Publications by authors named "J Budczies"
Objectives: Evaluating invasion in non-mucinous adenocarcinoma (NMA) of the lung is crucial for accurate pT-staging. This study compares the World Health Organization (WHO) with a recently modified NMA classification.
Materials And Methods: A retrospective case-control study was conducted on small NMA pT1N0M0 cases with a 5-year follow-up.
The adoption of comprehensive genomic profiling in oncology has rapidly increased the demand for standardized tumor sample processing in diagnostic laboratories. Automation of DNA and RNA library preparation workflows offers the possibility to scale-up and standardize sample processing. We report on the clinical implementation of the automated TruSight Oncology 500 High-Throughput library preparation workflow from formalin-fixed, paraffin-embedded tumor samples using the Biomek i7 hybrid Workstation.
View Article and Find Full Text PDFBackground: We aimed to validate the prognostic significance of tumor budding (TB) in p16-positive oropharyngeal squamous cell carcinomas (OPSCC).
Methods: We analyzed digitized H&E-stained slides from a multicenter cohort of five large university centers consisting of n = 275 cases of p16-positive OPSCC. We evaluated TB along with other histological parameters (morphology, tumor-stroma-ratio, lymphovascular invasion (LVI), perineural invasion) and calculated survival outcomes using both univariate and multivariate analyses.
Article Synopsis
- Whole Exome Sequencing (WES) is a powerful tool in cancer diagnostics that allows for comprehensive analysis of genes, improving the detection of complex biomarkers compared to traditional panel-based methods.
- A study analyzing tissue specimens across 21 NGS centers showed that, although there was a 76% agreement in somatic variant calling, refining filtering criteria improved this to 88%, highlighting the importance of filter settings in variant detection.
- The reliability of detecting specific genomic changes (like CNAs and complex biomarkers) varied among labs, emphasizing the need for improved bioinformatics processes and collaborative testing to minimize discrepancies in future analyses.
Introduction: Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability.
Methods: This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples.