使用HistoQC预测人表皮生长因子受体2 (HER2)评估的分歧

IF 2.7 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Laura Verzellesi , Moira Ragazzi , Andrea Botti , Giacomo Santandrea , Andrew Janowczyk , Luca Bottazzi , Alessandra Bisagni , Ione Tamagnini , Giorgio Gardini , Saverio Coiro , Elisa Gasparini , Mauro Iori
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引用次数: 0

摘要

人表皮生长因子受体2 (HER2)基因是乳腺癌治疗反应的重要预后和预测因素。HER2评估对靶向治疗的资格至关重要,但在HER2评估中,观察者间的可重复性是一个众所周知的问题。我们的研究目标是创建一个机器学习(ML)系统,能够检测可能导致观察者之间差异的整个幻灯片图像(wsi)。方法收集132张病理切片,采用双盲HER2评价,并将观察者之间的一致性定义为二元分类:0为不一致,1为一致。我们利用HistoQC软件根据一系列质量相关特征对病理切片进行分析和表征。使用histoqc衍生的质量度量来训练机器学习模型(XGBoost)来预测观察者之间的分歧。数据集被随机分为训练和测试,比例分别为60%/40%。结果我们的模型在训练集上进行了五次交叉验证,平均AUC为0.86(标准差,SD = 0.09),突出了其预测的可靠性。测试集上的AUC为0.81(置信区间,CI =[0.82-0.94]),强调了模型在预测未见WSI是否会导致不一致方面的准确性。我们的研究提出了一个机器学习模型,用于识别HER2病理评估中潜在的诊断分歧。结果表明病理切片质量与诊断结果之间存在相关性。经过适当的验证,我们的工具可以集成到解剖病理学部门使用的现有质量保证系统中,以改进HER2诊断过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using HistoQC to predict disagreement on human epidermal growth factor receptor 2 (HER2) assessment

Background

The human epidermal growth factor receptor 2 (HER2) gene is a significant prognostic and predictive factor for breast cancer therapy response. HER2 assessment is critical for targeted therapy eligibility, but interobserver reproducibility is a well-known issue in HER2 evaluation.

Purpose

The goal of our study is to create a machine learning (ML) system able to detect whole slide images (WSIs) that might cause discrepancies among observers.

Methods

We collected 132 pathology slides with double-blind HER2 evaluation and defined the agreement between observers as a binary classification: 0 for disagreement and 1 for agreement. We utilized HistoQC software to analyze and characterize the pathology slides based on a series of quality-related features. HistoQC-derived quality metrics were used to train a machine learning model (XGBoost) to predict interobserver disagreement. The dataset was randomly split into training and testing at proportions of 60%/40%, respectively.

Results

Our model demonstrated a mean AUC of 0.86 (standard deviation, SD = 0.09) across five cross-validation runs on the training set, highlighting its predictive reliability. The AUC on the testing set was 0.81 (confidence interval, CI = [0.82–0.94]), emphasizing the model’s precision in predicting whether an unseen WSI would lead to discordance.

Conclusions

Our study presents a machine learning model built to identify potential diagnostic disagreements in HER2 pathology evaluations. The results demonstrate a correlation between the quality of pathology slides and diagnostic outcomes. Upon proper validation, our tool could be integrated among the existing quality assurance systems used in anatomic pathology departments to improve HER2 diagnostic process.
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来源期刊
CiteScore
6.80
自引率
14.70%
发文量
493
审稿时长
78 days
期刊介绍: Physica Medica, European Journal of Medical Physics, publishing with Elsevier from 2007, provides an international forum for research and reviews on the following main topics: Medical Imaging Radiation Therapy Radiation Protection Measuring Systems and Signal Processing Education and training in Medical Physics Professional issues in Medical Physics.
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