Abstract Introduction: Prostate cancer (PCa) risk stratification, to precisely identify patients at greatest risk of progression to metastatic disease remains challenging. Integrating clinical data, multiparametric MRI (mpMRI) radiomics, and spatial transcriptomics (ST) may enable identification of imaging features linked to genomic signatures of aggressive PCa. Methods: Organ-wide spatial transcriptomics (VisiumTM v2, 10x Genomics) was performed on formalin-fixed paraffin-embedded prostatectomy sections and metastatic lymph nodes from patients recruited to a trial (ISRCTN10046036). A patient with Gleason 4+4 PCa and pre-operative mpMRI was included. This is a sub-study, part of a larger project mapping prostate cancer clonal dynamics in ten men using inferred clonal genomic analyses (SpatialInferCNV). Image processing involved anatomical segmentation and qualitative correlation with radiologist and pathologists. Image registration was performed using the ProsRegNet deep learning pipeline. Multiple ST sections were mapped to a common coordinate framework using SpatialStitcher (locally developed). Radiomic features were extracted using PyRadiomics. Results: Eight ST sections representing over 85000 ST spots (55µm diameter) were mapped to T2-axial MRI. Image registration achieved a DICE similarity coefficient of 0.861 for prostate capsule. The landmark deviation for urethra and BPH nodule were 2.17mm and 2.62mm respectively. A total of 93 radiomic features were selected. First-order radiomics significantly correlated with ST spot-level histological annotation according benign vs Gleason grade group (GG2, GG3 and GG5) (p0.001). Inferred clonal phylogenomic analysis revealed a region on the axial sections (X clone) with high concordance with lymph node metastasis. This region, harboured a distinct z-normalised textural (features: GLSZM, GLCM, GLDM, GLRLM, NGTDM) radiomic score compared to other tumour and benign regions (p0.001). No difference in first order radiomics (visible to the eye) was observed between the X clone and other tumour clones (p = 0.21). Conclusion: We show a distinct textural radiomic profile within the prostate that harbours a phylogenetic correlation with lymph node metastases. Further radio-spatial genomic profiling of the cohort and validation analyses are underway. These could offer insights into improving current MRI-based risk stratification for follow-up and lesion targeting for biopsy or focal therapy. Citation Format: Thineskrishna Anbarasan, Sandy Figiel, Max Beesley, Wencheng Yin, Ruth McPherson, Richard Colling, Freddie Hamdy, Richard J. Bryant, Bartlomiej Papiez, Alastair D. Lamb, Ian Mills. Integrating spatial transcriptomics with MRI reveals distinct radio-spatial genomic profile concordant with prostate cancer clonal heterogeneity abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2784.
Anbarasan et al. (2026) studied this question.