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增强型CT成像中的深度学习:预测直肠腺癌的入侵深度

Wenya Lin¹, Chengwei Liao², Huang Su³

¹Department of Ultrasonography, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Abdominal radiology (New York)

|January 12, 2026

概括

一个新的深度学习模型,RectoDepthAI,使用CT扫描准确评估直肠腺癌的入侵深度. 这种人工智能工具有助于区分早期和高级阶段,提高诊断精度,以便做出更好的治疗决策.

科学领域:

放射学和医学成像学医学成像学
在瘤学中使用人工智能
深度学习用于医学诊断

背景情况:

准确评估瘤入侵深度对于分期直肠腺癌至关重要.
区分早期 (粘膜下/肌肉自身) 和高级 (直肠/邻近结构) 侵袭影响治疗策略.

研究的目的:

开发和评估RectoDepthAI,一种深度学习模型,用于评估使用增强型CT图像评估直肠腺癌中的瘤入侵深度.
为了区分直肠癌的早期和晚期,非侵入性地.
为了提高直肠癌分期的诊断准确度.

主要方法:

RectoDepthAI使用ResNet-18进行特征提取和长短期记忆 (LSTM) 进行顺序CT切片分析.
使用了934名患者的数据集,进行了数据预处理,增强和5倍交叉验证.
用梯度加权类激活映射 (Grad-CAM) 来实现模型的可解释性.

主要成果:

在5轮验证中,RectoDepthAI的平均AUC为0.883,准确度为84.1%,灵敏度为87.0%,特异性为80.0%.
该模型在区分瘤入侵深度方面表现强.
格拉德-CAM为诊断预测提供了更好的解释性.

结论:

RectoDepthAI显示显著的希望作为一个非侵入性的工具,用于分期直肠腺癌.
该模型可以提高诊断精度,帮助临床医生在治疗决策中.
用人工智能驱动的CT图像分析为直肠癌管理提供了有价值的方法.

关键词:

在CT成像中使用CT成像.深度学习是一种深度学习.侵袭深度侵袭深度侵袭深度预测预测预测这是直肠腺癌.

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