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通过使用EfficientNet进行扩散权重成像来预测宫癌的阶段和等级

Souha Aouadi¹, Tarraf Torfeh¹, Othmane Bouhali²

¹Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.

Biomedical physics & engineering express

|May 30, 2024

概括

本研究引入了一种使用深卷积神经网络 (DCNN) 和明显扩散系数 (ADC) 地图的新型非侵入性方法,用于从单一图像中预测宫癌 (CC) 的等级和阶段.

科学领域:

放射学和医学成像学医学成像学
在瘤学瘤学.
人工智能在医学中的应用

背景情况:

宫癌 (CC) 的分级和分期对于治疗计划至关重要.
目前的方法可能具有侵入性或缺乏精度.
扩散权重成像 (DWI) 和明显扩散系数 (ADC) 地图为非侵入性评估提供了潜力.

研究的目的:

开发和验证一种使用单一图像预测CC等级和阶段的非侵入性方法.
为了利用深层卷积神经网络 (DCNN) 来分析ADC地图的图像.
评估DCNN模型的性能与传统的放射性分析相比.

主要方法:

追溯收集85名CC患者的ADC地图数据.
从预治疗DWI中提取瘤总体积 (GTV) 切片.
在二进制和四类分类任务中应用EfficientNetB0和EfficientNetB3模型.
利用合成少数群体过量采样技术 (SMOTE) 进行平衡的训练集和五倍交叉验证.

主要成果:

EfficientNetB3在年级预测方面取得了0.924的AUC.
对于阶段预测,EfficientNetB0实现了0.931的AUC.
DCNN模型显著优于放射性分析 (AUC为0.67-0.66) 并显示出优于或与ResNet50和Xception可比的结果.

结论:

使用DCNN从ADC地图预测CC等级和阶段是可行的.
基于EfficientNet的方法在非侵入性CC评估中显示出高准确度.
这种方法为改善CC诊断和治疗分层提供了一个有前途的工具.

关键词:

宫癌:宫癌是一种癌症.深度学习是一种深度学习.扩散加权成像是一种扩散加权成像.评分分级的评分分级.阶段化阶段化

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