I N T E L L I G E N T T E C H

The model demonstrates high accuracy for predicting ICI responsiveness by integrating tumour MSI status with stroma-to-tumour ratio. This breakthrough technology is expected to provide a path to significantly improve clinical outcomes for patients with gastric and colorectal cancers.

Researchers propose an innovative deep learning model for accurately predicting MSI tumour and ICI responsiveness.

Cancer remains one of the most serious health concerns for mankind, with one in three people expected to be diagnosed in their lifetime. A crucial indicator of the outcome of cancer is its tumour microsatellite status- whether it is stable or unstable. It refers to how stable the DNA is in tumours with respect to the number of mutations within microsatellites. The tumour microsatellite status has important clinical value because patients with microsatellite instability-high( MSI-H) cancers usually have more promising outcomes compared to patients with microsatellite stable tumours. Furthermore, tumours deficient in mismatch repair proteins- cells with mutations in specific genes that are involved in correcting mistakes made when DNA is copied in a cell- respond well to ICIs and not necessarily to chemotherapeutics.

Therefore, health practitioners and experts suggest MSI testing for newly diagnosed gastric and colorectal cancers. In recent years AI has made significant strides in this field and its incorporation in clinical workflow is expected to provide cost-efficient and highly accessible MSI testing. While several studies have utilised deep learning methods such as convolutional neural networks and visiontransformer-based techniques for MSI status prediction, they fail to capture the uncertainty in the prediction. Moreover, most of them do not provide key insights into ICI responsiveness, restricting their clinical applications.

Addressing these shortcomings, in a recent breakthrough, a team of researchers from the US and Korea including Jae-Ho Cheong, a Professor at Yonsei University College of Medicine, and Jeonghyun Kang, a Professor at Gangnam Severance Hospital, Yonsei University College of Medicine, proposed MSI-SEER. This innovative deep Gaussian process-based Bayesian model analyses haematoxylin and eosin-stained whole-slide images in weakly supervised learning to predict microsatellite status in gastric and colorectal cancers.

“ We performed extensive validation using multiple large datasets comprising patients from diverse racial backgrounds and found that MSI-SEER achieved state-of-the-art performance with MSI prediction by integrating uncertainty prediction,” said Prof Cheong.

In addition, the model proved to be highly accurate for ICI responsiveness prediction by integrating tumour MSI status and stroma-to-tumour ratio. Furthermore, the tile-level predictions by MSI-SEER provided key insights into the contribution of spatial distribution of MSI-H regions in the tumour microenvironment and ICI response.“ We believe our technology already has potential for real-world application as a form of prospective cohort surveillance or a kind of Phase IV clinical trials. The longer-term implication of this study is how an AI algorithm can analyse clinical multi-modal data and create clinically usable models for precision cancer medicine,” said Prof Cheong. �

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