemoradiotherapy in these patients was recently described by Wilkowski et al.11 The median progression-free survival (ie, from the start of chemoradiotherapy) was 14.7 months and this may be a promising therapeutic option. A small study from Horiuchi et al12 compared 2 groups of patients with recurrent pancreatic cancer with liver metastasis: one group receiving chemotherapy versus the other group without chemotherapy. The administration of gemcitabine seemed to prolong the survival period from a mean survival rate of 6.6 months without any therapy to 22.3 months. To the best of our knowledge, there are no series and only few cases about reresection of recurrent pancreatic cancer.13 On the background of high incidence of local recurrence and the lack of effective chemotherapy,14 this paper reports on the outcome of patients reoperated for recurrent disease. We evaluated the survival of 30 patients who underwent radical resection with curative intent (R0/R1), R2 resection, palliative bypass, or only explorative laparotomy depending on the extent of the disease.

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

Abstract: Summary Background The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden Methods In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial ( NCT01290939 ), acquired at 38 institutions across Europe In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset) Findings For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset) For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86–0·90], and for NEs 0·93 [0·92–0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90–0·92], NEs 0·93 [0·93–0·94] in the EORTC-26101 test dataset) Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86–3·60] vs central RANO 2·07 [1·46–2·92]; p Interpretation Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases Funding Medical Faculty Heidelberg Postdoc-Program, Else Kroner-Fresenius Foundation

The 2021 WHO Classification of Tumors of the Central Nervous System: a summary.

Abstract: The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

The somatic genomic landscape of glioblastoma

Abstract: We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

Molecular imaging in living subjects: seeing fundamental biological processes in a new light

Abstract: References http://genesdev.cshlp.org/content/17/5/545.full.html#related-urls Article cited in: http://genesdev.cshlp.org/content/17/5/545.full.html#ref-list-1 This article cites 228 articles, 79 of which can be accessed free at: service Email alerting click here top right corner of the article or Receive free email alerts when new articles cite this article sign up in the box at the Collections Topic (33 articles) Molecular Physiology and Metabolism • (98 articles) Cancer and Disease Models • Articles on similar topics can be found in the following collections