Faster Results, Fewer Bottlenecks: Optimizing Pathology Workflows
Agilent Technologies
Reduce turnaround time, enhance diagnostic accuracy, and minimize manual labor in immunohistochemistry
Immunohistochemistry
Is It Time for HER2-Low Breast Cancer?
Michael Schubert, PhD
Evaluating the evidence for, and against, distinguishing between HER2-low and HER2-zero breast cancers
How Does AI Augment Predictive Cancer Research and Diagnosis?
Mateusz Tylicki, BSc
Deep learning and AI-based decisions are becoming increasingly reliable but remain far from being adapted to cancer diagnostics
Leveraging Next-Gen IHC Tools to Advance Precision Oncology Research
Swathi Kodaikal, MSc
Harnessing the power of multiplexing and RNA–protein studies could help design targeted cancer treatments
A “Whole Person” Approach to Histology
María Carla Rosales Gerpe, MSc, PhD
Pathologist and assistant professor Rebecca Waters, DO, on finding “zebras” and her osteopathic medicine background
Deep Learning Unlocks Valuable Cancer Biology Clues
Dana-Farber Cancer Institute
AI models uncover patterns across two-dimensional images of tumor samples that could help predict response to immunotherapy
Is Immunohistochemistry an Assay Or a Stain?
Steven Bogen, MD, PhD
Interlaboratory inconsistency in IHC adversely affects the quality and standard of patient care
Recombinant, Universal MHC-I Molecules May Revolutionize Therapeutics
Swathi Kodaikal, MSc
Stable and compatible with a wide variety of HLA allotypes, pMHC-I molecules are a versatile immunotherapy tool
Immunolabeling New Biomarker May Help in Early Detection of Cervical Precancerous Lesions
Hefei Institutes of Physical Science
HMGB3 could effectively label the precancerous cells at different pathological stages
Artificial Intelligence Methods May Replace Histochemical Staining
Intelligent Computing
Improving pathology workflows using a new deep learning-based virtual staining framework