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Cedars-Sinai investigators develop AI model that may detect earliest signs of pancreatic cancer

New Study Indicates Artificial Intelligence's Potential for Predicting Who Will Develop Disease Based on CT Images An AI tool developed by Cedars-Sinai investigators could eventually be used to detect early pancreatic cancer in people undergoing CT scans for abdominal pain or other issues. Photo by Getty Images.

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years before being diagnosed with the disease. The findings, which may help prevent death through early detection of one of the most challenging cancers to treat, are published in the journal Cancer Biomarkers.

“This AI tool was able to capture and quantify very subtle, early signs of pancreatic ductal adenocarcinoma in CT scans years before the occurrence of the disease. These are signs that the human eye would never be able to discern,” said Debiao Li, Ph.D., director of the Biomedical Imaging Research Institute, professor of Biomedical Sciences and Imaging at Cedars-Sinai, and senior and corresponding author of the study. Li is also the Karl Storz Chair in Minimally Invasive Surgery in Honor of George Berci, MD. Debiao Li, PhD

Pancreatic ductal adenocarcinoma is not only the most common type of pancreatic cancer, but it’s also the most deadly. Less than 10% of people diagnosed with the disease live more than five years after being diagnosed or starting treatment. But recent studies have reported that finding the cancer early can increase survival rates by as much as 50%. There currently is no easy way to find pancreatic cancer early, however.

People with this type of cancer may experience symptoms such as general abdominal pain or unexplained weight loss, but these symptoms are often ignored or overlooked as signs of the cancer since they are common in many health conditions.

“There are no unique symptoms that can provide an early diagnosis for pancreatic ductal adenocarcinoma,” said Stephen J. Pandol, MD, director of Basic and Translational Pancreas Research and program director of the Gastroenterology Fellowship Program at Cedars-Sinai, and another author of the study. “This AI tool may eventually be used to detect early disease in people undergoing CT scans for abdominal pain or other issues.”

The investigators reviewed electronic medical records to identify people who were diagnosed with the cancer within the last 15 years and who underwent CT scans six months to three years before their diagnosis. These CT images were considered normal at the time they were taken. The team identified 36 patients who met these criteria, the majority of whom had CT scans done in the ER because of abdominal pain.

The AI tool was trained to analyze these pre-diagnostic CT images from people with pancreatic cancer and compare them with CT images from 36 people who didn’t develop the cancer. The investigators reported that the model was 86% accurate in identifying people who would eventually be found to have pancreatic cancer and those who would not develop the cancer. 

The AI model picked up on variations on the surface of the pancreas between people with cancer and healthy controls. These textural differences could be the result of molecular changes that occur during the development of pancreatic cancer. Stephen J. Pandol, MD

“Our hope is this tool could catch the cancer early enough to make it possible for more people to have their tumor completely removed through surgery,” said Touseef Ahmad Qureshi, Ph.D., a scientist at Cedars-Sinai and the first author of the study.

The investigators are currently collecting data from thousands of patients at healthcare sites throughout the U.S. to continue to study the AI tool’s prediction capability. Touseef Ahmad Qureshi, PhD

Leeds prof uses AI to detect cancer from patient data securely

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A new way of using artificial intelligence to predict cancer from patient data without putting personal information at risk has been developed by a team including University of Leeds medical scientists. Phil Quirke, Professor of Pathology in the University of Leeds’ School of Medicine

Artificial intelligence (AI) can analyze large amounts of data, such as images or trial results, and can identify patterns often undetectable by humans, making it highly valuable in speeding up disease detection, diagnosis, and treatment.

However, using the technology in medical settings is controversial because of the risk of accidental data release, and many systems are owned and controlled by private companies, giving them access to confidential patient data - and the responsibility for protecting it.

The researchers set out to discover whether a form of AI, called swarm learning, could be used to help computers predict cancer in medical images of patient tissue samples, without releasing the data from hospitals.

Swarm learning trains AI algorithms to detect patterns in data in a local hospital or university, such as genetic changes within images of human tissue. The swarm learning system then sends this newly trained algorithm - but importantly no local data or patient information - to a central computer. There, it is combined with algorithms identically generated by other hospitals to create an optimized algorithm. This is then sent back to the local hospital, where it is reapplied to the original data, improving the detection of genetic changes thanks to its more sensitive detection capabilities.

By undertaking this several times, the algorithm can be improved and one created that works on all the data sets. This means that the technique can be applied without the need for any data to be released to third-party companies or to be sent between hospitals or across international borders.

The team trained AI algorithms on study data from three groups of patients from Northern Ireland, Germany, and the USA. The algorithms were tested on two large sets of data images generated at Leeds and were found to have successfully learned how to predict the presence of different subtypes of cancer in the images.

The research was led by Jakob Nikolas Kather, Visiting Associate Professor at the University of Leeds’ School of Medicine and Researcher at the University Hospital RWTH Aachen. The team included Professors Heike Grabsch and Phil Quirke, and Dr. Nick West from the University of Leeds’ School of Medicine.

Dr. Kather said: “Based on data from over 5,000 patients, we were able to show that AI models trained with swarm learning can predict clinically relevant genetic changes directly from images of tissue from colon tumors.”

Phil Quirke, Professor of Pathology at the University of Leeds’s School of Medicine, said: “We have shown that swarm learning can be used in medicine to train independent AI algorithms for any image analysis task. This means it is possible to overcome the need for data transfer without institutions having to relinquish security control of their data.

“Creating an AI system which can perform this task improves our ability to apply AI in the future.”

UCI scientists turn a hydrogen molecule into a quantum sensor

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New technique enables precise measurement of electrostatic properties of materials

Physicists at the University of California, Irvine have demonstrated the use of a hydrogen molecule as a quantum sensor in a terahertz laser-equipped scanning tunneling microscope, a technique that can measure the chemical properties of materials at unprecedented time and spatial resolutions. The UCI team responsible for the assembly and use of the terahertz laser-equipped scanning tunneling microscope pictured here are, from left to right, Dan Bai, UCI Ph.D. student in physics & astronomy; Wilson Ho, Bren Professor of physics & astronomy and chemistry; Yunpeng Xia, Ph.D. student in physics & astronomy; and Likun Wang and Ph.D. candidate in chemistry. Steve Zylius / UCI

This new technique can also be applied to the analysis of two-dimensional materials which have the potential to play a role in advanced energy systems, electronics, and quantum supercomputers.

Today in Science, the researchers in UCI’s Department of Physics & Astronomy and Department of Chemistry describe how they positioned two bound atoms of hydrogen in between the silver tip of the STM and a sample composed of a flat copper surface arrayed with small islands of copper nitride. With pulses of the laser lasting trillionths of a second, the scientists were able to excite the hydrogen molecule and detect changes in its quantum states at cryogenic temperatures and in the ultrahigh vacuum environment of the instrument, rendering atomic-scale, time-lapsed images of the sample.

“This project represents an advance in both the measurement technique and the scientific question the approach allowed us to explore,” said co-author Wilson Ho, Bren Professor of physics & astronomy and chemistry. “A quantum microscope that relies on probing the coherent superposition of states in a two-level system is much more sensitive than existing instruments that are not based on this quantum physics principle.”

Ho said the hydrogen molecule is an example of a two-level system because its orientation shifts between two positions, up and down and slightly horizontally tilted. Through a laser pulse, the scientists can coax the system to go from a ground state to an excited state in a cyclical fashion resulting in a superposition of the two states. The duration of the cyclic oscillations is vanishingly brief – lasting mere tens of picoseconds – but by measuring this “decoherence time” and the cyclic periods the scientists were able to see how the hydrogen molecule was interacting with its environment.

“The hydrogen molecule became part of the quantum microscope in the sense that wherever the microscope scanned, the hydrogen was there in between the tip and the sample,” said Ho. “It makes for an extremely sensitive probe, allowing us to see variations down to 0.1 angstroms. At this resolution, we could see how the charge distributions change on the sample.”

The space between the STM tip and the sample is almost unimaginably small, about six angstroms or 0.6 nanometers. The STM that Ho and his team assembled is equipped to detect minute electrical current flowing in this space and produce spectroscopic readings proving the presence of the hydrogen molecule and sample elements. Ho said this experiment represents the first demonstration of chemically sensitive spectroscopy based on terahertz-induced rectification current through a single molecule.

The ability to characterize materials at this level of detail based on hydrogen’s quantum coherence can be of great use in the science and engineering of catalysts, since their functioning often depends on surface imperfections at the scale of single atoms, according to Ho.

“As long as hydrogen can be adsorbed onto a material, in principle, you can use hydrogen as a sensor to characterize the material itself through observations of their electrostatic field distribution,” said study lead author Likun Wang, UCI graduate student in physics & astronomy.