Publications by authors named "M G Troiano"
Background: Hepatocellular carcinoma (HCC) exhibits an exceptional intratumoral heterogeneity that might influence diagnosis and outcome. Advances in digital microscopy and artificial intelligence (AI) may improve the HCC identification of liver cancer cells.
Aim: Two AI algorithms were designed to perform computer-assisted discrimination of tumour from non-tumour nuclei in HCC.
This study was aimed at introducing a new method for predicting the original metrics of fragmented standardized artifacts, specifically of flint blades from the Middle Pre-Pottery Neolithic B (10,200/100-9,500/400 cal B.P.) in the Southern Levant.
View Article and Find Full Text PDFHLA-A*02:01:189 differs from HLA-A*02:01:01:01 by one nucleotide substitution in Exon 3, codon 101 TGC > TGT.
View Article and Find Full Text PDFHLA-DQB1*06:02:61 differs from HLA-DQB1*06:02:01:01 by one nucleotide substitution in exon 4-5512 G>A.
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- Studies show HPV-positive and HPV-negative oropharyngeal squamous cell carcinoma (OPSCC) have different molecular profiles, tumor characteristics, and outcomes, highlighting a need for better predictive models.
- This paper presents an explainable Convolutional Neural Network (CNN) model that predicts HPV status in OPSCC patients using pre-treatment CT images, achieving a 73.50% AUC on an independent test set.
- The Grad-CAM technique was employed to identify crucial tumor areas related to predictions, suggesting that the model improves classification accuracy by revealing informative regions for clinical use.