用细胞空间图谱实现肺癌生长模式精准分类
CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns
- 提出细胞组织图谱（cellOMaps）压缩病理图像信息
- 在内外部数据集上准确率超现有方法
- 可辅助预测肿瘤突变负荷，适合临床研究
肺腺癌（LUAD）具有高度形态异质性，主要表现为五种组织学生长模式。其分类对预后判断至关重要，但存在主观性强、观察者差异大等问题。尽管已有机器学习方法用于生长模式分类，但多数仅报告每张切片的主导模式，或缺乏充分评估。本文提出一种可泛化的机器学习流程，能将肺组织分类为五种生长模式之一或非肿瘤组织。该流程核心是一种新型紧凑的细胞组织图谱（cellOMaps），从苏木精-伊红全片扫描图像（WSI）中捕捉细胞空间分布特征。在内部未见切片和外部数据集上的评估表明，该方法性能达到当前最优水平，显著优于现有方法。初步结果显示，模型输出可用于预测患者肿瘤突变负荷（TMB）水平。
原文摘要 · Abstract (English)
Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification of such patterns is crucial due to their direct relation to prognosis but the high subjectivity and observer variability pose a major challenge. Although several studies have developed machine learning methods for growth pattern classification, they either only report the predominant pattern per slide or lack proper evaluation. We propose a generalizable machine learning pipeline capable of classifying lung tissue into one of the five patterns or as non-tumor. The proposed pipeline's strength lies in a novel compact Cell Organization Maps (cellOMaps) representation that captures the cellular spatial patterns from Hematoxylin and Eosin whole slide images (WSIs). The proposed pipeline provides state-of-the-art performance on LUAD growth pattern classification when evaluated on both internal unseen slides and external datasets, significantly outperforming the current approaches. In addition, our preliminary results show that the model's outputs can be used to predict patients Tumor Mutational Burden (TMB) levels.
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