Using an artificial intelligence (AI) model, researchers were able to simultaneously predict cancer subtype, TP53 mutation status, and survival outcomes across 32 solid tumors from routine hematoxylin and eosin (H&E)–stained whole-slide images, according to findings published in The American Journal of Pathology.
“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," noted co-lead investigator Abadh K. Chaurasia, PhD, of the Menzies Institute for Medical Research, University of Tasmania, Australia and Pandani Solutions Pty Ltd.
Study Methods
Researchers developed a Vision Transformer–based multi-instance learning model to identify the presence of a TP53 biomarker, detect 32 solid tumor types, and predict survival based on whole-slide images.
They gathered 11,000 primary tumor data from the Pan-Cancer Atlas and somatic mutation, RNA sequencing, and clinical outcome data as training data for the model. With a Vision Transformer encoder, the whole-slide images underwent tissue masking, quality control, stain normalization, path extraction, and feature embedding.
For the predictive tasks, seven task heads were developed to account for cancer type, TP53 mutation status, TP53 RNA expression level, overall survival, progression-free survival, and corresponding event times.
“Standard molecular profiling for TP53 mutations is often costly and inaccessible in underprivileged or remote clinical settings,” explained co-lead investigator Alex W. Hewitt, PhD, also of the Menzies Institute for Medical Research and School of Medicine, University of Tasmania, Australia. “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.”
Model training was first done on tumor-only patches at multiple magnifications and then the model was fine-tuned on whole-slide images with a content-aware strategy.
Performance evaluations were conducted on an independent validation group of 1,729 slides.
Key Findings
The model achieved an area under the receiver operating characteristic curve of 0.766 for TP53 mutation status detection on the independent validation set across 32 cancer types.
“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,” Dr. Hewitt noted.
The model was also able to infer TP53 RNA expression levels, with reasonable alignment between predicted and observed expression levels, and tumor taxonomy based on the histopathology images.
In terms of tumor classification, the fine-tuned model achieved an overall accuracy of 0.659 on internal validation, which was considered to be strongly generalizable for the different tumor types. With the exception of ovarian cancer, most tumor types achieved area under the receiver operating characteristic curves above 0.88; ovarian cancer, meanwhile, had an area under the receiver operating characteristic curve of 0.782.
For survival prediction, the model was able to clearly separate high- and low-risk groups with highly significant differences.
“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," Dr. Hewitt concluded.
DISCLOSURES: This study was supported by an Australian National Health and Medical Research Council Leadership Award. For full disclosures of the study authors, visit ajp.amjpathol.org.
