We assessed the trustworthiness of two self-supervision pretrained transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor segmentation using 670 CT and MRI scans. We measured segmentation accuracy on two public 3D-CT datasets, robustness on CT scans of patients with COVID-19, CT scans of patients with ovarian cancer and T2-weighted MRI of men with prostate cancer, and zero-shot generalization of LC for T2-weighted MRIs. Both models demonstrated high accuracy on in-distribution data (Dice 0.80 for SMIT and 0.78 for Swin UNETR). SMIT showed similar near-out-of-distribution performance on CT scans (AUROC 89.85% vs. 89.19%) but significantly better far-out-of-distribution accuracy on CT (AUROC 97.2% vs. 87.1%) and MRI (92.15% vs. 73.8%). SMIT outperformed Swin UNETR in zero-shot segmentation on MRI (Dice 0.78 vs. 0.69). We expect these findings to guide the safe development and deployment of current and future pretrained models in routine clinical use.

基于670个CT和MRI扫描，评估了两个自监督预训练的Transformer模型Swin UNETR和SMIT在肺肿瘤细分领域的可信度。模型表现出了较高的准确性和稳健性，并在不同领域的CT和MRI扫描中展现了良好的分割性能。研究结果有望在临床中指导当前和未来预训练模型的安全开发和应用。

预训练变换器用于肺癌分割的可信度