Deep learning creates virtual multiplexed immunostaining to improve cancer diagnosis

LOS ANGELES - Amzeal -- Researchers at the University of California, Los Angeles (UCLA), in collaboration with pathologists from Hadassah Hebrew University Medical Center and the University of Southern California, have developed a deep learning–based method that can digitally generate multiple immunohistochemical stains from a single, unstained tissue section. The approach enables accurate assessment of vascular invasion—a key indicator of cancer aggressiveness—without the need for conventional chemical staining procedures.

The study introduces a virtual multiplexed immunostaining (mIHC) framework that transforms autofluorescence microscopy images of label-free tissue into brightfield-equivalent images of hematoxylin and eosin (H&E) staining as well as two clinically important immunohistochemical markers: ERG, which labels endothelial cells, and PanCK, which highlights epithelial tumor cells. These virtual stains are generated simultaneously on the same physical tissue section using a single deep neural network.

More on Amzeal News

• Chicago's MATTER and Dr. S. Yin Ho to Explore the Future of Responsible AI in Healthcare on February 12th
• Unseasonable Warmth Triggers Early Pest Season Along I-5 Corridor
• Blue Fox Group Reinforces the Value of Strategic IT Guidance Within Managed IT Services
• JZ Electric Celebrates Over a Decade of Trusted Electrical and Mechanical Services in Vancouver
• Bug Busters Expands Service Footprint With New Carrollton, Georgia Branch

Vascular invasion—defined as the presence of tumor cells within blood or lymphatic vessels—is a critical prognostic factor across many solid tumors, including thyroid cancer. However, its identification in routine pathology can be challenging. Traditional workflows rely on H&E slides followed by immunohistochemical staining on serial tissue sections, a process that is labor-intensive, costly, and prone to tissue loss and section-to-section variability.

The method is based on imaging unstained tissue using autofluorescence microscopy, followed by computational transformation using a conditional generative adversarial network. A digital staining matrix guides the network to generate different virtual stains from the same input image, ensuring precise spatial alignment between H&E and immunohistochemical outputs.

Researchers applied the virtual mIHC framework to thyroid tissue microarrays and compared the digitally generated stains with conventional histochemical staining. Board-certified pathologists evaluated the images in a blinded study and found high concordance between virtual and traditional stains, with virtual staining often demonstrating superior consistency and specificity.

More on Amzeal News

• R U Next? When Technologies/Verification Fails -- Stranded for 8 Days
• ShotTracker Adds Univ. of Central Florida Women's Basketball To Its 2026 Roster of Partner Schools
• Why KULR Could Be a Quiet Enabler of Space-Based Solar Power (SBSP) Over The Long Term: KULR Technology Group, Inc. (NY SE American: KULR)
• Why Finland Had No Choice But to Legalize Online Gambling
• High-Margin Energy & Digital Infrastructure Platform Created after Merger with Established BlockFuel Energy, Innovation Beverage Group (NAS DAQ: IBG)

Because the stains are generated computationally, the results are highly reproducible and free from many of the artifacts seen in conventional immunohistochemistry, said Nir Pillar, a pathologist at Hadassah Hebrew University Medical Center and a corresponding author of the study. This has the potential to significantly improve diagnostic accuracy while reducing cost and turnaround time.

The virtual multiplexed immunostaining framework is compatible with existing digital pathology workflows and requires only a single tissue section. After training, the system can generate virtual stains in seconds for individual fields of view and in minutes for whole-slide images, making it suitable for high-throughput clinical environments.

The study was conducted by Yijie Zhang, Çağatay Işıl, Xilin Yang, Yuzhu Li, Anna Elia, Karin Atlan, William Dean Wallace, Nir Pillar, and Aydogan Ozcan. The work was supported by the U.S. National Institutes of Health. Ozcan is also a co-founder of Pictor Labs, a UCLA spin-off company commercializing virtual staining technologies.

Article: https://spj.science.org/doi/10.34133/bmef.0226