Refine
Year of publication
Document Type
- Article (104)
- Preprint (37)
- Conference Proceeding (2)
Language
- English (143)
Has Fulltext
- yes (143)
Is part of the Bibliography
- no (143)
Keywords
- LHC (7)
- ALICE (3)
- ALICE experiment (3)
- Hadron-Hadron Scattering (3)
- pp collisions (3)
- Beauty production (2)
- Patterns of care (2)
- Radiotherapy (2)
- SRSF3 (2)
- SRSF7 (2)
- Single electrons (2)
- iCLIP (2)
- 3′UTR length (1)
- 900 GeV (1)
- APA (1)
- Acetogen (1)
- Anal cancer (1)
- Artificial Intelligence (1)
- Atomic force microscopy (1)
- Atoms (1)
- Biochemistry (1)
- Biodiversity (1)
- Biomarker (1)
- Body Composition (1)
- Bone cancer (1)
- Brain metastasis (1)
- CD74 (1)
- CD8+ tumor infiltrating lymphocytes (1)
- CFIm (1)
- COVID (1)
- Cancer genetics (1)
- Cancer survivors (1)
- Cardiac biomarkers (1)
- Cardio-oncology (1)
- Cardiotoxicity (1)
- Charm physics (1)
- Chemoradiotherapy (1)
- Circulating MiRNA (1)
- Clinical Trials and Observations (1)
- Compact astrophysical objects (1)
- Comparators (1)
- Comparison with QCD (1)
- DNA damage (1)
- DNA sequencing (1)
- Database searching (1)
- E. coli (1)
- EP300 (1)
- EWSR1 (1)
- Ech (1)
- Ecosystem services (1)
- Energy conservation (1)
- Exercise therapy (1)
- FIP1 (1)
- FOXO1 (1)
- Femtoscopy (1)
- G protein-coupled receptors (1)
- Galaxies and clusters (1)
- Gene fusion (1)
- Genome sequence (1)
- H+ transport (1)
- HBT (1)
- HIV (1)
- HLA class II (1)
- HLA peptidome (1)
- HNSCC (1)
- Hadron production (1)
- Head and neck cancer (1)
- Heart failure (1)
- Heavy Ions (1)
- Heavy flavor production (1)
- Heavy flavour production (1)
- Heavy ions (1)
- Heavy-flavour production (1)
- Heavy-ion collisions (1)
- Hepatocellular Carcinoma (1)
- High-energy astrophysics (1)
- Human well-being (1)
- ICON model (1)
- Imrt (1)
- Inclusive spectra (1)
- Intensity interferometry (1)
- Ions (1)
- Jets (1)
- KCGS (1)
- Liver Fibrosis (1)
- Loco-regional control (1)
- Lower back pain (1)
- Lymphoid Neoplasia (1)
- MACE-seq (1)
- Machine learning (1)
- Mediation (1)
- Medicinal chemistry (1)
- Membrane biophysics (1)
- Metaanalysis (1)
- MiR-29 (1)
- Mid-rapidity (1)
- Molecular diagnostics (1)
- Multi-strange baryons (1)
- NR4A2 (1)
- NXF1 (1)
- Nature (1)
- Neuroepithelial tumor (1)
- Nuclear modification factor (1)
- PD-L1 (1)
- PLAGL1 (1)
- PYTHIA (1)
- Pain sensation (1)
- Pb–Pb (1)
- Perturbative methods (1)
- Pharmacology (1)
- Proton–proton (1)
- Public health (1)
- RNA Biology (1)
- Radiomics (1)
- Randomized controlled trials (1)
- Relativistic heavy ion physics (1)
- Risk stratification (1)
- SR protein (1)
- Salivary gland cancer (1)
- Salivary gland carcinoma (1)
- Scattering of atoms, molecules, clusters & ions (1)
- Scattering theory (1)
- Single muons (1)
- Small molecules (1)
- Stereotactic ablative body radiotherapy (1)
- Strength training (1)
- Supratentorial (1)
- Surgery (1)
- Survey (1)
- Systemic therapy (1)
- Target validation (1)
- Thermoanaerobacter kivui (1)
- Transarterial Chemoembolization (1)
- Transverse momentum (1)
- Tumor infiltrating lymphocytes (1)
- Validation (1)
- accident (1)
- alternative 3′ end processing (1)
- anal cancer (1)
- antibodies (1)
- archeological modeling (1)
- b-cell lymphomas (1)
- bendamustine (1)
- biomarker (1)
- biophysics (1)
- cancer-specific survival (1)
- caspase 8 (1)
- cell biology (1)
- cervical cancer (1)
- chemogenomic set (1)
- chemoradiotherapy (1)
- chemotherapy regimen (1)
- chimeric antigen receptor t-cell therapy (1)
- chimeric antigen receptors (1)
- dPAS (1)
- diffusion-weighted magnetic resonance imaging (1)
- dislocation (1)
- drug discovery (1)
- druggable genome (1)
- e-scooter (1)
- electric scooter (1)
- everolimus (1)
- fatty acid/biosynthesis (1)
- filopodia (1)
- fracture (1)
- gene signature (1)
- head and neck squamous cell carcinoma (1)
- hematopoietic stem cell transplantation (1)
- human–environment interaction (1)
- image-based risk modelling (1)
- injury (1)
- kinase inhibitor (1)
- kinetically trapped state (1)
- leukapheresis (1)
- leukopenia (1)
- lipidomics (1)
- local control (1)
- lockdown (1)
- lymphoma (1)
- mRNA export (1)
- machine learning (1)
- membranes/fluidity (1)
- merkel cell carcinoma (1)
- metastatic renal cell carcinoma (1)
- mouse (1)
- multiplexed immunofluorescence (1)
- neoadjuvant chemoradiotherapy (1)
- neurodegeneration (1)
- nuclear receptor-related 1 (1)
- oocytes (1)
- open science (1)
- oral cavity cancer (1)
- outcome (1)
- overall survival (1)
- p63 (1)
- pPAS (1)
- paleoclimate modeling (1)
- paleoenvironment modeling (1)
- paleoenvironment reconstruction (1)
- parameterization development (1)
- pediatric intensive care (1)
- personalised therapy (1)
- pharmacophore model (1)
- phase IV (1)
- phenotypic screening (1)
- phospholipids/phosphatidylglycerol (1)
- polo-like kinase 3 (1)
- polyomavirus (MCPyV) (1)
- postoperative radiochemotherapy (1)
- postoperative radiotherapy (1)
- predictive biomarker (1)
- propensity score matching (1)
- protein kinase (1)
- quality control (1)
- radiation oncology (1)
- radioimmunotherpay (1)
- radiomic (1)
- rituximab (1)
- sarcopenia (1)
- second-line (1)
- single-column mode (1)
- small molecules (1)
- soraphen (1)
- spectra (1)
- spring-loaded activation (1)
- structural biolog (1)
- thrombopenia (1)
- toxicity (1)
- traffic accident (1)
- transcription factor (1)
- transportation (1)
- trauma (1)
- traumatic brain injury (1)
- understudied kinase (1)
- validation (1)
- vascular biology/endothelial cells (1)
- √sN N = 2.76 TeV (1)
Institute
- Physik (83)
- Frankfurt Institute for Advanced Studies (FIAS) (69)
- Informatik (68)
- Medizin (39)
- Biowissenschaften (9)
- Biochemie, Chemie und Pharmazie (5)
- Geowissenschaften (5)
- Biochemie und Chemie (3)
- Buchmann Institut für Molekulare Lebenswissenschaften (BMLS) (2)
- Exzellenzcluster Makromolekulare Komplexe (2)
Comprehensive analysis of tumour sub-volumes for radiomic risk modelling in locally advanced HNSCC
(2020)
Simple Summary: Radiomic risk models are usually based on imaging features, which are extracted from the entire gross tumour volume (GTV entire ). This approach does not explicitly consider the complex biological structure of the tumours. Therefore, in this retrospective study, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma who were treated with primary radio-chemotherapy. The GTV entire was cropped by different margins to define the rim and corresponding core sub-volumes of the tumour. Furthermore, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. As a result, the models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed an improved performance compared to models based on the corresponding tumour core. This indicates that the consideration of tumour sub-volumes may help to improve radiomic risk models.
Abstract: Imaging features for radiomic analyses are commonly calculated from the entire gross tumour volume (GTVentire). However, tumours are biologically complex and the consideration of different tumour regions in radiomic models may lead to an improved outcome prediction. Therefore, we investigated the prognostic value of radiomic analyses based on different tumour sub-volumes using computed tomography imaging of patients with locally advanced head and neck squamous cell carcinoma. The GTVentire was cropped by different margins to define the rim and the corresponding core sub-volumes of the tumour. Subsequently, the best performing tumour rim sub-volume was extended into surrounding tissue with different margins. Radiomic risk models were developed and validated using a retrospective cohort consisting of 291 patients in one of the six Partner Sites of the German Cancer Consortium Radiation Oncology Group treated between 2005 and 2013. The validation concordance index (C-index) averaged over all applied learning algorithms and feature selection methods using the GTVentire achieved a moderate prognostic performance for loco-regional tumour control (C-index: 0.61 ± 0.04 (mean ± std)). The models based on the 5 mm tumour rim and on the 3 mm extended rim sub-volume showed higher median performances (C-index: 0.65 ± 0.02 and 0.64 ± 0.05, respectively), while models based on the corresponding tumour core volumes performed less (C-index: 0.59 ± 0.01). The difference in C-index between the 5 mm tumour rim and the corresponding core volume showed a statistical trend (p = 0.10). After additional prospective validation, the consideration of tumour sub-volumes may be a promising way to improve prognostic radiomic risk models.
Purpose: To develop and validate a CT-based radiomics signature for the prognosis of loco-regional tumour control (LRC) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated by primary radiochemotherapy (RCTx) based on retrospective data from 6 partner sites of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG).
Material and methods: Pre-treatment CT images of 318 patients with locally advanced HNSCC were collected. Four-hundred forty-six features were extracted from each primary tumour volume and then filtered through stability analysis and clustering. First, a baseline signature was developed from demographic and tumour-associated clinical parameters. This signature was then supplemented by CT imaging features. A final signature was derived using repeated 3-fold cross-validation on the discovery cohort. Performance in external validation was assessed by the concordance index (C-Index). Furthermore, calibration and patient stratification in groups with low and high risk for loco-regional recurrence were analysed.
Results: For the clinical baseline signature, only the primary tumour volume was selected. The final signature combined the tumour volume with two independent radiomics features. It achieved moderately good discriminatory performance (C-Index [95% confidence interval]: 0.66 [0.55–0.75]) on the validation cohort along with significant patient stratification (p = 0.005) and good calibration.
Conclusion: We identified and validated a clinical-radiomics signature for LRC of locally advanced HNSCC using a multi-centric retrospective dataset. Prospective validation will be performed on the primary cohort of the HNprädBio trial of the DKTK-ROG once follow-up is completed.
Highlights:
• Assessment of body composition parameters in a large cohort of patients with HCC undergoing TACE.
• Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition.
• Skeletal muscle volume and related parameters were independent prognostic factors in patients with HCC undergoing TACE.
Background & Aims: Body composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Herein, we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE).
Methods: This retrospective multicenter study included a total of 754 treatment-naïve patients with HCC who underwent TACE at six tertiary care centers between 2010–2020. Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition was performed to assess skeletal muscle volume (SM), total adipose tissue (TAT), intra- and intermuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue (SAT) on pre-intervention computed tomography scans. BCA parameters were normalized to the slice number of the abdominal cavity. We assessed the influence of BCA parameters on median overall survival and performed multivariate analysis including established estimates of survival.
Results: Univariate survival analysis revealed that impaired median overall survival was predicted by low SM (p <0.001), high TAT volume (p = 0.013), and high SAT volume (p = 0.006). In multivariate survival analysis, SM remained an independent prognostic factor (p = 0.039), while TAT and SAT volumes no longer showed predictive ability. This predictive role of SM was confirmed in a subgroup analysis of patients with BCLC stage B.
Conclusions: SM is an independent prognostic factor for survival prediction. Thus, the integration of SM into novel scoring systems could potentially improve survival prediction and clinical decision-making. Fully automated approaches are needed to foster the implementation of this imaging biomarker into daily routine.
Impact and implications: Body composition assessment parameters, especially skeletal muscle volume, have been identified as relevant prognostic factors for many diseases and treatments. In this study, skeletal muscle volume has been identified as an independent prognostic factor for patients with hepatocellular carcinoma undergoing transarterial chemoembolization. Therefore, skeletal muscle volume as a metaparameter could play a role as an opportunistic biomarker in holistic patient assessment and be integrated into decision support systems. Workflow integration with artificial intelligence is essential for automated, quantitative body composition assessment, enabling broad availability in multidisciplinary case discussions.