Addressing the critical need for accurate prognostic biomarkers in cancer
treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell
lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification
in whole slide images (WSIs) is laborious and subject to variability,
potentially undermining patient outcomes. Our study introduces an automated
pipeline that utilizes semi-stochastic patch sampling, patch classification to
retain prognostically relevant patches, and cell quantification using the
HoVer-Net model to streamline the TIL evaluation process. This pipeline
efficiently excludes approximately 70% of areas not relevant for prognosis and
requires only 5% of the remaining patches to maintain prognostic accuracy
(c-index 0.65 +- 0.01). The computational efficiency achieved does not
sacrifice prognostic accuracy, as demonstrated by the TILs score's strong
correlation with patient survival, which surpasses traditional CD8 IHC scoring
methods. While the pipeline demonstrates potential for enhancing NSCLC
prognostication and personalization of treatment, comprehensive clinical
validation is still required. Future research should focus on verifying its
broader clinical utility and investigating additional biomarkers to improve
NSCLC prognosis.

我们的研究引入了一个自动化流程，该流程利用半随机补丁采样、补丁分类和 HoVer-Net 模型对肺癌患者中浸润肿瘤淋巴细胞（TILs）进行评估，从而实现对非小细胞肺癌（NSCLC）患者进行预后评估的过程的优化。该流程通过排除约 70％与预后不相关的区域，并使用仅剩下的 5％的补丁维持预后准确性（c 索引为 0.65+-0.01），在不降低预后准确性的情况下实现了计算效率，在患者存活方面与传统的 CD8 IHC 评分方法相比，该流程在 TILs 得分方面具有较强的相关性。然而，仍然需要全面的临床验证，未来的研究应着重验证其更广泛的临床实用性，并探索其他生物标志物以改善非小细胞肺癌的预后评估。

基于半随机贴片采样的肺癌 WSI 中 TILs 的快速估计

Fast TILs estimation in lung cancer WSIs based on semi-stochastic patch  sampling

Two major causes of death in the United States and worldwide are stroke and
myocardial infarction. The underlying cause of both is thrombi released from
ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the
heart (myocardial infarction) or the brain (stroke). Clinical studies show that
plaque composition plays a more important role than lesion size in plaque
rupture or erosion events. To determine the plaque composition, various cell
types in 3D cardiovascular immunofluorescent images of plaque lesions are
counted. However, counting these cells manually is expensive, time-consuming,
and prone to human error. These challenges of manual counting motivate the need
for an automated approach to localize and count the cells in images. The
purpose of this study is to develop an automatic approach to accurately detect
and count cells in 3D immunofluorescent images with minimal annotation effort.
In this study, we used a weakly supervised learning approach to train the
HoVer-Net segmentation model using point annotations to detect nuclei in
fluorescent images. The advantage of using point annotations is that they
require less effort as opposed to pixel-wise annotation. To train the HoVer-Net
model using point annotations, we adopted a popularly used cluster labeling
approach to transform point annotations into accurate binary masks of cell
nuclei. Traditionally, these approaches have generated binary masks from point
annotations, leaving a region around the object unlabeled (which is typically
ignored during model training). However, these areas may contain important
information that helps determine the boundary between cells. Therefore, we used
the entropy minimization loss function in these areas to encourage the model to
output more confident predictions on the unlabeled areas. Our comparison
studies indicate that the HoVer-Net model trained using our weakly ...

本研究旨在开发一种自动计数血管壁动脉粥样硬化斑块细胞的方法，并使用弱监督学习方法训练 HoVer-Net 模型来检测荧光图像中的细胞核，并使用熵最小化损失函数来提高该模型在周围未标记区域的预测置信度。