Recent advancements in digital pathology have enabled comprehensive analysis of Whole-Slide Images (WSI) from tissue samples, leveraging high-resolution microscopy and computational capabilities. Despite this progress, there is a lack of labeled datasets and open source pipelines specifically tailored for analysis of skin tissue. Here we propose Histo-Miner, a deep learning-based pipeline for analysis of skin WSIs and generate two datasets with labeled nuclei and tumor regions. We develop our pipeline for the analysis of patient samples of cutaneous squamous cell carcinoma (cSCC), a frequent non-melanoma skin cancer. Utilizing the two datasets, comprising 47,392 annotated cell nuclei and 144 tumor-segmented WSIs respectively, both from cSCC patients, Histo-Miner employs convolutional neural networks and vision transformers for nucleus segmentation and classification as well as tumor region segmentation. Performance of trained models positively compares to state of the art with multi-class Panoptic Quality (mPQ) of 0.569 for nucleus segmentation, macro-averaged F1 of 0.832 for nucleus classification and mean Intersection over Union (mIoU) of 0.907 for tumor region segmentation. From these predictions we generate a compact feature vector summarizing tissue morphology and cellular interactions, which can be used for various downstream tasks. Here, we use Histo-Miner to predict cSCC patient response to immunotherapy based on pre-treatment WSIs from 45 patients. Histo-Miner identifies percentages of lymphocytes, the granulocyte to lymphocyte ratio in tumor vicinity and the distances between granulocytes and plasma cells in tumors as predictive features for therapy response. This highlights the applicability of Histo-Miner to clinically relevant scenarios, providing direct interpretation of the classification and insights into the underlying biology.

翻译：数字病理学的最新进展，结合高分辨率显微成像与计算能力，使得对组织样本的全切片图像进行综合分析成为可能。尽管取得了这些进步，目前仍缺乏专门针对皮肤组织分析而构建的标记数据集与开源流程。本文提出Histo-Miner，一个基于深度学习的皮肤全切片图像分析流程，并生成了两个包含标记细胞核与肿瘤区域的数据集。我们针对皮肤鳞状细胞癌（一种常见的非黑色素瘤皮肤癌）的患者样本开发了此分析流程。利用这两个分别包含来自cSCC患者的47,392个标注细胞核和144张肿瘤分割全切片图像的数据集，Histo-Miner采用卷积神经网络和视觉Transformer进行细胞核分割与分类以及肿瘤区域分割。训练模型的性能与现有先进水平相比表现良好：细胞核分割的多类全景质量（mPQ）为0.569，细胞核分类的宏平均F1分数为0.832，肿瘤区域分割的平均交并比（mIoU）为0.907。基于这些预测结果，我们生成了一个概括组织形态与细胞间相互作用的紧凑特征向量，该向量可用于各种下游任务。本文中，我们利用Histo-Miner，基于45名患者治疗前的全切片图像，预测了cSCC患者对免疫疗法的反应。Histo-Miner识别出淋巴细胞百分比、肿瘤邻近区域粒细胞与淋巴细胞比率以及肿瘤内粒细胞与浆细胞之间的距离，作为预测治疗反应的特征。这凸显了Histo-Miner在临床相关场景中的适用性，为分类提供了直接解释，并深入揭示了潜在的生物学机制。