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Baoqiang Ma¹, Jiapan Guo², Lisanne V van Dijk³

¹Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

|March 21, 2025

概括

一个标准的DenseNet模型实现了与最先进的深度学习模型可比的结果,用于预测头癌的无复发期. CT和PET成像数据的晚期融合改善了外部测试性能.

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:

在HECKTOR 2022挑战中,使用PET和CT图像开发了最先进的深度学习模型来预测头癌的无复发期 (RFP).
预测RFP对于优化头癌患者治疗策略至关重要.

研究的目的:

评估传统的DenseNet架构是否具有优化的层和图像融合策略,可以与最先进的RFP预测模型的性能匹配.
调查不同输入数据 (CT,PET,GTV) 和融合策略对预测性能的影响.

主要方法:

利用HECKTOR 2022数据集 (489名口腔癌患者) 进行内部测试,另外400名患者进行外部测试.
将DenseNet81模型与使用CT,PET和总瘤体积 (GTV) 数据的三种最先进模型进行比较.
研究了早期融合 (输入通道) 和晚期融合 (特征连接) 策略.

主要成果:

早期融合 (CT,PET,GTV) 的DenseNet81在内部测试中获得了0.69的C指数,与最先进的模型相比.
删除GTV数据保留了内部测试C指数,但改善了外部测试C指数从0.59到0.63.
与仅使用PET的模型相比,CT和PET数据与DenseNet81的晚期融合产生了更高的C指数值 (0.68内部,0.66外部).

结论:

一个基本的81层DenseNet架构证明了与更复杂的最先进模型相比具有竞争力的预测性能.
CT和PET成像数据的晚期融合是提高外部预测准确性的优越策略.
优化输入数据和融合技术可以提高深度学习模型对癌症复发的预测能力.

关键词:

在DenseNet中,使用的是DenseNet.在口腔喉癌.结果预测结果预测.聚乙烯/聚乙烯/聚乙烯

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