CellOMaps：一种用于肺腺癌生长模式鲁棒分类的紧凑表示
CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns
AI总结：
针对肺腺癌生长模式分类主观性强、现有方法评估不足的问题，提出基于紧凑cellOMaps表示的机器学习流水线，实现五类模式及非肿瘤的分类，性能达SOTA，还可初步预测TMB水平。
摘要：
肺腺癌（LUAD）是一种形态异质性疾病，以五种主要组织学生长模式为特征。这类模式的分类至关重要，因其与预后直接相关，但高度主观性和观察者间差异构成了重大挑战。尽管已有多项研究开发了用于生长模式分类的机器学习方法，但它们要么仅报告每张切片的主要模式，要么缺乏恰当的评估。我们提出了一种可泛化的机器学习流水线，能够将肺组织分为五种模式之一或非肿瘤组织。该流水线的优势在于一种新颖的紧凑细胞组织图谱（Cell Organization Maps, cellOMaps）表示，它可从苏木精-伊红全切片图像（WSIs）中捕捉细胞空间模式。在内部未见过的切片和外部数据集上评估时，该流水线在LUAD生长模式分类任务上取得了当前最优性能，显著优于现有方法。此外，我们的初步结果表明，该模型的输出可用于预测患者的肿瘤突变负荷（TMB）水平。
英文摘要：
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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