"Scopio's X100 scanner supports groundbreaking study in British Journal of Hematology"

#mdpijpm 🚀Radiomic and radiogenomic approaches offer valuable insights for personalized medicine by exploring relationships between imaging characteristics, genomic information, and outcomes that are often missed by qualitative interpretation. This review paper, "#Radiomics and #Radiogenomics in Preclinical #Imaging on Murine Models: A Narrative Review", aims to: 1️⃣ present the current state of the art in the application of radiomics and radiogenomics on murine models; 2️⃣ provide a brief description of relevant articles found in the literature with a discussion on the implications and potential translational relevance of these findings. 💟 Happy reading: https://lnkd.in/dPWb9Hk8 #PersonalizedMedicine #PrecisionMedicine

The sound of blood: photoacoustic imaging in blood analysis https://lnkd.in/gwSs_bVF Blood analysis is a ubiquitous and critical aspect of modern medicine. Analyzing blood samples requires invasive techniques, various testing systems, and samples are limited to relatively small volumes. Photoacoustic imaging (PAI) is a novel imaging modality that utilizes non-ionizing energy that shows promise as an alternative to current methods. This paper seeks to review current applications of PAI in blood analysis for clinical use. Furthermore, we discuss obstacles to implementation and future directions to overcome these challenges. Firstly, we discuss three applications to cellular analysis of blood: sickle cell, bacteria, and circulating tumor cell detection. We then discuss applications to the analysis of blood plasma, including glucose detection and anticoagulation quantification. As such, we hope this article will serve as inspiration for PAI's potential application in blood analysis and prompt further studies to ultimately implement PAI into clinical practice. #Photoacoustic imaging #Blood analysis #Pathogen detection #Analytes #Photoacoustic flow cytometry #Novel diagnostic technique #research #technology #innovation

Preclinical imaging, including studies of ex-vivo tissue and organoid models, play a critical role in advancing MR imaging research by providing the means to explore the biological underpinnings of disease, refine imaging techniques and uncover insights that can be incorporated into human-centered investigations. The characterisation of imaging features, structures and biomarkers, relevant to understanding disease pathways and progression, require validation solutions. PreOperative Performance has metrology phantoms, compatible with NMR and ultra-high field (UHF) imaging workflows, that can support this validation, provide quality controls that inform human-studies, and strengthen interpretability and impact. Join us at the preclinical imaging workshop session on Wednesday 8th at #ESMRMB meeting in Marseille. #microstructure #microstructuralimaging #diffusionMRI #UHF #neurorad #medicalimaging #anisotropicdiffusion #tractography #organoid

🔬 Image based Single-cell spatial transcriptomics on FFPE: a platform performance snapshot 📊 The study “Comparison of imaging based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed paraffin-embedded tumor samples”, published in Nature Communications by Nejla Ozirmak Lermi & Max Molina Ayala and cols from Cara Haymaker, Ken Chen & Luisa M. Solis Soto labs, compares imaging-based single-cell ST platforms on FFPE tumor samples. 📌 Systematic head-to-head: CosMx, MERFISH, Xenium (unimodal & multimodal) on lung adenocarcinoma and pleural mesothelioma TMAs. 📌 Coverage: CosMx required FOV selection; MERFISH/Xenium scanned whole tissue per vendor guidance. 📌 Panels: CosMx 1,000-plex; MERFISH 500-plex; Xenium 289-plex + 50 custom; 93 genes shared. 📌 Filters: CosMx removed cells <30 transcripts and overly large areas; MERFISH/Xenium removed cells <10 transcripts. ✅ If you plan to do image based spatial transcriptomics you must read it! 📚 Nature Communications paper: https://lnkd.in/dy2FR-B8 💻 code: https://lnkd.in/dBc2dmTC 📢 Join the Conversation 📢 Share your ideas, methods, and tools in the comments! 👇 💬 👉 Follow my blog for more https://lnkd.in/dvppv8uc

From Image to Insight: Linking Digital Pathology with Molecular Analysis In today’s labs, digital pathology and molecular profiling are often seen as separate domains—one focused on morphology, the other on sequencing and proteomics. But the true power emerges when these two worlds are connected. For pathologists, digital tools enable precise annotation of regions of interest. For molecular biologists, high-quality (low noise)  input is everything—DNA, RNA, and proteins that truly reflect the biology of disease. When these disciplines are linked, the result is more accurate data, clearer signals, and ultimately, better-informed decisions for patients and researchers. Bridging the digital and molecular workflows isn’t just a technical step—it’s a paradigm shift in reducing uncertainty. It means going beyond what we see to what we can measure, ensuring that every molecular readout is rooted in carefully defined histology. This is what we have been building at QuantumCyte, Inc.  (www.quantumcyte.com) As the fields of oncology and translational research move forward, this connection will be critical to reducing noise, accelerating biomarker discovery and sharpening the impact of molecular tests on patient care. The QuantumCyte team will be at Digital Pathology Association #PathologyVisions this weekend – come visit our poster and learn about how your lab can easily improve sample purity! Poster title: High-Precision Tissue Extraction: QuantumCyte integrated with Roche navify® Digital Pathology Sunday, October 5th, 5 pm to 6 pm, Seaport Ballroom G #digitalpathology #precisionmedicine #pathology #NGS

🔍 Reply: Bridging Predictive Power and Biology in Radiomics for HCC In response to Yoshiyasu Takefuji’s commentary on the challenges of biologically interpreting radiomics models, Mathew Vithayathil, Deniz Koku, Eric O. Aboagye, and Rohini Sharma (Imperial College London) offer an important perspective in the Journal of Hepatology. They emphasize that while predictive performance is key, biological relevance must not be overlooked. Their machine learning approach combined systematic model selection with ensemble learning, finding that non-linear models (neural networks, SVM, naïve Bayes) better captured complex biological relationships than linear models. Importantly, they acknowledge that feature importance (e.g., permutation importance) provides statistical insight but not definitive biological explanation. To address this gap, they highlight strategies such as: -Correlating radiomic models with biological data (e.g., transcriptomics, immunohistochemistry) to uncover mechanistic links. -Using biologically correlated features for model development, as demonstrated in NSCLC PD-L1–linked radiomics predicting immunotherapy response. -Integrating multi-omic data with deep learning (e.g., DPCCA models) to enhance predictive power and enable radiomics to serve as non-invasive surrogates of immune or genomic profiles. They conclude that future radiomics research must embed causal inference and expand beyond mathematical optimization to truly capture underlying biology — an exciting step toward biologically meaningful precision oncology. 🔗 Read the full reply: https://lnkd.in/e6uqZ-Ws #Radiomics #MachineLearning #HCC #LiverCancer #PrecisionOncology #OncologyFrontier #AIinMedicine

For decades, pathology has been defined by two opposing forces: seeing more and understanding better. Today, spatial technologies promise to reconcile both, mapping gene expression directly within the tissue, preserving cellular architecture and biological context. Yet their clinical adoption remains more of a promise than a reality. According to Lim et al. (2025, BMC Genomics), platforms like 10x Visium HD, CosMx SMI, Xenium, and Merscopedeliver unprecedented resolution, from tissue-level to subcellular, but come with a clear challenge: cost and operational scalability. 💸 With a per-sample price ranging between $3,000 and $6,000, the economic barrier remains significant, especially when the clinical return on investment is still uncertain. How can such costs be justified if we lack tailored pipelines and validated algorithms to translate these complex datasets into reproducible clinical insights? For now, their strongest impact remains within translational discovery, not yet in routine diagnostics. Still, the potential is undeniable. Spatial transcriptomics is redefining how we understand tumor microenvironments, tissue heterogeneity, and therapeutic response. But before it can cross into hospital workflows, it must prove not only analytical precision, but also clinical, economic, and operational value. 🔭 Perhaps the next step is not just to optimize technology, but to build a real dialogue between industry, hospitals, and pathology labs. One focused on developing disease-specific spatial kits with targeted panels that are more affordable, designed to identify actionable biomarkers, and capable of balancing discovery with clinical decision support. Could this be the model that finally brings spatial transcriptomics from the lab bench to the hospital floor? Lim et al. : https://lnkd.in/edPvXW98 #DigitalPathology #SpatialTranscriptomics #PrecisionMedicine #AIinHealthcare 📘 If you're just starting your journey into the world of digital pathology, here's my free ebook to help you along the way: https://lnkd.in/edcZbMxk

🔬✨ On this upcoming #WorldArthritisDay, we highlight the work of Prof. Costantino Pitzalis and Prof. Elisa Gremese, whose team is leading groundbreaking European studies to better understand #RheumatoidArthritis and improve patient care. Through major consortia such as MDR-RA, SQUEEZE, and 3TR, their research connects clinical expertise with advanced molecular science to uncover why patients respond differently to treatments. By studying the synovium, the tissue lining our joints, and applying cutting-edge imaging, transcriptomics, and metabolomics, they are identifying key cellular mechanisms that guide personalised therapies. This integrated, multidisciplinary approach is helping to bridge the gap between laboratory research and clinical application, turning scientific insight into real progress for people living with rheumatoid arthritis. Find out more 👉 https://bit.ly/4pYXgmF #HumanitasResearch #Hunimed #Rheumatology #InnovationInHealth

Radiogenomics: The Non-Invasive “Biopsy” Is Getting Real Tumor heterogeneity and Gleason upstaging still drive over- and undertreatment in GU cancer. The fix is moving from visual reads to quantitative imaging biomarkers. Radiomics uses AI to extract deep features from routine CT/MRI. Radiogenomics links those signatures to underlying biology to anticipate risk and likely response before tissue is taken. Why now • STONG PERFORMANCE: Peer-reviewed studies report high discriminatory accuracy on MRI for separating low- vs high-grade PCa, with prospective validations growing. • THERAPY GUIDANCE: Emerging work suggests imaging signatures can flag HRR/BRCA-related biology to inform PARP decisions earlier. • REGULATORY MOMENTUM: Recent FDA clearances in prostate imaging AI shift the question from if it works to how we standardize across scanners and sites. The real barrier: translation and harmonization so these tools generalize clinic-to-clinic. Join Chris Hare (Canon Medical Informatics), Ángel Alberich-Bayarri (Quibim), and Prof. Kevin Blyth (University of Glasgow) to map the operational and regulatory path. PMWC26 session: Radiomics & Radiogenomics: Harnessing Precision Imaging for Oncology Details & Track 2 registration: https://lnkd.in/gPtkWZbQ #Radiogenomics #Radiomics #ProstateCancer #OncologyAI #PrecisionMedicine #CanonMedical PMWC - Precision Medicine World Conference  Sandy Weir, Sharat Israni, Regina Barzilay, James Zou, Olivier Gevaert, Curtis Langlotz , Eliot Siegel, Katherine Andriole, Pavan Anné, Avi Veidman (Nucleai), Joe Zaccaria, Ed Kim, Joseph Lehár, Darius Meadon, Kimberly Slagle Asaf Zviran, Thomas C. Hawes, M.D., MBA, Shreyas Vasanawala OWKIN, Onc.AI, RADLogics Inc., Arterys (Acquired by Tempus AI), Lantheus, Quantib, ZS #Radiogenomics #Radiomics #ProstateCancer #MRI #OncologyAI #PrecisionMedicine #CanonMedical