多模式深度学习:瘤和内脏脂肪对结肠直肠癌的影响 隐藏的腹膜转移
Shidi Miao1, Mengzhuo Sun1, Beibei Zhang2
1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
European radiology
|February 17, 2025
概括
这项研究引入了针对结直肠癌 (CRC) 的多式深度学习 (DL) 方法,该方法使用瘤和内脏脂肪成像来预测腹膜转移,改善早期检测和预后.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 结肠直肠癌 (CRC) 的诊断和预后受到隐性腹转移的挑战.
- 当前的预测模型通常依赖于单模分析,限制准确性.
- 多式成像分析为改善腹转移的检测提供了潜力.
研究的目的:
- 开发和评估一种多式深度学习 (DL) 方法,用于预测CRC患者的腹转移.
- 研究瘤特征和内脏脂肪对转移预测的综合影响.
- 将DL模型的性能与放射科医生进行比较,并确定独立的预后因素.
主要方法:
- 基于ResNet18开发了一个深度学习模型,即基于ResNet18的多尺度功能融合网络 (MSFF-Net).
- 该模型从手术前的CT扫描中提取了瘤部分和内脏脂肪 (L3级) 的特征.
- 后勤回归将DL特征与临床数据集成在一起;随机森林分类器评估了性能.
主要成果:
- 结合内脏脂肪特征的多模式DL模型优于单模式分析.
- 将临床数据与DL特征相结合,产生了最高的诊断性能 (AUC为0.941和0.911).
- 更高的内脏脂肪组织指数 (VATI) 和最大瘤直径是腹转移的独立预后因素.
结论:
- 对瘤和内脏脂肪特征的多模式分析显著提高了CRC中腹转移的预测.
- 内脏脂肪作为一种有价值的成像生物标志物,用于早期检测CRC中的腹转移.
- 开发的DL方法有助于早期诊断和对CRC患者的准确预后评估.
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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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