Roberto Tirabosco
Royal National Orthopaedic Hospital
125 Papers
251 Citations
Roberto Tirabosco is an academic researcher from Royal National Orthopaedic Hospital. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 42, co-authored 110 publications.
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Papers
Fat-forming solitary fibrous tumour (lipomatous haemangiopericytoma) of the spine: case report and literature review
TL;DR: This is the first known report of a fat-forming SFT in the spine, along with a literature review, and presents the clinical, radiological and histological features of a case of intraspinal fat-formed SFT.
Melanotic schwannoma: an 11-year case series
TL;DR: A case series over an 11-year period is presented to highlight salient imaging features of melanotic schwannoma with reference to the current concerns regarding its malignant potential.
MRI-histopathological correlation in paediatric conventional central chondrosarcoma: a report of 17 cases
Amir Ardakani,Panagiotis D. Gikas,Michael Khoo,Paul O'Donnell,Paul O'Donnell,Roberto Tirabosco,Asif Saifuddin +6 more
TL;DR: MRI findings in paediatric CC-CS may be misleading, showing features suggestive of HGCS 7 of 17 (41.2%) of cases and this should be taken into consideration when planning surgical treatment.
Meta-analysis of IDH-mutant cancers identifies EBF1 as an interaction partner for TET2.
Paul Guilhamon,Malihe Eskandarpour,Dina Halai,Gareth A. Wilson,Andrew Feber,Andrew E. Teschendorff,Valenti Gomez,Alexander Hergovich,Roberto Tirabosco,M Fernanda Amary,Daniel Baumhoer,Gernot Jundt,Mark T. Ross,Adrienne M. Flanagan,Adrienne M. Flanagan,Stephan Beck +15 more
TL;DR: Meta-analysis of the acute myeloid leukaemia, low-grade glioma, cholangiocarcinoma and CS methylation data identifies cancer-specific effectors within the retinoic acid receptor activation pathway among the hypermethylated targets and identifies the transcription factor EBF1 as an interaction partner for TET2, suggesting a sequence-specific mechanism for regulating DNA methylation.
OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides
Zhuoyan Shen,Mikaël Simard,Douglas Brand,Vanghelita Andrei,Ali Al-Khader,Fatine Oumlil,Katherine Trevers,Thomas Butters,Simon Haefliger,Eleanna Kara,Fernanda Amary,Roberto Tirabosco,Paul Cool,Gary Royle,Maria A. Hawkins,Adrienne M Flanagan,Charles Fekete +16 more
TL;DR: This study proposes OMG-Net, a deep learning framework that detects pan-cancer mitotic figures from haematoxylin and eosin-stained slides with high accuracy (F1-score 0.84), outperforming previous benchmarks, and establishes a large pan-cancer dataset of 74,620 mitotic figures.