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Grape: Alibaba Launches AI To Detect Early Stomach Cancer Via CT Scans

Alibaba Group has announced what it claims to be the world’s first artificial intelligence (AI) model capable of detecting gastric (stomach) cancer, including early-stage cases, by analyzing computed tomography (CT) scans. The model, called Grape—short for “gastric cancer risk assessment procedure”—was developed through a collaboration between Alibaba’s Damo Academy and the Zhejiang Cancer Hospital in Hangzhou, China.

Grape utilizes a deep-learning framework to process three-dimensional CT images, identifying and segmenting potential gastric tumors. Traditionally, stomach cancer diagnosis has relied on endoscopy, an invasive procedure involving a camera and biopsy tools inserted through the throat. However, in China, fewer than 30% of patients comply with doctors’ recommendations for endoscopy due to its invasive nature, according to Cheng Xiangdong, a gastric surgeon at Zhejiang Cancer Hospital.

Image courtesy of Nature.com

According to a study published in Nature Medicine, Grape demonstrated significant improvements in diagnostic performance compared to human radiologists. The model achieved 85.1% sensitivity (ability to correctly identify cancer cases) and 96.8% specificity (ability to avoid false positives), outperforming radiologists by 21.8% and 14% respectively. These improvements were particularly pronounced in detecting early-stage cancer, which is often asymptomatic and harder to identify.

The developers suggest that this innovation could revolutionize the screening process for gastric cancer, not only in China but potentially on a global scale. Early detection is critical, as stomach cancer is the fourth leading cause of cancer-related deaths worldwide, often going unnoticed until advanced stages.

In one real-world case cited by Damo Academy, a patient with late-stage gastric cancer could have received a diagnosis six months earlier if Grape had been used, highlighting the system’s potential to reduce diagnostic delays. The team hopes that by integrating this AI tool into standard clinical workflows, early detection rates can be significantly improved, ultimately leading to better patient outcomes.

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