A survey on machine learning system for intraductal papillary mucinous neoplasms detection

Madhuri Martis 1, *, Subramanya Bhat 2 and Sreenivasa B R 3

1 Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere and Research Scholar, Srinivas University, Mukka, Mangalore-574146.
2 Department of Computer Science and Engineering, Srinivas University, Mukka, Mangalore-574146.
3 Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere- 577002.
 
Research Article
International Journal of Science and Research Archive, 2024, 12(02), 976–985.
Article DOI: 10.30574/ijsra.2024.12.2.1308
Publication history: 
Received on 09 June 2024; revised on 18 July 2024; accepted on 20 July 2024
 
Abstract: 
IPMN cysts, a pre-malignant risk to the pancreas, have the potential to develop into pancreatic cancer. Accurately identifying and evaluating the risk level is crucial for planning an efficient treatment strategy. However, this task is immensely challenging due to the varied and irregular shapes, textures, and sizes of IPMN cysts, as well as those of the pancreas itself. In this study, we introduce a new computer-aided diagnostic approach for classifying IPMN risk levels based on multi-contrast MRI scans. The proposed analysis framework comprises an efficient volumetric self-adapting segmentation strategy for delineating the pancreas, followed by a newly developed deep learning-based classification scheme incorporating a radiomics-based predictive approach. To evaluate the proposed decision-fusion model, we use multi-centre datasets and multi-contrast MRI scans, aiming to achieve superior performance compared to the current state of the art in this field. The ablation studies illustrate the importance of both radiomics and deep learning modules in achieving a new state-of-the-art (SOTA) performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). These key findings carry significant implications for clinical decision-making, potentially revolutionizing the way IPMN risk levels are classified. Through a series of rigorous experiments on multi-centre datasets (involving more MRI scans from five centers), we attained unprecedented performance levels with moderate accuracy. The code will be made available upon publication.
 
Keywords: 
Radiomics; IPMN Classification; Pancreatic Cysts; MRI; Pancreas segmentation
 
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