Digital Pathology Podcast

Aleksandra Zuraw, DVM, PhD
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Aleksandra Zuraw from Digital Pathology Place discusses digital pathology from the basic concepts to the newest developments, including image analysis and artificial intelligence. She reviews scientific literature and together with her guests discusses the current industry and research digital pathology trends.

  1. 16 СЕНТ.

    162: How Color Impacts Every Diagnosis |Color Calibration in Digital Pathology w/ Tom Kimpe (Barco) and Monika Lamba Saini

    Send us a text What if up to 35% of the diagnostic color data on your pathology slides never reaches your eyes—just because of your monitor? In this episode, sponsored by Barco, I sit down with Dr. Monika Lamba Saini (ADC Therapeutics) and Tom Kimpe (Barco) to uncover why color calibration in digital pathology isn’t optional anymore—it’s critical for diagnosis, efficiency, and AI readiness. Highlights: [00:03:42] Monika’s path from CROs to biopharma and why color consistency matters in clinical trials.[00:09:22] What “color science” means in pathology and why color is one-third of diagnosis.[00:12:40] When the same tissue looks different across labs and scanners—and how this causes diagnostic conflicts.[00:16:19] Why HER2 scoring and IHC rely on color intensity—and how poor color fidelity lowers diagnostic confidence.[00:18:34] Research showing up to 35% of H&E slide colors fall outside of the sRGB color space—meaning you never see them on a standard monitor.[00:22:23] Where the biggest sources of color variability occur across the imaging chain come from.[00:26:26] Calibrated displays and pathologist speed—why confidence = faster reads.[00:35:19] How monitors degrade over time and why calibration is essential.[00:41:27] Why choosing a monitor based on price is short-sighted—and the real ROI of medical-grade displays.[00:43:45] ICC profiles explained: the missing piece in end-to-end color consistency.[00:52:48] Training pathologists on color literacy and internal calibration strategies.[01:00:10] How color variability affects AI algorithm accuracy—up to a 30% drop if scanners differ.[01:14:57] The role of professional societies in building color literacy and regulatory guidance.[01:22:30] Final takeaways: if you’re skeptical about calibration, here’s why you should care. Resources from this Episode FDA Research by Cheng – H&E slide colors beyond sRGB Reproducible Color Gamut of Hematoxylin and Eosin Stained Images in Standard Color Space. Barco White Paper – The Importance of Color in Modern Pathology.Barco eBook – Digital Pathology: What Are The BenefitsBarco MDPC-8127 Monitor – Medical-grade display optimized for pathology. Digital Pathology 101 (by me, Dr. Aleksandra Zuraw) – Free PDF & Amazon print edition. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

    1 ч. 26 мин.

  2. 16 СЕНТ.

    161: 7 Secrets to Smarter AI in Cancer Care | Lessons from NCCN Summit

    Send us a text 7 Counterintuitive Secrets from NCCN’s 2025 AI in Cancer Care Summit When the National Comprehensive Cancer Network (NCCN) gathers healthcare leaders, people listen. I attended the 2025 Policy Summit on the evolving AI landscape in cancer care—and walked away with insights that were raw, practical, and surprisingly hopeful. Instead of hype or overpromising, cancer care leaders shared honest strategies for implementing AI responsibly and effectively. In this episode, I break down the 7 counterintuitive secrets they’re using to fast-track adoption—while others remain stuck. Whether you’re in digital pathology, oncology, or healthcare AI, these lessons matter for your projects. KEY HIGHLIGHTS 0:04 – Reporting from Washington DC: what the NCCN AI Policy Summit revealed about the real state of AI in cancer care.1:10 – Why NCCN guidelines shape cancer care worldwide.1:36 – Even top cancer centers struggle with AI implementation—why delays and budget overruns are common.3:16 – Secret #1: Stop chasing perfect AI tools—build strategic guardrail frameworks instead.6:20 – Secret #2: Plan for biological drift from day one.9:29 – Secret #3: Target underutilized care areas, not your strongest programs.12:07 – Secret #4: Design AI for patients receiving care, not just providers giving it.16:29 – Secret #5: Follow the pioneers—don’t reinvent from scratch.19:09 – Secret #6: Build flexible systems for evolving regulatory pathways.22:09 – Secret #7: Stop using human-level performance as the gold standard.31:23 – Why integration is now as important as innovation in AI for pathology.34:31 – What’s next: NCCN will publish a report based on these discussions.THIS EPISODE'S RESOURCES NCCN – National Comprehensive Cancer NetworkEpisode with Dr. Lija Joseph on patient-pathologist communicationAeffner F. et al. – The Gold Standard Paradox in Digital Image Analysis: Manual vs Automated Scoring as Ground TruthArtera AI FDA de novo authorization news (August 2025)Maryland AI Regulation (effective October 1, 2025)If this episode resonated with you, please share it with colleagues. Speaking the same language around digital pathology and AI implementation will help us all move forward. 🎧 Thank you for trailblazing with me. Until next time, keep trailblazing however you can. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

    36 мин.

  3. 31 АВГ.

    160: AI in Medicine: Neuropathology, Renal Disease, Hematology & Cytology

    Send us a text What if the way we quantify pathology is more guesswork than science? In this episode of DigiPath Digest, I take you through the latest research where AI is not just supporting but challenging traditional methods of image analysis in neuropathology, nephrology, hematology, and cytology. From Boston brain banks to Mayo Clinic kidney models, we look at how advanced AI compares to human vision—and where it already outperforms us. Episode Highlights: [00:02:49] Neuropathology image analysis (Boston VA & BU) – Why traditional semiquantitative scoring often fails, and how AI-based density quantification reveals more subtle pathology in CTE.[00:13:16] Chronic kidney changes with AI (Mayo Clinic, Cambridge, Emory, Geneva) – A 20-class AI model trained on 20,500 annotations, showing how multiclass segmentation outperforms human guesswork in renal pathology.[00:21:09] Digital hematology review (University of Pennsylvania) – Current hurdles in AI for blood and bone marrow evaluation: regulatory oversight, data standardization, and resistance to change.[00:25:52] AI in cytology review (Journal of Cytopathology) – From BD FocalPoint to deep learning: two decades of digital cytology, stagnation, and why adoption still lags despite proven benefits.[00:32:09] Neuropathology goes digital – Where digital neuropathology is already routine (Ohio State, Mayo Clinic, Leeds, Granada) and why this specialty is crucial for pushing adoption.[00:34:19] Personal note – Why I believe learning, sharing, and experimenting with AI tools now will shape the way we practice pathology tomorrow.Resources from this Episode Comparison of quantitative strategies in neuropathologic image analysis – Boston VA / BU Brain Bank study.Multiclass AI model for chronic kidney changes – Mayo Clinic, Cambridge, Emory, Georgia Tech, Geneva collaboration.Review: Digital hematology in the AI era – International Journal of Laboratory Hematology.Review: AI and machine learning in cytology – Journal of the American Society of Cytopathology.Digital Pathology 101 (by me, Dr. Aleksandra Zuraw) – Free PDF & Amazon print edition.Pathology AI Makeover Course – Practical training for AI in pathology workflows. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

    25 мин.

  4. 30 АВГ.

    159: What If Your AI Tool Is Lying: Hidden Bias in Pathology Algorithms

    Send us a text What if the AI tools we trust for cancer diagnosis are not always correct? This episode of DigiPath Digest takes on the uncomfortable but critical question: can AI “lie” to us—and how do we verify its performance before adopting it in clinical practice? Highlights: [00:02:00] Foundation models in action: Deployment of a fine-tuned pathology foundation model for EGFR biomarker detection in lung cancer—reducing the need for rapid molecular tests by 43%.[00:08:41] Bone marrow AI misclassifications: Why automated digital morphology still struggles with consistency across leukemia and lymphoma cases.[00:14:45] Lossy DICOM conversion: How file format changes can subtly—but significantly—affect AI model performance.[00:21:45] Federated tumor segmentation challenge: Coordinating 32 international institutions to benchmark healthcare AI fairly across diverse datasets.[00:27:47] AI in gynecologic cytology: Reviewing AI-driven Pap smear screening—promise, limitations, and why rigorous validation remains essential.[00:32:27] Takeaway: Trust but verify—AI tools must be validated before they can support or replace clinical decisions.Resources from this Episode Nature Medicine – Fine-tuned pathology foundation model for lung cancer EGFR biomarker detection.Scientific Reports (Germany) – Study on how DICOM conversion impacts AI performance in digital pathology.Federated Tumor Segmentation Challenge – Benchmarking AI across 32 global institutions.Acta Cytologica – Review on AI in gynecologic cytology and Pap smear screening.Support the show Get the "Digital Pathology 101" FREE E-book and join us!

    28 мин.

  5. 29 АВГ.

    158: Multimodal Magic AI’s Role in Lung & Prostate Cancer Predictions

    Send us a text What if AI could predict cancer outcomes better than traditional methods—and at a fraction of the cost? In this episode, I explore how multimodal AI is reshaping lung and prostate cancer predictions and why integration challenges still stand in the way. Episode Highlights with Timestamps: [00:02:57] Agentic AI in toxicologic pathology – what it is and how it could orchestrate workflows.[00:05:40] Grandium desktop scanners – making histology studies more accessible and efficient.[00:08:03] Clover framework – a cost-effective multimodal model combining vision + language for pathology.[00:13:40] NSCLC study (Beijing Chest Hospital) – AI predicts progression-free and overall survival with high accuracy.[00:17:58] Prostate cancer prognostic model (Cleveland Clinic & US partners) – validating AI-enabled Pathomic PRA test.[00:23:35] Thyroid neoplasm classification – challenges for AI in distinguishing overlapping histopathological features.[00:34:49] Real-world Belgium case study – AI integration into prostate biopsy workflow reduced IHC testing and turnaround time.[00:41:03] Lessons learned – adoption hurdles, system integration, and why change management is essential for successful digital transformation.Resources from this Episode World Tumor Registry – A global open-access repository for histopathology images: World Tumor RegistryBeijing Chest Hospital NSCLC AI Prognostic Study – Prognosis prediction using multimodal models.Cleveland Clinic Pathomic PRA Study – Independent validation of AI-enabled prostate cancer risk assessment.Grandium Scanners – Compact desktop scanners for histology slides: Grandium.aiSupport the show Get the "Digital Pathology 101" FREE E-book and join us!

    29 мин.

  6. 22 АВГ.

    157: How Academic Pathology Programs Can Prepare for AI | UPMC Podcast

    Send us a text “AI in Pathology Isn’t Coming — It’s Already Here. Are You Ready?” From confusion to clarity — that’s what this episode is all about. I sat down with Drs. Liron Pantanowitz, Hooman Rashidi, and Matthew Hanna to dissect one of the most important and comprehensive AI-in-pathology resources ever created: the 7-part Modern Pathology series from UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE). This isn’t just another opinion piece — it's your complete guide to understanding, implementing, and navigating AI in pathology with real-world insights and a global lens. Together, we discuss: Why pathologists and computer scientists are often lost in translation How AI bias, regulation, and ethics are being addressed — globally What it really takes to operationalize AI in patient care today If you’ve ever asked, “Where do I even start with AI in pathology?” — this is your answer. 🔍 Highlights & Timestamps 00:00 – The importance of earned trust in AI 01:00 – Education gaps in AI for both pathologists & developers 03:00 – Why CPAiCE was built & the three missions it serves 07:00 – The seven-part series: a blueprint for AI literacy 10:00 – Making AI education accessible without losing technical integrity 13:00 – How this series is being used for global teaching (including by me!) 17:00 – Generative AI in creating figures vs. human-authored content 21:00 – Eye-opening global AI regulations that pathologists MUST know 24:00 – Ethics, bias & strategies to mitigate real clinical risks 30:00 – What’s next: CPAiCE’s mission to reshape pathology education & practice 34:00 – A teaser: the first CPAiCE textbook is on the way! 📚 Resources from This Episode 📰 Read the full series (open access!):  Modern Pathology 7-Part AI Series: https://www.modernpathology.org/article/S0893-3952(25)00001-8/fulltext 👨‍⚕️ UPMC’s Computational Pathology & AI Center of Excellence (CPAiCE)  🌍 Creative Commons licensing means YOU can reuse, remix & teach from these resources — just cite the source. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

    39 мин.

  7. 21 АВГ.

    156: Digital Pathology and AI in Cancer Grading, T-Cell Imaging & Biomarkers

    Send us a text Can AI Grade Cancer Better Than Us? The Truth About T-Cell Imaging, Biomarkers & Digital Pathology Disruption You think Saturday mornings are for coffee? Try diving into bone marrow morphology, organ donor kidney biopsies, and AI-driven metastasis detection at sunrise. That’s how I do it—and you’re invited to join. Welcome to another data-packed episode of DigiPath Digest, where we explore the latest frontier in digital pathology and AI. This time, I reviewed some of the most exciting recent abstracts spanning cancer grading, T-cell quantification, and AI agents in oncology decision-making. These studies aren’t just fascinating—they’re redefining what’s possible in diagnostics, especially in under-resourced areas where digital pathology can create game-changing access and efficiency. 🔬 Highlights with Timestamps [00:04:00] Detecting Metastases with Vision Transformers A team from Leeds Teaching Hospital developed a model for identifying lymph node and omental metastases in ovarian and peritoneal cancers with 99.8% AUROC and 100% balanced accuracy—this isn’t hype; it’s real AI pre-screening that could reduce diagnostic strain on pathologists. [00:08:00] DeepHeme: Bone Marrow Smears Meet AI UCSF and Memorial Sloan Kettering collaborated on DeepHeme, an ensemble deep learning model that classifies bone marrow aspirate cells with expert-level accuracy. With over 30K training images and strong external validation, it outperforms humans in both speed and detail. [00:16:00] Multimodal AI for Head & Neck Cancer This review showcases how integrating radiology, histopathology, and genomics with AI enhances personalized treatment and prognosis. Spoiler alert: Multimodal > unimodal. [00:24:00] Real-Time Kidney Biopsy Evaluation via AI Shoutout to our Digital Pathology Place sponsor, Techcyte, for their AI-powered tool improving accuracy and halving the time it takes to evaluate frozen kidney biopsies. This is the kind of innovation we need in organ transplantation. [00:32:00] GPT-4 as an Oncology Agent? Heidelberg researchers created an autonomous AI agent using GPT-4 plus vision models and OncoKB to handle oncology case decisions with 91% accuracy. This isn’t ChatGPT guessing—it’s a hybrid system citing guidelines and performing complex reasoning. 🧠 Resources From This Episode 📰 Multiple Instance Learning for Metastases Detection in Ovarian Cancer – Cancers journal🧬 DeepHeme: Generalizable Bone Marrow Cell Classifier – Science Translational Medicine📚 AI in Head and Neck Cancer: A Multimodal Review – Cancers journal🧪 AI-Assisted Review of Donor Kidney Pathology – Techcyte & Digital Pathology Place demo🤖 Autonomous AI Agent for Oncology Decisions – Heidelberg Group🎙️ Podcast on GPT-4 agents with Dr. Nina Kolker🧵 Earrings mentioned in the livestream? Find them in the DPP Store I’d love to hear your feedback, your projects, and what digital pathology means to you. You can always reach out through comments, LinkedIn, or email. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

    35 мин.

  8. 20 АВГ.

    155: AI Pathology & Genomics_ A New Benchmark for Predicting Gene Mutations

    Send us a text AI Pathology & Genomics: A New Benchmark for Predicting Gene Mutations If you still think visual quantification is “good enough” in pathology, think again. In this 27th episode of DigiPath Digest, I break down four transformative abstracts that show how AI is shifting our diagnostic landscape—from breast cancer segmentation to fibrosis assessment, and all the way to spatial immunology and the evolving immunoscore. If you’re still relying on manual scoring, static staging systems, or single-marker immunohistochemistry, this episode will challenge you to look deeper—literally and algorithmically. 🔬 Episode Highlights & Timestamps [02:00] Abstract 1 – AI + IHC for epithelial cell segmentation in breast cancer [07:30] Abstract 2 – Deep learning quantifies TILs in esophageal cancer [14:30] Abstract 3 – Biopsy size impacts SHGTPF-based liver fibrosis staging [22:30] Abstract 4 – Immunoscore in colorectal cancer: promise & limits 🧬 Key Insights & Takeaways 1. IHC-Guided Segmentation for Breast Cancer Using immunohistochemistry as a ground truth for AI segmentation reveals how effective our models can be—but also where they fall short. The challenge? Accurately subclassifying benign, in situ, and invasive epithelial cells. Spoiler: We’re not quite there yet. 2. Tumor-Infiltrating Lymphocytes in Esophageal SCC A Chinese team trained deep learning algorithms to analyze TILs spatially. Result? High TIL counts in both intra- and peritumoral zones correlated with better survival—highlighting the emerging power of spatial immunology. 3. Liver Fibrosis Staging with SHGTPF Microscopy Second harmonic generation two-photon microscopy gives us label-free imaging of unstained tissue. The takeaway: bigger biopsies (20–26mm) yield better fibrosis quantification. Biopsy position? Surprisingly irrelevant. A game-changer for MASLD diagnostics. 4. Immunoscore for Colorectal Cancer This image analysis-based tool outperforms traditional TNM staging, helping stratify patients for immunotherapy. But adoption is hampered by cost and digital slide access. Integrating AI could take it to the next level—something we should all watch closely. 🎓 Resources from This Episode Breast cancer segmentation using IHC-guided AI (Trondheim, Norway)Esophageal SCC & spatial TILs (Cancer Medicine, China)SHGTPF microscopy in liver fibrosis (UK/US multi-center study)Immunoscore in colorectal cancer (Jerome Galon group origins)💡 Bonus: I show off some histology-inspired earrings and talk about the story behind them—multinucleated giant cells, cartilage, and more. Check them out if you’re into pathology fashion! We’re not just validating AI anymore—we're redefining diagnostics. From high-res, label-free imaging to robust spatial biology insights, the path forward in pathology is clearer and more precise than ever. Whether you’re a practicing pathologist, researcher, or innovator, this episode offers tools and perspectives you can apply today. Support the show Get the "Digital Pathology 101" FREE E-book and join us!

    23 мин.
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Aleksandra Zuraw from Digital Pathology Place discusses digital pathology from the basic concepts to the newest developments, including image analysis and artificial intelligence. She reviews scientific literature and together with her guests discusses the current industry and research digital pathology trends.

Информация

  • Автор
    Aleksandra Zuraw, DVM, PhD
  • Годы выхода
    2019 - 2025
  • Выпуски
    162
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © 2025 Digital Pathology Podcast
  • Сайт подкаста
    Digital Pathology Podcast

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