The Genomic Analysis of Patients with Musculoskeletal Metastases from an Unknown Origin. (2025)
Journal Article
Eastley, N., Cool, P., Jafri, M., Raghavan, M., & Stevenson, J. (2025). The Genomic Analysis of Patients with Musculoskeletal Metastases from an Unknown Origin. Surgical Oncology, 58, 1-5. https://doi.org/10.1016/j.suronc.2025.102187

Background
A subgroup of patients present with musculoskeletal (MSK) metastases but no detectable primary tumour. An inability to employ disease-specific treatment means this cohort's median survival is just 6–10 months. We present a novel, prospect... Read More about The Genomic Analysis of Patients with Musculoskeletal Metastases from an Unknown Origin..

A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides (2024)
Journal Article
Shen, Z., Simard, M., Brand, D., Andrei, V., Al-Khader, A., Oumlil, F., Trevers, K., Butters, T., Haefliger, S., Kara, E., Amary, F., Tirabosco, R., Cool, P., Royle, G., Hawkins, M. A., Flanagan, A. M., & Collins-Fekete, C.-A. (in press). A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides. Communications Biology, 7(1), 1674. https://doi.org/10.1038/s42003-024-07398-6

Mitotic activity is an important feature for grading several cancer types. However, counting mitotic figures (cells in division) is a time-consuming and laborious task prone to inter-observer variation. Inaccurate recognition of MFs can lead to incor... Read More about A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides.