Researchers at Vanderbilt University Medical Center using artificial intelligence have helped develop two technologies for improving cancer care. 

One technology called MSI-SEER, described in a study published in npj Digital Medicine, better predicts microsatellite instability-high status from standard pathology slides and provides clinicians with specific data, including any uncertainties with predictions. The other technology, a breakthrough three-dimensional imaging tool described in a study published in Nature Communications, has transformative potential beyond cancer diagnostics. 

These new technologies showcase how VUMC researchers are using the power of AI to meet a wide range of medical needs, said Tae Hyun Hwang, PhD, professor of Surgery, founding director of the Molecular AI Initiative, and director of AI Research for the Vanderbilt Section of Surgical Sciences. He noted that the 3D imaging could significantly advance development of therapeutic drugs, provide more detailed assessments of organ transplant rejections, assist with personalized medicine, and aid with tissue analysis for biopharmaceutical development. 

“This technology fundamentally redefines how we visualize and analyze tissue architecture, moving from traditional two-dimensional views to full 3D microenvironment mapping at the subcellular level,” said Hwang, a corresponding author of the study, who provided senior leadership in the development, validation and translational development of the technology. 

The 3D study published in Nature Communications introduced an innovative framework that integrates holotomography with deep learning to generate hematoxylin- and eosin-stained images directly from thick tissue samples. This noninvasive, AI-driven approach preserves tissue integrity, overcomes the traditional 4- to 5-micron thickness limit of routine histology, and enables volumetric visualization of biological structures up to 50 microns thick. 

By preserving tissue samples and avoiding chemical alteration, this method also ensures compatibility with downstream molecular assays, such as spatial transcriptomics, proteomics and genomic profiling — enhancing the breadth and depth of diagnostic and research capabilities.  

“This is not just a digital copy of hematoxylin- and eosin-staining,” Hwang said. “It is a foundational platform for AI-driven volumetric tissue analysis that accelerates discoveries in oncology, immunology, regenerative medicine and therapeutic development.” 

The multi-institutional effort also included researchers from KAIST, Tomocube Inc., Yonsei University College of Medicine and Mayo Clinic. Hwang received funding support from the National Cancer Institute (grants R01CA276690, R37CA265967, U01CA294518). 

VUMC researchers developed the MSI-SEER predictor technology in collaboration with Mayo Clinic, Yonsei Severance Hospital and Seoul St. Mary’s Hospital in South Korea. This technology identifies patients who will benefit from an immunotherapy that might otherwise be missed with existing prediction models. 

Microsatellite instability-high (MSI-H) status is a well-established biomarker used to identify patients likely to respond to immune checkpoint inhibitors, especially patients with gastrointestinal cancers. However, traditional testing methods — including immunohistochemistry and PCR-based assays — offer only a binary result and often miss focal or heterogeneous MSI-H regions within tumors.  

MSI-SEER overcomes this limitation by dividing each pathology slide into thousands of image tiles and generating region-by-region predictions of MSI-H probability. This enables visualization of the tumor’s spatial heterogeneity and quantification of the MSI-H fraction across the tumor. In multiple cases, MSI-SEER identified MSI-H regions in tumors previously classified as microsatellite stability, and those patients subsequently responded to immunotherapy. 

“This is analogous to what we say in HER2-low gastric cancer, where patients previously not eligible for targeted therapy are now being treated with agents like trastuzumab deruxtecan,” Hwang said. “Likewise, patients with low or heterogeneous MSI-features may now be reconsidered for immunotherapy if spatially resolved analysis like MSI-SEER is used.” 

A key innovation of MSI-SEER is its ability to report not only predictions but the confidence level for each result.  

“AI should not dictate clinical decisions; it should support them,” Hwang said. “MSI-SEER gives clinicians both the answer and a measure of how reliable the answer is. It’s not about replacing human expertise but about combining the best of AI computation with physician judgment to drive safe, precise decisions.” 

Hwang, who conceptualized the study and is the paper’s senior author, received research support from the National Cancer Institute and the Department of Defense. He also received support from the Eric and Wendy Schmidt Fund for AI Research and Innovation and the American Association for Cancer Research Innovation and Discovery Grant.  

Other VUMC researchers who authored the study are Sunho Park, PhD, Minji Kim, MS, Jean Clemenceau, PhD, and Inyeop Jang, PhD. 

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