论著摘要 |【PET】使用卷积神经网络预测PET成像对新辅助化疗的反应(双语版)

2017-12-27 18:46:46 admin 0

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

发表日期: 2015.09.10   来源:PLoS One. 2015 Sep 10;10(9):e0137036.

作者:

Ypsilantis PP1, Siddique M2, Sohn HM2 , Davies A2, Cook G2, Goh V2, Montana G1.

作者介绍:

1. Department of Biomedical Engineering, King's College London, London SE1 7EH, United Kingdom.

2. Department of Cancer Imaging, King's College London, London SE1 7EH, United Kingdom.

摘要

用18F-氟脱氧葡萄糖正电子发射断层扫描(18F-FDG PET)成像成为肿瘤诊断和分期的标准组成部分,作为个体对治疗反应的定量监测变得越来越重要。 在本文中,我们调查了在治疗前采集的单次18F-FDG PET扫描预测患者对新辅助化疗的反应的挑战性问题。我们采取“放射组学”方法,从而从预处理PET图像中自动提取大量的定量特征,以构建肿瘤表型的综合定量。虽然主导方法依赖于手工制作的纹理特征,但我们探索了直接从PET扫描自动学习低级到高级功能的潜力。我们报告了一项比较两种竞争性影像学策略性能的研究:基于最先进的统计分类器的方法,使用超过100种定量成像描述符,包括纹理特征以及标准摄取值,以及卷积神经网络 ,3S-CNN,通过采取相邻肿瘤内切片的PET扫描直接训练。我们的实验结果基于107例食管癌患者的样本,提供了卷积神经网络有潜力提取高度预测治疗反应的PET成像表征的初步证据。 在该数据集上,3S-CNN在预测无应答者时的灵敏度平均为80.7%,特异性为81.6%,优于其他竞争预测模型。

Abstact

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient’s response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a “radiomics” approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

阅读原文:PMID: 26355298  PMCID: PMC4565716DOI:   10.1371/journal.pone.0137036


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