An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.

Theophanous, S.; Lonne, PI.; Choudhury, A.; Berbee, M.; Deijen, C.; Dekker, A.; Field, M.; Gambacorta, MA.; Gilbert, A.; Gronlie Guren, M.; Jadon, R.; Kochhar, R.; Martin, D.; Mohamed, AA.; Muirhead, R.; Pares, O.; Raszewski, L.; Roy, R.; Scarsbrook, A.; Sebag-Montefiore, D.; Spezi, E.; Vassiliou, V.; Malinen, E.; Wee, L.; Appelt, A.

An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.

All Authors

Theophanous, S.

Lonne, PI.

Choudhury, A.

Berbee, M.

Deijen, C.

Dekker, A.

Field, M.

Gambacorta, MA.

Gilbert, A.

Gronlie Guren, M.

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LTHT Author

Theophanous, Stelios
Theophanous, Stelios
Gilbert, Alexandra
Scarsbrook, Andrew
Appelt, Ane

LTHT Department

Medical Physics & Engineering
Oncology
Leeds Cancer Centre
Radiology

Non Medic

Physicist

Publication Date

2026

Item Type

Journal Article

Abstract

Precision oncology relies on access to high-quality data for increasingly smaller patient subgroups. The international atomCAT consortium investigates the potential of federated learning to support this, using anal cancer as a rare cancer exemplar. Here, we show that federated multivariable Cox models trained across 14 centres (1428 patients) and externally validated in two additional centres (277 patients) achieve consistent calibration and discrimination during leave-one-centre-out and external validation (c-indices 0.68-0.79). Lower T stage, absence of nodal involvement, smaller tumour volume, female sex, younger age, and mitomycin- or cisplatin-based chemotherapy are associated with improved overall survival. Lower T stage, smaller tumour volume, and female sex are associated with improved locoregional control, while absence of nodal involvement and smaller tumour volume are associated with better freedom from distant metastases. These findings demonstrate that federated learning enables robust, privacy-preserving prognostic modelling for rare cancers using real-world data, supporting international collaboration without data sharing.