Lung adenocarcinoma is a morphologically heterogeneous disease, characterized
by five primary histologic growth patterns. The quantity of these patterns can
be related to tumor behavior and has a significant impact on patient prognosis.
In this work, we propose a novel machine learning pipeline capable of
classifying tissue tiles into one of the five patterns or as non-tumor, with an
Area Under the Receiver Operating Characteristic Curve (AUCROC) score of 0.97.
Our model's strength lies in its comprehensive consideration of cellular
spatial patterns, where it first generates cell maps from Hematoxylin and Eosin
(H&E) whole slide images (WSIs), which are then fed into a convolutional neural
network classification model. Exploiting these cell maps provides the model
with robust generalizability to new data, achieving approximately 30% higher
accuracy on unseen test-sets compared to current state of the art approaches.
The insights derived from our model can be used to predict prognosis, enhancing
patient outcomes.

本研究针对肺腺癌提出了一个新颖的机器学习流程，能够将组织细胞划分为五个主要的组织学增长模式之一或非肿瘤状态，并通过在新数据上取得约 30% 更高的准确性，提供了异常扩展性。该模型所得的见解可用于预测患者预后，改善患者预后效果。

肺腺癌生长模式整张切片图像分类的细胞分布表示

Cell Maps Representation For Lung Adenocarcinoma Growth Patterns  Classification In Whole Slide Images

The most common type of lung cancer, lung adenocarcinoma (LUAD), has been
increasingly detected since the advent of low-dose computed tomography
screening technology. In clinical practice, pre-invasive LUAD (Pre-IAs) should
only require regular follow-up care, while invasive LUAD (IAs) should receive
immediate treatment with appropriate lung cancer resection, based on the cancer
subtype. However, prior research on diagnosing LUAD has mainly focused on
classifying Pre-IAs/IAs, as techniques for distinguishing different subtypes of
IAs have been lacking. In this study, we proposed a multi-head attentional
feature fusion (MHA-FF) model for not only distinguishing IAs from Pre-IAs, but
also for distinguishing the different subtypes of IAs. To predict the subtype
of each nodule accurately, we leveraged both radiomics and deep features
extracted from computed tomography images. Furthermore, those features were
aggregated through an adaptive fusion module that can learn attention-based
discriminative features. The utility of our proposed method is demonstrated
here by means of real-world data collected from a multi-center cohort.

该研究提出了一种多头注意力特征融合（MHA-FF）模型，旨在区分早期浸润性肺腺癌（Pre-IAs）和浸润性肺腺癌（IAs）并区分不同的 IAs 亚型。通过提取计算机断层扫描图像的辐射组学和深度特征，并通过自适应融合模块汇总这些特征，我们成功地预测了每个结节的亚型，使用多中心队列的真实世界数据验证了这种方法的实用性。