多中心宫颈癌核磁共振成像淋巴结转移预测的排序注意多实例学习

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Shan Jin, Hongming Xu, Yue Dong, Xiaofeng Wang, Xinyu Hao, Fengying Qin, Ranran Wang, Fengyu Cong
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引用次数: 0

摘要

目的:在目前的临床诊断过程中,淋巴结转移(LNM)诊断的金标准是手术淋巴腺切除后的组织病理学检查。开发一种无创、术前预测 LNM 的方法很有必要,并具有重要的临床意义:方法:我们开发了一种排序注意力多实例学习(RA-MIL)模型,该模型整合了卷积神经网络(CNN)和排序注意力池,可通过 T2 MRI 诊断 LNM。我们的 RA-MIL 模型应用卷积神经网络(CNN)从二维核磁共振成像切片中提取成像特征,并利用排序注意集合创建患者级别的特征表示,用于诊断分类。基于 MIL 和注意力理论,对 LNM 阳性患者的 MRI 切片中排名靠前的信息区域进行可视化处理,以提高 LNM 自动预测的可解释性。这项回顾性研究从一家医院(289 名患者)和一个开源数据集(11 名患者)中收集了 300 名接受 T2 加权磁共振成像(MRI)扫描和组织病理学诊断的宫颈癌女性患者:我们的 RA-MIL 模型具有良好的 LNM 预测性能,内部测试集的接收者操作特征曲线下面积 (AUC) 为 0.809,公开数据集的接收者操作特征曲线下面积 (AUC) 为 0.833。实验表明,与其他最先进的(SOTA)比较深度学习模型相比,使用所提出的 RA-MIL 模型在 LNM 状态预测方面有明显改善:所开发的 RA-MIL 模型有望成为术前 LNM 预测的无创辅助工具,为 LNM 阳性患者的 MRI 切片和区域提供可视化解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI

Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI

Purpose

In the current clinical diagnostic process, the gold standard for lymph node metastasis (LNM) diagnosis is histopathological examination following surgical lymphadenectomy. Developing a non-invasive and preoperative method for predicting LNM is necessary and holds significant clinical importance.

Methods

We develop a ranking attention multiple instance learning (RA-MIL) model that integrates convolutional neural networks (CNNs) and ranking attention pooling to diagnose LNM from T2 MRI. Our RA-MIL model applies the CNNs to derive imaging features from 2D MRI slices and employs ranking attention pooling to create patient-level feature representation for diagnostic classification. Based on the MIL and attention theory, informative regions of top-ranking MRI slices from LNM-positive patients are visualized to enhance the interpretability of automatic LNM prediction. This retrospective study collected 300 female patients with cervical cancer who underwent T2-weighted magnetic resonance imaging (MRI) scanning and histopathological diagnosis from one hospital (289 patients) and one open-source dataset (11 patients).

Results

Our RA-MIL model delivers promising LNM prediction performance, achieving the area under the receiver operating characteristic curve (AUC) of 0.809 on the internal test set and 0.833 on the public dataset. Experiments show significant improvements in LNM status prediction using the proposed RA-MIL model compared with other state-of-the-art (SOTA) comparative deep learning models.

Conclusions

The developed RA-MIL model has the potential to serve as a non-invasive auxiliary tool for preoperative LNM prediction, offering visual interpretability regarding informative MRI slices and regions in LNM-positive patients.

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来源期刊
CiteScore
3.60
自引率
19.00%
发文量
331
审稿时长
3 months
期刊介绍: Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
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