Radiomics Boosts Deep Learning Model for IPMN Classification

TL;DR

This study introduces a novel computer-aided diagnosis pipeline combining radiomics and deep learning for accurate IPMN risk classification from multi-contrast MRI scans, outperforming existing methods.

Contribution

The paper presents a new integrated framework with a volumetric segmentation and a radiomics-based deep learning classifier, achieving state-of-the-art accuracy in multi-center datasets.

Findings

Achieved 81.9% accuracy in IPMN classification.

Outperformed existing guidelines and published studies.

Validated on multi-center data from five different institutions.

Abstract

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9\% vs 61.3\% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9\% accuracy).