Background/Objectives: Identifying tumor budding (TB) in colorectal cancer (CRC) is vital for better prognostic assessment as it may signify the initial stage of metastasis. Despite its importance, TB detection remains challenging due to subjectivity in manual evaluations. Identifying TBs remains difficult, especially at high magnification levels, leading to inconsistencies in prognosis. To address these issues, we propose an automated system for TB classification using deep learning. Methods: We trained a deep learning model to identify TBs through weakly supervised learning by aggregating positive and negative bags from the tumor invasive front. We assessed various foundation models for feature extraction and compared their performance. Attention heatmaps generated by attention-based multi-instance learning (ABMIL) were analyzed to verify alignment with TBs, providing insights into the interpretability of the features. The dataset includes 29 WSIs for training and 70 whole slide images (WSIs) for the hold-out test set. Results: In six-fold cross-validation, Phikon-v2 achieved the highest average AUC (0.984 ± 0.003), precision (0.876 ± 0.004), and recall (0.947 ± 0.009). Phikon-v2 again achieved the highest AUC (0.979) and precision (0.980) on the external hold-out test set. Moreover, its recall rate (0.910) was still higher than that of UNI’s (0.879). UNI exhibited a balanced performance on the hold-out test set, with an AUC of 0.960 and a precision of 0.968. CtransPath showed strong precision on the external hold-out test set (0.947) but had a slightly lower recall (0.911). Conclusions: The proposed technique enhances the accuracy of TB assessment, offering potential applications for CRC and other cancer types.
背景/目的：在结直肠癌中识别肿瘤出芽对于改善预后评估至关重要，因为它可能预示着转移的初始阶段。尽管其重要性不言而喻，但由于人工评估的主观性，肿瘤出芽的检测仍然具有挑战性。尤其是在高倍放大下识别肿瘤出芽尤为困难，这导致了预后评估的不一致性。为解决这些问题，我们提出了一种基于深度学习的肿瘤出芽自动分类系统。方法：我们通过弱监督学习训练了一个深度学习模型，通过聚合肿瘤浸润前沿的阳性和阴性样本包来识别肿瘤出芽。我们评估了多种用于特征提取的基础模型并比较了它们的性能。通过分析基于注意力的多示例学习模型生成的注意力热图，验证了其与肿瘤出芽区域的一致性，从而深入了解了特征的可解释性。数据集包含29张用于训练的全视野数字切片和70张用于留出法测试集的全视野数字切片。结果：在六折交叉验证中，Phikon-v2取得了最高的平均AUC（0.984 ± 0.003）、精确率（0.876 ± 0.004）和召回率（0.947 ± 0.009）。在外部留出测试集上，Phikon-v2再次获得了最高的AUC（0.979）和精确率（0.980）。此外，其召回率（0.910）仍高于UNI模型（0.879）。UNI模型在留出测试集上表现出均衡的性能，AUC为0.960，精确率为0.968。CtransPath模型在外部留出测试集上显示出较强的精确率（0.947），但召回率略低（0.911）。结论：所提出的技术提高了肿瘤出芽评估的准确性，为结直肠癌及其他癌症类型提供了潜在的应用前景。
肿瘤(癌症)患者之家