Histology-informed microstructural diffusion simulations for MRI cancer characterisation (Histo-µSim): histology substrates

The Histo-μSim diffusion MRI technique: histology substrates This data set contains the histological data used to build the Histo-μSim techniquefor the non-invasive characterisation of cancer properties through diffusion MRI. The technique and the methods followed to acquire and process the histological datacontained in this data set can be found in our preprint: "Histology-informed microstructural diffusion simulations for MRI cancer characterisation - the Histo-microSim framework"Athanasios Grigoriou, Carlos Macarro, Marco Palombo, Daniel Navarro-Garcia, Anna Voronova, Kinga Bernatowicz, Ignasi Barba, Alba Escriche, Emanuela Greco, Maria Abad, Sara Simonetti, Garazi Serna, Richard Mast, Xavier Merino, Nuria Roson, Manuel Escobar, Maria Vieito, Paolo Nuciforo, Rodrigo Toledo, Elena Garralda, Roser Sala-Llonch, Els Fieremans, Dmitry S. Novikov, Raquel Perez-Lopez and Francesco Grussu. medrxiv 2024, DOI: 10.1101/2024.07.15.24310280.https://doi.org/10.1101/2024.07.15.24310280Data descriptionThis data set contains 18 cellular environments reconstructed from hemaotxylin-eosin (HE) histology, referred to as "substrates". For each substrate, we store the corresponding data in a folder (sub1, sub2, ...). The substrates match those reported in table 1 of our preprint (https://doi.org/10.1101/2024.07.15.24310280). Unzip the compressed file HistouSim_substrates_v1.zip. This will extract the folder HistouSim_substrates_v1,containing version1 of the histological substrates used to build the Histo-microSim technique. Inside HistouSim_substrates_v1, you will find sub-folders sub1, sub2, ... subn, ... sub18. Each of these contains:- dims.txt: dimensions of the substrate (width and height, in μm). Note that the dimensions refer to the WHOLE histological image, even if cells and other structures have been outlined only in a sub-region of the image. - info.txt: primary cancer type (if non-cancerous, it specifies that it corresponds to liver tissue) and structures being segmented, with corresponding colourb (e.g., green: cells; etc)- subn.svg: the HE image with the outline structures, in different colours (cells, lumen, fat, vessels; examples: sub1.svg within sub1, sub5.svg within sub5, etc)AcknowledgementsVHIO would like to acknowledge: the State Agency for Research (Agencia Estatal de Investigacion) for the financial support as a Center of Excellence Severo Ochoa (CEX2020-001024-S / AEI / 10.13039 / 501100011033), the Cellex Foundation for providing research facilities and equipment and the CERCA Programme from the Generalitat de Catalunya for their support on this research. This study has been funded by Instituto de Salud Carlos III (ISCIII) through the project "PI21/01019" and co-funded by the European Union. Part of the data acquisitionhas been supported by PREdICT, sponsored by AstraZeneca. R.P.L is supported by the "la Caixa" Foundation CaixaResearch Advanced Oncology Research Program, the Prostate Cancer Foundation (18YOUN19), a CRIS Foundation Talent Award (TALENT19-05), the FERO Foundation through the XVIII Fero Fellowship for Oncological Research, the Instituto de Salud Carlos III-Investigacion en Salud (PI18/01395 and PI21/01019), the Asociacion Espanola Contra el Cancer (AECC) (PRYCO211023SERR) and the Generalitat de Catalunya Agency for Management of University and Research Grants of Catalonia (AGAUR) (2023PROD00178). The project that gave rise to these results received the support of a fellowship from "la Caixa" Foundation (ID 100010434). The fellowship code is "LCF/BQ/PR22/11920010" (funding F.G.). A.G. is supported by a Severo Ochoa PhD fellowship (PRE2022-102586). C.M. is funded by the Asociacion Espanola Contra el Cancer (AECC) (PRYCO211023SERR).# Histo-μSim扩散磁共振成像（diffusion MRI）技术：组织学底物 本数据集包含用于构建Histo-μSim技术的组织学数据，该技术通过扩散磁共振成像实现癌症特性的无创表征。 本数据集所包含的组织学数据的采集与处理方法，以及该技术的相关细节，均可在我们的预印本中查阅： > 《面向磁共振成像癌症表征的组织学引导微结构扩散模拟——Histo-microSim框架》 > 作者：Athanasios Grigoriou、Carlos Macarro、Marco Palombo、Daniel Navarro-Garcia、Anna Voronova、Kinga Bernatowicz、Ignasi Barba、Alba Escriche、Emanuela Greco、Maria Abad、Sara Simonetti、Garazi Serna、Richard Mast、Xavier Merino、Nuria Roson、Manuel Escobar、Maria Vieito、Paolo Nuciforo、Rodrigo Toledo、Elena Garralda、Roser Sala-Llonch、Els Fieremans、Dmitry S. Novikov、Raquel Perez-Lopez、Francesco Grussu > 发表于medRxiv，2024年，DOI: 10.1101/2024.07.15.24310280，链接：https://doi.org/10.1101/2024.07.15.24310280 ## 数据集说明 本数据集包含通过苏木精-伊红（HE）染色组织学图像重建的18种细胞微环境，这些微环境被称为“底物”。我们为每个底物在对应的文件夹（sub1、sub2……）中存储相关数据。本数据集的底物与预印本表1中所列的底物完全一致，预印本链接：https://doi.org/10.1101/2024.07.15.24310280。 请解压压缩文件HistouSim_substrates_v1.zip，解压后将得到HistouSim_substrates_v1文件夹，其中包含用于构建Histo-microSim技术的第1版组织学底物数据。 在HistouSim_substrates_v1文件夹内，您将找到sub1、sub2……subn……sub18共18个子文件夹。每个子文件夹均包含以下文件： - dims.txt：底物的尺寸信息（宽度与高度，单位为微米（μm））。请注意，此处的尺寸对应完整的组织学图像，即便仅在图像的子区域内勾勒出细胞与其他结构。 - info.txt：主要癌症类型（若为非癌组织，则标注为肝组织）、已分割的结构类型及其对应的配色（例如：绿色代表细胞等）。 - subn.svg：带有不同颜色标注结构的HE染色图像（标注结构包括细胞、管腔、脂肪、血管等；示例：sub1文件夹内的sub1.svg、sub5文件夹内的sub5.svg等）。 ## 致谢 瓦尔德希伯伦肿瘤研究所（VHIO）谨此致谢：西班牙国家研究署（Agencia Estatal de Investigacion）以Severo Ochoa卓越中心项目（CEX2020-001024-S / AEI / 10.13039 / 501100011033）提供的经费支持、Cellex基金会提供的研究设施与设备，以及加泰罗尼亚政府CERCA计划对本研究的支持。 本研究由西班牙国家卫生研究院（ISCIII）通过项目“PI21/01019”资助，并由欧盟联合资助。部分数据采集工作得到了阿斯利康（AstraZeneca）赞助的PREdICT项目支持。 Raquel Perez-Lopez得到了“la Caixa”基金会CaixaResearch高级肿瘤学研究项目、前列腺癌基金会（18YOUN19）、CRIS基金会人才奖（TALENT19-05）、FERO基金会第十八届Fero肿瘤研究奖学金、西班牙国家卫生研究院-健康研究项目（PI18/01395与PI21/01019）、西班牙癌症防治协会（AECC）（PRYCO211023SERR）以及加泰罗尼亚大学与研究资助管理局（AGAUR）（2023PROD00178）的支持。 产生本研究成果的项目得到了“la Caixa”基金会奖学金（ID 100010434）的支持，奖学金编号为LCF/BQ/PR22/11920010（资助Francesco Grussu）。Athanasios Grigoriou得到了Severo Ochoa博士生奖学金（PRE2022-102586）的支持。Carlos Macarro得到了西班牙癌症防治协会（AECC）（PRYCO211023SERR）的资助。