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, 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 paired scans without requiring any spatial alignment 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. Code and weights available at: https://github.com/Jotanator/SSDCA

翻译：越来越多的证据支持接受全程新辅助治疗（TNT）后再次分期显示临床完全缓解（cCR）的直肠癌患者采用观察等待（WW）监测方案。然而，在WW期间通过随访内镜图像早期准确检测局部再生长（LR）对于管理治疗和预防远处转移至关重要。为此，我们开发了具有双交叉注意力机制的孪生Swin Transformer（SSDCA），用于结合再分期和随访时的纵向内镜图像，以区分cCR与LR。SSDCA利用预训练的Swin Transformer提取领域无关特征，增强对成像变化的鲁棒性。通过实现双交叉注意力机制，在无需任何空间配准的情况下突出配对扫描图像中的关键特征以预测应答。我们使用135例患者的图像对训练SSDCA及基于Swin的基线模型，并在62例患者的独立图像对测试集上进行评估。SSDCA取得了最佳平衡准确率（81.76% ± 0.04）、灵敏度（90.07% ± 0.08）和特异度（72.86% ± 0.05）。鲁棒性分析显示，无论存在血液、粪便、毛细血管扩张还是图像质量差的伪影，模型均保持稳定性能。对提取特征的UMAP聚类分析表明，SSDCA实现了最大簇间分离度（1.45 ± 0.18）和最小簇内离散度（1.07 ± 0.19），证实了其判别性表示学习能力。代码和权重可在https://github.com/Jotanator/SSDCA获取。