Illustration of human liver cancer showing a red liver on a blue background surrounded by pink/red cancerous cells. The liver cancer severity is being assessed by deep learning tools in this study.
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Research led by Tsinghua University, Beijing, has developed a deep learning (DL) program that can improve prognostic biomarker discovery to help patients with liver cancer.

The researchers used the tool, known as PathFinder, to show the value of a biomarker that plays a key role in liver cancer outcomes. They also hope it can be useful for finding biomarkers for different types of cancer in the future.

“Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment and treatment planning. However, there are few known biomarkers that are robust enough to show true analytical and clinical value,” write Lingjie Kong, a senior researcher from Tsinghua University, Beijing, and colleagues in the journal Nature Machine Intelligence.

“DL-based computational pathology can be used as a strategy to predict survival, but the limited interpretability and generalizability prevent acceptance in clinical practice… Thus there is still a desperate need for identifying additional robust biomarkers to guide tumor diagnosis and prognosis, and to direct the research of tumor mechanism.”

PathFinder is a DL-guided framework that is designed to be easy to interpret for pathologists and other healthcare professionals or researchers who are not computational experts. It uses a combination of whole slide images from patients with cancer and healthy controls with spatial information, as well as DL, to search for new biomarkers.

In this study, using liver cancer as an example, the tool showed spatial distribution of necrosis in liver cancer is strongly related to patient prognosis. This biomarker is known, but rarely used in current clinical practice.

From their findings, the research team suggested two measurements, necrosis area fraction and tumor necrosis distribution, as ways pathologists can assess spatial distribution of necrosis in liver cancer patients to improve the accuracy of prognostic predictions. They then verified these measures in the Cancer Genome Atlas Liver Hepatocellular Carcinoma dataset and the Beijing Tsinghua Changgung Hospital dataset.

“By combining sparse multi-class tissue spatial distribution information of whole slide images with attribution methods, PathFinder can achieve localization, characterization and verification of potential biomarkers, while guaranteeing state-of-the-art prognostic performance,” write the authors.

“In this study, we did not target AI as a substitute for pathologists, but as a tool for pathologists to mine dominate biomarkers. Just as AI guides mathematical intuition, pathologists can formulate specific hypotheses based on their clinical experience, and then use PathFinder to deeply mine the connection between hypotheses-relevant information and prognosis.”

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