One Sequence Can Suffice for Brain MRI AI, but Generalizability Remains a Concern

A single MRI sequence (T2-FLAIR) may be enough for AI brain tumor segmentation, but models trained this way show a 10–20% drop in Dice score when applied to external data, highlighting a generalizability gap.

• Study design: Retrospective analysis of deep learning segmentation models trained on single vs. multi-sequence MRI from public datasets (exact n not reported in source).
• Secondary finding: Performance gaps widened in postoperative and lower-grade glioma cases where tissue boundaries are more ambiguous.
• Limitation: Retrospective design and reliance on public datasets that may not reflect real-world scanner and protocol variability, limiting immediate clinical translation.

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