MIT’s New AI Model Detects Early Signs of Pancreatic Cancer
The AI lab of MIT (MIT CSAIL), in collaboration with Dr. Limor Apelbaum of the department of radiation oncology at Harvard University, has come up with two new AI-powered models called PRISM neural network (PrismNN) and logistic regression (PrismLR)—for the early detection of pancreatic ductal adenocarcinoma (PDAC), a fatal form of cancer. Why is this discovery important? Results show that at a five times higher relative risk threshold, PRISM can identify 35% of PDAC cases, in comparison to the standard 10% identified by conventional screening criteria. This performance improvement marks a significant leap in early intervention potential. The pancreas’s deep abdominal location makes early detection challenging, and the lack of effective treatment underscores the importance of identifying high-risk patients early on. So the research team tapped into a federated network company, leveraging Electronic Health Record (EHR) data consisting of patient demographics, diagnoses, medications, and lab results from different institutions across the US. This extensive database, encompassing over five million patients, ensures the models’ reliability and generalizability, making them applicable across various populations, geographical locations, and demographic groups. PrismNN employs artificial neural networks to detect intricate patterns, while PrismLR uses logistic regression for a simpler analysis, providing a comprehensive evaluation of PDAC risk from the same EHR data. While the use of AI in cancer risk detection is not new PRISM’s distinguishing features lie in its development and validation on an extensive database, surpassing most prior research in the field. “The PRISM models stand out for their development and validation on an extensive database of over five million patients, surpassing the scale of most prior research in the field,” Kai Jia, MIT CSAIL PhD student and senior author on the paper, told AIM. “The model uses routine clinical and lab data to make its predictions, and the diversity of the US population is a significant advancement over other PDAC models, which are usually confined to specific geographic regions like a few healthcare centers in the US. Additionally, using a unique regularization technique in the training process enhanced the models’ generalizability and interpretability,” he added. The team envisions expanding the models’ applicability to international datasets beyond US and adding more biomarkers for more refined risk assessment. Earlier, Google DeepMind’s protein folding AI system AlphaFold helped to speed up the design and synthesis of a drug to treat hepatocellular carcinoma (HCC), the most common type of primary liver cancer.
The model can identify 35% of cancer cases, in comparison to the standard 10% identified by conventional screening criteria.
The model can identify 35% of cancer cases, in comparison to the standard 10% identified by conventional screening criteria. 
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Shritama (she/her) is a technology journalist at AIM who is passionate to explore generative AI with a special focus on big techs, database, healthcare, DE&amp;I, hiring in tech and more.