BEIJING (ANN/CHINA DAILY) – In a groundbreaking development, a cutting-edge deep learning method has emerged, enabling the precise detection and classification of pancreatic lesions through non-contrast computed tomography.
This innovative approach, highlighted in a recent publication in the esteemed medical journal Nature Medicine, heralds a potential game-changer in large-scale pancreatic cancer screening.
The artificial intelligence-driven pancreatic cancer detection technique harnesses advanced deep learning technology crafted by Alibaba Group’s Damo Academy.
The research, unveiled on the journal’s website, marks a significant stride toward more effective and accessible early detection methods for pancreatic cancer.
Scientists from Damo Academy collaborated with over 10 esteemed medical institutions across China, the Czech Republic and the United States in a groundbreaking study.
Employing state-of-the-art medical AI technology alongside CT scans, the research successfully identified 31 instances of pathological changes while screening over 20,000 asymptomatic individuals for pancreatic cancer in real-world conditions.
Notably, two patients diagnosed with early-stage pancreatic cancer underwent successful surgical interventions, resulting in a cure.
Pancreatic cancer poses a formidable challenge with an average five-year survival rate of less than 10 per cent, ranking it among the malignancies with the poorest prognosis globally. This holds true in both China and worldwide.
Alarmingly, approximately 80 per cent of pancreatic cancer cases are typically detected only at an advanced and inoperable stage, underscoring the critical need for advanced screening methods and early interventions.
Medical experts said that there is a lack of effective screening methods in the current clinical guidelines, as the contrast of CT scan images commonly used in physical examinations is low, which makes it hard to identify early pancreatic pathological changes.
In view of the often hidden location of pancreatic tumours and the lack of obvious representation in CT images, researchers have constructed a deep learning framework and developed it as an early detection model for pancreatic cancer. Among its functions are locating the pancreas, detecting abnormalities, and classifying and identifying the types of pancreatic pathological changes.
“In short, the technology uses AI to magnify and identify the subtle features of pathological changes in non-contrast CT images that are difficult to identify with the naked eye and thus achieves efficient and safe early pancreatic cancer detection.
“It also overcomes the problem of high false positives as seen in earlier screening methods,” said Lyu Le, who is in charge of the medical AI team at Damo Academy.
Cao Kai, co-first author of the paper and a doctor at the Shanghai Institute of Pancreatic Diseases, said that the study was verified by more than 10 hospitals, and showed 92.9 per cent sensitivity, the rate of accuracy in determining the presence of pancreatic pathological changes, and 99.9 per cent specificity, the rate of accuracy in determining the absence of the disease.
The institutions involved in developing the approach include the Shanghai Institute of Pancreatic Diseases, the First Affiliated Hospital of Zhejiang University School of Medicine, Shengjing Hospital of China Medical University, the First Faculty of Medicine at Charles University in Prague, and Johns Hopkins University in the US.
Researchers said that they will continue to conduct multi-centre, prospective clinical validation.
“The paper proposed a potential method to screen for pancreatic cancer on a large scale. It may improve the detection rate without putting additional radiation and financial burdens on patients,” said Gu Yajia, director of the department of diagnostic radiology at the Fudan University Shanghai Cancer Centre.
The medical AI team at Damo Academy said it is also collaborating with multiple top medical institutions around the world to use AI to explore new methods of low-cost and efficient multiple cancer screening, in order to allow individuals to screen for a variety of early-stage cancers through a single non-contrast CT scan.
