论著摘要 |【Radiomics-综述】机器学习方法在肺癌定义中的应用

2018-06-19 17:13:20 admin
标签:   影像组学 肺癌 PET-CT

Application of machine learning methodology for PET-based definition of lung cancer.

发表日期: 2010.02.01   来源:Curr Oncol. 2010 Feb;17(1):41-7.

作者:

Kerhet A1, Small C, Quon H, Riauka T, Schrader L, Greiner R, Yee D, McEwan A, Roa W.

作者介绍:

1. Department of Oncology, University of Alberta, Edmonton, AB. kerhet@ualberta.ca

摘要

我们应用学习方法框架来辅助基于阈值的正电子发射断层摄影-计算机断层扫描(PET-CT)成像中用于放疗计划的非小细胞肺癌(NSCLC)肿瘤的分割。对两名患者的门控和标准自由呼吸研究进行独立分析(共四项研究)。每项研究都有PET-CT和一个治疗计划CT图像。两名经验丰富的放射肿瘤学家确定了参考的总肿瘤体积(GTV),同时还确定了最接近每个切片上的GTV轮廓的参考标准化摄取值(SUV)阈值。也计算了每个PET切片的一组摄取分布相关属性。在PET切片的子集上训练机器学习算法以处理最佳切换阈值中的切片到切片变化:即,从每个切片的计算属性预测最合适的SUV阈值。使用其余的PET切片来评估该算法的性能。在预测SUV阈值和参考SUV阈值(Jaccard指数超过0.82)概述的区域之间实现了高度的几何相似性。同一患者的门控和自由呼吸结果之间无显着差异。在这项前期工作中,我们展示了机器学习方法作为非小细胞肺癌放疗计划辅助工具的潜在适用性。

Abstact

We applied a learning methodology framework to assist in the threshold-based segmentation of non-small-cell lung cancer (NSCLC) tumours in positron-emission tomography-computed tomography (PET-CT) imaging for use in radiotherapy planning. Gated and standard free-breathing studies of two patients were independently analysed (four studies in total). Each study had a pet-ct and a treatment-planning ct image. The reference gross tumour volume (GTV) was identified by two experienced radiation oncologists who also determined reference standardized uptake value (SUV) thresholds that most closely approximated the GTV contour on each slice. A set of uptake distribution-related attributes was calculated for each PET slice. A machine learning algorithm was trained on a subset of the PET slices to cope with slice-to-slice variation in the optimal suv threshold: that is, to predict the most appropriate suv threshold from the calculated attributes for each slice. The algorithm's performance was evaluated using the remainder of the pet slices. A high degree of geometric similarity was achieved between the areas outlined by the predicted and the reference SUV thresholds (Jaccard index exceeding 0.82). No significant difference was found between the gated and the free-breathing results in the same patient. In this preliminary work, we demonstrated the potential applicability of a machine learning methodology as an auxiliary tool for radiation treatment planning in NSCLC.

关键词:

正电子发射断层扫描;人工智能;肿瘤总体积;GTV;肺癌;机器学习;PET;放射治疗;支持向量机;SVM

Keywords:

Positron-emission tomography; artificial intelligence; gross tumour volume; gtv; lung cancer; machine learning; pet; radiation treatment; support vector machine; svm

阅读原文:PMID: 20179802  PMCID: PMC2826776


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