Deriving interpretable prognostic features from deep-learning-based
prognostic histopathology models remains a challenge. In this study, we
developed a deep learning system (DLS) for predicting disease specific survival
for stage II and III colorectal cancer using 3,652 cases (27,300 slides). When
evaluated on two validation datasets containing 1,239 cases (9,340 slides) and
738 cases (7,140 slides) respectively, the DLS achieved a 5-year
disease-specific survival AUC of 0.70 (95%CI 0.66-0.73) and 0.69 (95%CI
0.64-0.72), and added significant predictive value to a set of 9
clinicopathologic features. To interpret the DLS, we explored the ability of
different human-interpretable features to explain the variance in DLS scores.
We observed that clinicopathologic features such as T-category, N-category, and
grade explained a small fraction of the variance in DLS scores (R2=18% in both
validation sets). Next, we generated human-interpretable histologic features by
clustering embeddings from a deep-learning based image-similarity model and
showed that they explain the majority of the variance (R2 of 73% to 80%).
Furthermore, the clustering-derived feature most strongly associated with high
DLS scores was also highly prognostic in isolation. With a distinct visual
appearance (poorly differentiated tumor cell clusters adjacent to adipose
tissue), this feature was identified by annotators with 87.0-95.5% accuracy.
Our approach can be used to explain predictions from a prognostic deep learning
model and uncover potentially-novel prognostic features that can be reliably
identified by people for future validation studies.

本研究通过开发基于深度学习的系统，在 3,652 例（27,300 张切片）的结直肠癌患者中进行预测，探索人类可解释的患病特征并阐述其潜在预测价值。通过对两个验证数据集分别包含 1,239 例（9,340 张切片）和 738 例（7,140 张切片）的评估，该系统实现了 0.70 和 0.69 的 5 年疾病特异性存活 AUC，并为一组 9 个临床病理特征提供了显著的预测价值。