Abstract #0110
qMRI enhances standard-of-care for hepatocellular carcinoma detection: proof-of-concept using Bayesian networks
Yi-Chun Wang1,2, Yu Jun Wong3,4, Jeremy Wei Song Choo5, Sulaiha Binte Ithnin6, Guan Huei Lee7, Marianne Anastasia De Roza8, Kok Kiong ONG9, Patricia Ching Yen Chia10, Kee Tung Tan11, Ngiap Chuan Tan12, Han Chong Toh13, Oi Fong Chong14, Jason Pik Eu Chang15, Wei Lyn Yang16, Xin Yi Yeap17, Cheryl Min En Chua18, Jacelyn Siou Sze Chua18, Jade Shu Qi Goh18, Yu Ki Sim18, Carine Ching Yee Lim18, Daniel Bulte1, Michael Brady1,2, and Pierce Chow19,20
1University of Oxford, Oxford, United Kingdom, 2Perspectum Ltd, Oxford, United Kingdom, 3Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore, Singapore, 4Medical Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore, 5SingHealth Polyclinic - Sengkang, Singapore, Singapore, 6SingHealth Polyclinic - Tampines, Singapore, Singapore, 7Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore, 8Department of Gastroenterology and Hepatology, Sengkang General Hospital, Singapore, Singapore, 9SingHealth Polyclinic - Outram, Singapore, Singapore, 10SingHealth Polyclinic - Bukit Merah, Singapore, Singapore, 11SingHealth Polyclinic - Marine Parade, Singapore, Singapore, 12SingHealth Polyclinic - Pasir Ris, Singapore, Singapore, 13Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore, 14SingHealth Polyclinic - Bedok, Singapore, Singapore, 15Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore, Singapore, 16Gastroenterology and Hepatology, Tan Tock Seng Hospital, Singapore, Singapore, 17SingHealth Polyclinic - Punggol, Singapore, Singapore, 18Division of Surgery and Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore, 19Department of Hepato-pancreato-biliary and Transplant Surgery, Division of Surgery and Surgical Oncology, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore, 20Surgery Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
Synopsis
Keywords: Liver, Cancer, Modelling, Data analysis, Quantitative Imaging
Motivation: Current standard-of-care (SOC) using ultrasound and alpha fetoprotein (AFP) to detect hepatocellular carcinoma (HCC) has low specificity. Quantitative MRI (qMRI) shows promise, but is not yet widely used.
Goal(s): Using Bayesian networks (BNs) to demonstrate “what if” reasoning that assess the added value of qMRI over SOC.
Approach: Data from the ELEGANCE study were curated to train general linear models (GLMs) and BNs. BNs performed incremental probabilistic inference to rule out HCC.
Results: Each qMRI metric increased confidence in ruling out HCC, when combined, approximately a 10% confidence boost was observed. Validation showed comparable performance between GLMs and BNs for classifying cirrhosis.
Impact: qMRI information increases a clinician’s confidence in ruling out HCC using either GLMs or BNs. Additionally, Bayesian networks enable incremental assessment of new metrics before the completion of data collection.
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