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人工智能驱动的脑转移瘤对立体定向放射治疗(SRS)反应的测量结果与当前的手动评估标准相比具有优势。

Artificial-intelligence-driven measurements of brain metastases' response to SRS compare favorably with current manual standards of assessment.

作者信息

Prezelski Kayla, Hsu Dylan G, Del Balzo Luke, Heller Erica, Ma Jennifer, Pike Luke R G, Ballangrud Åse, Aristophanous Michalis

机构信息

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Saint Louis University School of Medicine, St. Louis, Missouri, USA.

出版信息

Neurooncol Adv. 2024 Feb 19;6(1):vdae015. doi: 10.1093/noajnl/vdae015. eCollection 2024 Jan-Dec.

Abstract

BACKGROUND

Evaluation of treatment response for brain metastases (BMs) following stereotactic radiosurgery (SRS) becomes complex as the number of treated BMs increases. This study uses artificial intelligence (AI) to track BMs after SRS and validates its output compared with manual measurements.

METHODS

Patients with BMs who received at least one course of SRS and followed up with MRI scans were retrospectively identified. A tool for automated detection, segmentation, and tracking of intracranial metastases on longitudinal imaging, MEtastasis Tracking with Repeated Observations (METRO), was applied to the dataset. The longest three-dimensional (3D) diameter identified with METRO was compared with manual measurements of maximum axial BM diameter, and their correlation was analyzed. Change in size of the measured BM identified with METRO after SRS treatment was used to classify BMs as responding, or not responding, to treatment, and its accuracy was determined relative to manual measurements.

RESULTS

From 71 patients, 176 BMs were identified and measured with METRO and manual methods. Based on a one-to-one correlation analysis, the correlation coefficient was  = 0.76 ( = .0001). Using modified BM response classifications of BM change in size, the longest 3D diameter data identified with METRO had a sensitivity of 0.72 and a specificity of 0.95 in identifying lesions that responded to SRS, when using manual axial diameter measurements as the ground truth.

CONCLUSIONS

Using AI to automatically measure and track BM volumes following SRS treatment, this study showed a strong correlation between AI-driven measurements and the current clinically used method: manual axial diameter measurements.

摘要

背景

随着立体定向放射外科治疗(SRS)后脑转移瘤(BMs)治疗数量的增加,对其治疗反应的评估变得复杂。本研究使用人工智能(AI)跟踪SRS后的脑转移瘤,并将其输出结果与手动测量结果进行验证。

方法

回顾性确定接受至少一个疗程SRS并进行MRI扫描随访的脑转移瘤患者。将一种用于在纵向成像上自动检测、分割和跟踪颅内转移瘤的工具——重复观察下的转移瘤跟踪(METRO)应用于数据集。将METRO识别的最长三维(3D)直径与手动测量的最大轴向脑转移瘤直径进行比较,并分析它们的相关性。使用METRO在SRS治疗后识别的测量脑转移瘤大小变化将脑转移瘤分类为对治疗有反应或无反应,并相对于手动测量确定其准确性。

结果

从71例患者中,通过METRO和手动方法识别并测量了176个脑转移瘤。基于一对一相关性分析,相关系数为 = 0.76( = .0001)。使用修改后的脑转移瘤大小变化反应分类,当以手动轴向直径测量作为金标准时,METRO识别的最长3D直径数据在识别对SRS有反应的病变方面的敏感性为0.72,特异性为0.95。

结论

本研究使用人工智能在SRS治疗后自动测量和跟踪脑转移瘤体积,结果显示人工智能驱动的测量与当前临床使用的方法(手动轴向直径测量)之间存在很强的相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5d39/10924534/ea160304e49c/vdae015_fig1.jpg

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