Can CT Image Reconstruction Parameters Impact the Predictive Value of Radiomics Features in Grading Pancreatic Neuroendocrine Neoplasms?

The WHO grading of pancreatic neuroendocrine neoplasms (PanNENs) is essential in patient management and an independent prognostic factor for patient survival. Radiomics features from CE-CT images hold promise for the outcome and tumor grade prediction. However, variations in reconstruction parameters can impact the predictive value of radiomics.

Detection of clinically relevant variants in the gene below 10% allelic frequency: A multicenter study by ERIC, the European Research Initiative on CLL.

In chronic lymphocytic leukemia, the reliability of next-generation sequencing (NGS) to detect variants ≤10% allelic frequency (low-VAF) is debated. We tested the ability to detect 23 such variants in 41 different laboratories using their NGS method of choice. The sensitivity was 85.

MET Exon 14 Skipping and Novel Actionable Variants: Diagnostic and Therapeutic Implications in Latin American Non-Small-Cell Lung Cancer Patients.

Targeted therapy indications for actionable variants in non-small-cell lung cancer (NSCLC) have primarily been studied in Caucasian populations, with limited data on Latin American patients. This study utilized a 52-genes next-generation sequencing (NGS) panel to analyze 1560 tumor biopsies from NSCLC patients in Chile, Brazil, and Peru. The RNA sequencing reads and DNA coverage were correlated to improve the detection of the actionable exon 14 skipping variant (METex14).

Integration of RNA Editing with Multiomics Data Improves Machine Learning Models for Predicting Drug Responses in Breast Cancer Patients.

Background: The integration of conventional omics data such as genomics and transcriptomics data into artificial intelligence models has advanced significantly in recent years; however, their low applicability in clinical contexts, due to the high complexity of models, has been limited in their direct use inpatients. We integrated classic omics, including DNA mutation and RNA gene expression, added a novel focus on promising omics methods based on A>I(G) RNA editing, and developed a drug response prediction model.

Methods: We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (NCT02022202).