AI-powered pathology advances translational research by shrinking the gap from discovery to diagnosis through highly performant analytical capabilities. Here, we utilize the HALO®/HALO AI quantitative image analysis platform to compare the tumor microenvironment (TME) of patient tumor samples and corresponding tumoroids. Tumoroids created (in vitro) from primary tumor tissue (in vivo) allow researchers in the field of oncology to more accurately replicate the TME of patient biological specimens. Tumoroids compared to 2-D cell culture are believed to better mimic the physiologically relevant environment by reducing the destruction of native tissue structure and cellular composition. We perform AI-powered image and spatial analysis on multiplexed immunofluorescent tissues incorporating biomarkers for immune subtypes and key features of tumoroids such as TME, cell proliferation, immune profile. The samples have been obtained from the BioStudies BioImage Archive under the study S-BIAD1661 containing samples of clear cell Renal Cell Carcinoma (ccRCC) 1 . By applying digital pathology techniques to these research approaches, we demonstrate a thorough and versatile analysis workflow to address the characterization of tumoroids and their corresponding patient samples and highlight the impact of these tools on research, discovery and diagnostics. Greice Michele Zickuhr, Hazem Abdullah, In Hwa Um, Alexander Laird, Peter Mullen, David J. Harrison and Alison L. Dickson. "Clear Cell Renal Cell Carcinoma Patient-Derived Tumoroids characterisation by Spatial Mass Spectrometry, Histology and Multiplex Immunofluorescence." BioStudies , S-BIAD1661, 2025, www.ebi.ac.uk. Accessed 22 September 2025.