多模式成像深度学习模型用于预测使用MpMRI和18F-PSMA-PET/CT的前列腺癌外延伸
Fei Yao1,2, Heng Lin1, Ying-Nan Xue1,2
1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, No. 1 of Xuefubei Road, Ouhai District, Wenzhou, 325000, Zhejiang Province, China.
概括
结合MRI和PET/CT扫描的新型深度学习模型准确地预测前列腺癌的前列腺外扩散. 这种工具提高了放射科医生的准确性,帮助个性化治疗决策.
科学领域:
- 放射学
- 医学成像
- 人工智能
背景情况:
- 准确预测前列腺外延伸 (EPE) 对于前列腺癌 (PCa) 治疗计划至关重要.
- 目前的诊断方法在精确确定EPE方面存在局限性.
- 多模式成像可以提高诊断准确性.
研究的目的:
- 开发一个多式深度学习 (DL) 模型,集成磁共振成像 (mpMRI) 和18F-PSMA-PET/CT,用于PCa中的EPE预测.
- 评估DL模型在提高放射科医生的EPE诊断准确性的有效性.
- 评估开发的DL模型的临床净益.
主要方法:
- 从经过激进前列腺切除术的PCa患者中回顾性收集临床和成像数据.
- 组合mpMRI和18F-PSMA-PET/CT的多模式DL模型的开发.
- 将多模式DL模型的性能与单模式模型和无模型辅助的放射学评估进行比较.
主要成果:
- 综合的mpMRI+PET/CT多模式DL模型在初级机构实现AUC为0.82和外部验证组为0.81.
- 与单一模式模型相比,多模式DL模型显示出更高的预测准确性.
- 与单独的放射科医生相比,深度学习辅助的评估显著改善了AUC和灵敏度 (P < 0. 05),具有适度的特异性降低.
结论:
- 综合mpMRI和18F-PSMA-PET/CT的多式深度学习模型显示了EPE在前列腺癌中的强大预测性能.
- 该模型有效地提高了放射科医生的EPE评估准确性.
- 这种人工智能工具显示出在前列腺癌管理中支持个性化和精确的治疗决策的潜力.
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