Increasing evidence supports watch-and-wait (WW) surveillance for patients with rectal cancer who show clinical complete response (cCR) at restaging following total neoadjuvant treatment (TNT). However, objectively accurate methods to early detect local regrowth (LR) from follow-up endoscopy images during WW are essential to manage care and prevent distant metastases. Hence, we developed a Siamese Swin Transformer with Dual Cross-Attention (SSDCA) to combine longitudinal endoscopic images at restaging and follow-up and distinguish cCR from LR. SSDCA leverages pretrained Swin transformers to extract domain agnostic features and enhance robustness to imaging variations. Dual cross attention is implemented to emphasize features from the two scans without requiring any spatial alignment of images to predict response. SSDCA as well as Swin-based baselines were trained using image pairs from 135 patients and evaluated on a held-out set of image pairs from 62 patients. SSDCA produced the best balanced accuracy (81.76\% $\pm$ 0.04), sensitivity (90.07\% $\pm$ 0.08), and specificity (72.86\% $\pm$ 0.05). Robustness analysis showed stable performance irrespective of artifacts including blood, stool, telangiectasia, and poor image quality. UMAP clustering of extracted features showed maximal inter-cluster separation (1.45 $\pm$ 0.18) and minimal intra-cluster dispersion (1.07 $\pm$ 0.19) with SSDCA, confirming discriminative representation learning.

翻译：越来越多的证据支持对经全新辅助治疗（TNT）后重新分期显示临床完全缓解（cCR）的直肠癌患者实施观察等待（WW）监测。然而，在WW期间，通过随访内镜图像早期客观准确地检测局部再生（LR），对于管理病情和预防远处转移至关重要。因此，我们开发了一种带有双重交叉注意力的孪生Swin Transformer（SSDCA），用于结合重新分期和随访时的纵向内镜图像，以区分cCR和LR。SSDCA利用预训练的Swin Transformer提取领域无关特征，并增强对成像变化的鲁棒性。通过实现双重交叉注意力，无需图像空间对齐即可突出两次扫描的特征以预测应答。SSDCA以及基于Swin的基线模型使用135名患者的图像对进行训练，并在62名患者的保留图像对上进行评估。SSDCA产生了最佳平衡准确率（81.76% ± 0.04）、敏感度（90.07% ± 0.08）和特异度（72.86% ± 0.05）。鲁棒性分析表明，无论是否存在血液、粪便、毛细血管扩张和图像质量差等伪影，性能均保持稳定。对提取特征进行UMAP聚类显示，SSDCA具有最大的簇间分离度（1.45 ± 0.18）和最小的簇内离散度（1.07 ± 0.19），证实了其判别性表示学习能力。