评估MRI解剖在机器学习预测模型中评估水凝胶间隔剂对前列腺癌患者的益处

Q1 Nursing
Madison Bush , Scott Jones , Catriona Hargrave
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引用次数: 0

摘要

水凝胶间隔剂(HS)旨在通过在直肠和包括前列腺和精囊(SV)在内的目标治疗体积之间创造物理间隙,将前列腺癌放射治疗(RT)中直肠的辐射剂量降至最低。本研究旨在确定将诊断性MRI (dMRI)信息纳入统计机器学习(SML)模型的可行性,该模型与计划CT (pCT)解剖一起开发,用于剂量和直肠毒性预测。SML模型旨在支持在RT规划程序之前的HS插入决策。方法回顾性分析20例患者的感兴趣区域(roi)在pCT和dMRI扫描上的轮廓。计算ROI Dice和Hausdorff distance (HD)比较指标。将ROI和患者临床危险因素(CRFs)变量输入到三个SML模型中,然后通过混淆矩阵、AUC曲线、准确性性能度量结果和观察到的患者结果比较pCT和dmri的剂量和毒性模型的性能。结果前列腺、SV和直肠dMRI与pCT roi的平均Dice值分别为0.81、0.47和0.71。前列腺、SV和直肠的平均Hausdorff距离分别为2.15、2.75和2.75 mm。使用dMRI roi时,所有模型的平均精度指标为0.83,使用pCT roi时为0.85。结论pCT和dMRI解剖ROI变量的差异在本研究中没有影响SML模型的性能,证明了使用dMRI图像的可行性。由于样本量有限,建议对包括dMRI解剖在内的预测模型进行进一步训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients

Introduction

Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a physical gap between the rectum and the target treatment volume inclusive of the prostate and seminal vesicles (SV). This study aims to determine the feasibility of incorporating diagnostic MRI (dMRI) information in statistical machine learning (SML) models developed with planning CT (pCT) anatomy for dose and rectal toxicity prediction. The SML models aim to support HS insertion decision-making prior to RT planning procedures.

Methods

Regions of interest (ROIs) were retrospectively contoured on the pCT and registered dMRI scans for 20 patients. ROI Dice and Hausdorff distance (HD) comparison metrics were calculated. The ROI and patient clinical risk factors (CRFs) variables were inputted into three SML models and then pCT and dMRI-based dose and toxicity model performance compared through confusion matrices, AUC curves, accuracy performance metric results and observed patient outcomes.

Results

Average Dice values comparing dMRI and pCT ROIs were 0.81, 0.47 and 0.71 for the prostate, SV, and rectum respectively. Average Hausdorff distances were 2.15, 2.75 and 2.75 mm for the prostate, SV, and rectum respectively. The average accuracy metric across all models was 0.83 when using dMRI ROIs and 0.85 when using pCT ROIs.

Conclusion

Differences between pCT and dMRI anatomical ROI variables did not impact SML model performance in this study, demonstrating the feasibility of using dMRI images. Due to the limited sample size further training of the predictive models including dMRI anatomy is recommended.
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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
48
审稿时长
67 days
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