3D pathology of prostate biopsies with biochemical recurrence outcomes: raw H&E-analog datasets and image translation-assisted segmentation in 3D (ITAS3D) datasets

<p>This collection provides public access to a 3D pathology dataset of prostate cancer, allowing researchers to further investigate various 3D tissue structures and their correlation with prostate cancer patient outcomes (biochemical recurrence). These 3D tissue structures are revealed through: (1) a H&E-analog stain, (2) synthetically generated immunofluorescence staining of CK8 (targeting the luminal epithelial cells of all prostate glands), and (3) 3D segmentation masks of the gland lumen, epithelium, and stromal regions of prostate biopsies.  This data collection will promote research in the field of computational 3D pathology for clinical decision support.</p><p>In this TCIA collection, we provide the 2x down-sampled fused OTLS-imaged images (H&E-analog staining), the synthetic cytokeratin-8 (CK8) immunofluorescent images at 2x-downsampled resolution, the 3D semantic segmentation masks of glands at 4x down-sampled resolution, the clinical data for patient outcomes (biochemical recurrence), and the coordinates for the cancer-enriched regions of each biopsy. All datasets are from the 50 patient cases studied in this publication: [<a href="https://pubmed.ncbi.nlm.nih.gov/34853071/"><em>W. Xie et al., Cancer Research</em>, 2022</a>].  The Python code for the deep-learning models, and for 3D glandular segmentations based on synthetic-CK8 datasets, are available on GitHub at <a href="https://github.com/WeisiX/ITAS3D">https://github.com/WeisiX/ITAS3D</a>.</p><p>Note that the 3D pathology datasets provided in this collection were generated in Dr. Jonathan Liu’s lab at the University of Washington with a custom open-top light-sheet (OTLS) microscope developed by the lab [<a href="https://pubmed.ncbi.nlm.nih.gov/31273194/">A.K. Glaser et al., <em>Nature Communications</em>, 2019</a>].  There is no clinical metadata within the imaging files and all patients are referred to with coded identifiers.  All of the clinical outcomes data provided in this collection have already been published within the supplement of [<a href="https://pubmed.ncbi.nlm.nih.gov/34853071/"><em>W. Xie et al., Cancer Research</em>, 2022</a>].</p>

本数据集向公众开放了前列腺癌的3D病理学数据集，使研究人员能够进一步探究多种3D组织结构与前列腺癌患者预后（生化复发）之间的关联。这些3D组织结构通过以下方式呈现：（1）H&E类似染色，（2）合成的CK8（针对所有前列腺腺体的管腔上皮细胞）免疫荧光染色，（3）前列腺活检的腺体管腔、上皮和间质区域的3D分割掩膜。此数据集将推动计算3D病理学领域的研究，以支持临床决策。在本TCIA数据集中，我们提供了2倍下采样的融合OTLS图像（H&E类似染色），2倍下采样分辨率的合成细胞角蛋白-8（CK8）免疫荧光图像，4倍下采样分辨率的腺体3D语义分割掩膜，患者预后（生化复发）的临床数据，以及每个活检样本中富含癌症区域的坐标。所有数据集均来自本研究中分析的50个病例：[见参考文献W. Xie等，《癌症研究》，2022]。深度学习模型的Python代码以及基于合成CK8数据集的3D腺体分割代码可在GitHub上获取：[https://github.com/WeisiX/ITAS3D]。请注意，本集中提供的3D病理学数据集是在华盛顿大学乔纳森·刘博士的实验室中生成的，该实验室拥有实验室自行开发的定制开放式顶盖光片（OTLS）显微镜[见参考文献A.K. Glaser等，《自然通讯》，2019]。影像文件中不包含临床元数据，所有患者均以编码标识符称呼。本集中提供的所有临床预后数据已在[见参考文献W. Xie等，《癌症研究》，2022]的补充材料中发表。