组织学信息引导的全组织切片分块可提升癌症复发与基因变异的可解释性及预测能力
Histology-informed tiling of whole tissue sections improves the interpretability and predictability of cancer relapse and genetic alterations
AI总结：
针对数字病理网格分块忽略组织结构的问题，本文提出组织学信息引导分块方法HIT，可提取腺体作为MIL输入，提升癌症相关基因变异与复发预测的性能及可解释性。
摘要：
病理学家通过评估组织学结构（如前列腺癌中的腺体）确定癌症分级，但数字病理流程通常依赖基于网格的分块方式，忽略组织结构，会引入无关信息并限制可解释性。本文提出组织学信息引导分块（HIT）方法，利用语义分割从全切片图像（WSIs）中提取腺体，作为多示例学习（MIL）和表型分析的具有生物学意义的输入块。在ProMPT队列的137个样本上训练后，HIT取得了0.83±0.17的腺体级Dice分数。通过从ICGC-C和TCGA-PRAD队列的760张全切片图像中提取38万个腺体，HIT将用于检测上皮-间质转化（EMT）相关基因及MYC基因拷贝数变异（CNVs）的多示例学习模型AUC提升了10%，并识别出15个腺体簇，其中多个簇与癌症复发、致癌突变及高Gleason评分相关。因此HIT提升了多示例学习预测的准确性与可解释性，同时通过在特征提取阶段聚焦有生物学意义的结构简化了计算流程。
英文摘要：
Histopathologists establish cancer grade by assessing histological structures, such as glands in prostate cancer. Yet, digital pathology pipelines often rely on grid-based tiling that ignores tissue architecture. This introduces irrelevant information and limits interpretability. We introduce histology-informed tiling (HIT), which uses semantic segmentation to extract glands from whole slide images (WSIs) as biologically meaningful input patches for multiple-instance learning (MIL) and phenotyping. Trained on 137 samples from the ProMPT cohort, HIT achieved a gland-level Dice score of 0.83 +/- 0.17. By extracting 380,000 glands from 760 WSIs across ICGC-C and TCGA-PRAD cohorts, HIT improved MIL models AUCs by 10% for detecting copy number variation (CNVs) in genes related to epithelial-mesenchymal transitions (EMT) and MYC, and revealed 15 gland clusters, several of which were associated with cancer relapse, oncogenic mutations, and high Gleason. Therefore, HIT improved the accuracy and interpretability of MIL predictions, while streamlining computations by focussing on biologically meaningful structures during feature extraction.
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