导管内乳头状黏液瘤检测机器学习系统调查

Madhuri Martis, Subramanya Bhat, Sreenivasa B R
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引用次数: 0

摘要

IPMN 囊肿是胰腺恶性肿瘤的前期风险,有可能发展为胰腺癌。准确识别和评估风险水平对于规划有效的治疗策略至关重要。然而,由于 IPMN 囊肿以及胰腺本身的囊肿形状、质地和大小各不相同且不规则,因此这项任务极具挑战性。在本研究中,我们介绍了一种新的计算机辅助诊断方法,用于根据多重对比 MRI 扫描结果对 IPMN 风险等级进行分类。所提出的分析框架包括一种用于划分胰腺的高效容积自适应分割策略,以及一种新开发的基于深度学习的分类方案,该方案结合了一种基于放射组学的预测方法。为了评估所提出的决策融合模型,我们使用了多中心数据集和多对比度磁共振成像扫描,旨在实现优于该领域现有技术水平的性能。消融研究表明,与国际指南和已发表的研究相比,放射组学和深度学习模块在实现新的最先进(SOTA)性能(准确率为 81.9% 对 61.3%)方面具有重要意义。这些重要发现对临床决策具有重大意义,有可能彻底改变 IPMN 风险等级分类的方式。通过在多中心数据集(涉及来自五个中心的更多核磁共振扫描)上进行一系列严格的实验,我们获得了前所未有的性能水平和中等精度。代码将在发表后公布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A survey on machine learning system for intraductal papillary mucinous neoplasms detection
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.
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