August 13, 2026 – Researchers have developed a novel AI model that can analyze a routine whole histopathology image and simultaneously predict cancer subtype, specific genetic mutations, and survival outcomes across 32 different solid cancers rather than focusing on a single cancer type. The findings from the study in The American Journal of Pathology , published by Elsevier, highlight the potential of computational pathology to connect routine diagnostic imaging with molecular oncology.
Histopathology, the microscopic study of tissue, remains the gold standard for diagnosing cancer and identifying prognostic features across most solid tumors. However, current clinical workflows still depend on additional molecular and genomic assays to identify key alterations, such as those in TP53, which is one of the most frequently altered tumor suppressor genes across human cancers. Because mutations in TP53 influence tumor growth and treatment resistance, accurate diagnosis and molecular profiling are essential for guiding treatment and improving patient outcomes.
"Standard molecular profiling for TP53 mutations is often costly and inaccessible in underprivileged or remote clinical settings," explains co-lead investigator Alex W. Hewitt, PhD, Menzies Institute for Medical Research and School of Medicine, University of Tasmania. "We wanted to develop a more practical tool for pathologists. Currently, most deep learning-based models are used for single-model concepts; one model for one task. We developed a single model that can generate seven outputs simultaneously from the whole histopathology image, including TP53 mutation status, TP53 RNA expression, tumor type, and survival-related outcomes at the slide level."
The AI-based Vision Transformer model analyzed routine hematoxylin and eosin (H&E) stained whole slide images of human solid tumors. The model was trained on a dataset that included more than 11,000 primary tumor cases retrieved from the Pan-Cancer Atlas, with corresponding somatic mutation, RNA-sequencing, and clinical outcome data.
The most significant result was that the model achieved a strong predictive accuracy score (AUROC of 0.766) for TP53 mutation detection across 32 solid tumor types in an independent validation set of 1,729 slides. The model also demonstrated the ability to infer TP53 RNA expression levels and tumor taxonomy directly from whole slide images.
Co-lead investigator Abadh K. Chaurasia, PhD, Menzies Institute for Medical Research, University of Tasmania, and Pandani Solutions Pty Ltd, notes, "This approach could help identify patients who may benefit from confirmatory molecular testing, support triage in settings with limited genomic testing, and provide additional decision support to clinicians. Importantly, this method should be viewed as complementary to molecular testing, not a replacement. Its potential impact is strongest as a screening, prioritization, or decision-support tool within broader diagnostic pathways."
Because whole slide images are extremely large and complex,and obtaining detailed expert annotations for every relevant tumor region is difficult, costly, and often subjective, the researchers deployed a weakly supervised learning strategy.
"In this study, molecular labels such as TP53 mutation status were available at the patch level, but whole slide images containing TP53-associated morphological information were not manually labeled. Weak supervision enabled the model to learn from slide-level labels and identify relevant patterns across image patches without requiring exhaustive pixel- or region-level annotations," notes Dr. Hewitt.
He concludes, "Accurate molecular profiling from routine histopathology slides, already widely used in cancer care, could transform clinical oncology. This new AI-based model integrates diagnostic, molecular, and prognostic tasks, and could help clinicians obtain more information from existing pathology workflows, ultimately supporting more accessible precision cancer care and early intervention."
