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使用深度神经网络开发并验证用于脑转移瘤的全自动组织勾画模型

Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network.

作者信息

Zhao Jie-Yi, Cao Qi, Chen Jing, Chen Wei, Du Si-Yu, Yu Jie, Zeng Yi-Miao, Wang Shu-Min, Peng Jing-Yu, You Chao, Xu Jian-Guo, Wang Xiao-Yu

机构信息

Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.

Department of Reproductive Medical Center, West China Second University Hospital, Sichuan University, Chengdu, China.

出版信息

Quant Imaging Med Surg. 2023 Oct 1;13(10):6724-6734. doi: 10.21037/qims-22-1216. Epub 2023 Aug 31.

Abstract

BACKGROUND

Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain metastases delineation, none of these studies have performed clinical evaluation, raising concerns about clinical applicability. This study aimed to develop an artificial intelligence (AI) tool for the automatic delineation of single brain metastasis that could be integrated into clinical practice.

METHODS

Data from 426 patients with postcontrast T1-weighted MRIs who underwent SRS between March 2007 and August 2019 were retrospectively collected and divided into training, validation, and testing cohorts of 299, 42, and 85 patients, respectively. Two Gamma Knife (GK) surgeons contoured the brain metastases as the ground truth. A novel 2.5D CNN network was developed for single brain metastasis delineation. The mean Dice similarity coefficient (DSC) and average surface distance (ASD) were used to assess the performance of this method.

RESULTS

The mean DSC and ASD values were 88.34%±5.00% and 0.35±0.21 mm, respectively, for the contours generated with the AI tool based on the testing set. The DSC measure of the AI tool's performance was dependent on metastatic shape, reinforcement shape, and the existence of peritumoral edema (all P values <0.05). The clinical experts' subjective assessments showed that 415 out of 572 slices (72.6%) in the testing cohort were acceptable for clinical usage without revision. The average time spent editing an AI-generated contour compared with time spent with manual contouring was 74 196 seconds, respectively (P<0.01).

CONCLUSIONS

The contours delineated with the AI tool for single brain metastasis were in close agreement with the ground truth. The developed AI tool can effectively reduce contouring time and aid in GK treatment planning of single brain metastasis in clinical practice.

摘要

背景

立体定向放射外科(SRS)治疗计划需要精确勾勒脑转移瘤,这项任务可能既繁琐又耗时。尽管已有研究探索了在磁共振成像(MRI)中使用卷积神经网络(CNN)自动勾勒脑转移瘤,但这些研究均未进行临床评估,引发了对临床适用性的担忧。本研究旨在开发一种可整合到临床实践中的人工智能(AI)工具,用于自动勾勒单个脑转移瘤。

方法

回顾性收集了2007年3月至2019年8月期间接受SRS的426例有增强T1加权MRI的患者的数据,并分别分为299例、42例和85例患者的训练、验证和测试队列。两名伽玛刀(GK)外科医生将脑转移瘤勾勒为真实情况。开发了一种用于单个脑转移瘤勾勒的新型2.5D CNN网络。使用平均骰子相似系数(DSC)和平均表面距离(ASD)来评估该方法的性能。

结果

基于测试集,使用AI工具生成的轮廓的平均DSC和ASD值分别为88.34%±5.00%和0.35±0.21毫米。AI工具性能的DSC测量取决于转移瘤形状、强化形状和瘤周水肿的存在(所有P值<0.05)。临床专家的主观评估显示,测试队列中572个切片中的415个(72.6%)在无需修订的情况下可用于临床。与手动勾勒轮廓相比,编辑AI生成的轮廓平均花费的时间分别为74秒和196秒(P<0.01)。

结论

使用AI工具勾勒的单个脑转移瘤轮廓与真实情况高度一致。所开发的AI工具可有效减少勾勒轮廓的时间,并有助于临床实践中单个脑转移瘤的GK治疗计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4b21/10585546/01ff618b896d/qims-13-10-6724-f1.jpg

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