  Objectives: This work aims to explore the impact of multicenter data
heterogeneity on deep learning brain metastases (BM) autosegmentation
performance, and assess the efficacy of an incremental transfer learning
technique, namely learning without forgetting (LWF), to improve model
generalizability without sharing raw data.
  Materials and methods: A total of six BM datasets from University Hospital
Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, NYU and
BraTS Challenge 2023 on BM segmentation were used for this evaluation. First,
the multicenter performance of a convolutional neural network (DeepMedic) for
BM autosegmentation was established for exclusive single-center training and
for training on pooled data, respectively. Subsequently bilateral collaboration
was evaluated, where a UKER pretrained model is shared to another center for
further training using transfer learning (TL) either with or without LWF.
  Results: For single-center training, average F1 scores of BM detection range
from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed
multicenter training notably improves F1 scores at Stanford and NYU, with
negligible improvement at other centers. When the UKER pretrained model is
applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL
(0.570) and single-center training (0.688) on combined UKER and USZ test data.
Naive TL improves sensitivity and contouring accuracy, but compromises
precision. Conversely, LWF demonstrates commendable sensitivity, precision and
contouring accuracy. When applied to Stanford, similar performance was
observed.
  Conclusion: Data heterogeneity results in varying performance in BM
autosegmentation, posing challenges to model generalizability. LWF is a
promising approach to peer-to-peer privacy-preserving model training.
