Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing improved prediction accuracy and predictive power for undefined categories, while also mitigating the impact of poor-quality data. Despite these advantages, its application in predicting primary cancer is rare. To leverage the similarity of cancers and the specificity of methylation patterns among them, we developed the Cancer Hierarchy Classification Tool (CHCT) using the idea of hierarchical classification, with methylation data from 30 cancer types and 8239 methylome samples downloaded from publicly available databases (The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO)).
View Article and Find Full Text PDFBackground: High-throughput sequencing of blood cell-free DNA (cfDNA) techniques offer an opportunity to characterize and monitor cancer rapidly in a non-invasive and real-time manner. Nonetheless, there lacks a tool within therapeutic arsenal to identify multi-omics alterations simultaneously from a single biopsy. In current times, bisulfite-based sequencing detects 5mC and 5hmC at single-base resolution is the golden standard of DNA methylation, while the degradation of DNA and biased sequencing data are the problems of this method.
View Article and Find Full Text PDFPurpose/objective: We present our single-institution experience in the management of invasive breast cancer with targeted intraoperative radiotherapy (TARGIT-IORT), focusing on patient suitability for IORT determined by the American Society for Radiation Oncology (ASTRO) Accelerated Partial Breast Irradiation (APBI) consensus guidelines.
Materials/methods: We identified 237 patients treated for biopsy-proven early-stage invasive breast cancer using low energy x-ray TARGIT-IORT at the time of lumpectomy between September 2013 and April 2020 who were prospectively enrolled in an institutional review board (IRB) approved database. We retrospectively reviewed preoperative and postoperative clinicopathologic factors to determine each patient's ASTRO APBI suitability (suitable, cautionary or unsuitable) according to the 2017 consensus guidelines (CG).