Publications by authors named "T Goranova"
Article Synopsis
- * Researchers developed a machine learning model that combines clinical, blood-based, and radiomic data from patients to predict changes in disease volume after NACT, achieving an 8% improvement in prediction accuracy when integrating radiomics.
- * The study shows the importance of using radiomics in patient response models, offering a potential path for creating new clinical trial methods focused on biomarkers in HGSOC treatment.
The drivers of recurrence and resistance in ovarian high grade serous carcinoma remain unclear. We investigate the acquisition of resistance by collecting tumour biopsies from a cohort of 276 women with relapsed ovarian high grade serous carcinoma in the BriTROC-1 study. Panel sequencing shows close concordance between diagnosis and relapse, with only four discordant cases.
View Article and Find Full Text PDFPurpose: Ovarian high-grade serous carcinoma (HGSC) is usually diagnosed at late stage. We investigated whether late-stage HGSC has unique genomic characteristics consistent with acquisition of evolutionary advantage compared with early-stage tumors.
Experimental Design: We performed targeted next-generation sequencing and shallow whole-genome sequencing (sWGS) on pretreatment samples from 43 patients with FIGO stage I-IIA HGSC to investigate somatic mutations and copy-number (CN) alterations (SCNA).
Purpose: Chromosomal aberration and DNA copy number change are robust hallmarks of cancer. The gold standard for detecting copy number changes in tumor cells is fluorescence in situ hybridization (FISH) using locus-specific probes that are imaged as fluorescent spots. However, spot counting often does not perform well on solid tumor tissue sections due to partially represented or overlapping nuclei.
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