Tran Anh T, Desser Dmitriy, Zeevi Tal, Abou Karam Gaby, Zietz Julia, Dell'Orco Andrea, Chen Min-Chiun, Malhotra Ajay, Qureshi Adnan I, Murthy Santosh B, Majidi Shahram, Falcone Guido J, Sheth Kevin N, Nawabi Jawed, Payabvash Seyedmehdi
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06519, USA.
Department of Neuroradiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, 10117 Berlin, Germany.
Appl Sci (Basel). 2025 Jan;15(1). doi: 10.3390/app15010111. Epub 2024 Dec 27.
Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team's preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation ( = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47-88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate.
血肿扩大（HE）是脑出血（ICH）预后不良的独立预测因素，也是一个可改变的治疗靶点。在大型数据集中评估HE需要在入院时和随访CT扫描上对血肿进行分割，在大规模研究中，这个过程既耗时又费力。血肿的自动分割可以加快这一进程；然而，入院时和随访扫描分割产生的累积误差会妨碍准确的HE分类。在本研究中，我们将串联深度学习分类模型与自动分割相结合，以生成假阳性HE分类的概率度量。通过这种策略，我们可以将自动血肿分割的专家审查限制在数据集的一个子集上，该子集根据研究团队偏好的敏感性或特异性阈值以及他们对假阳性与假阴性结果的容忍度进行定制。我们利用三个独立的多中心队列进行交叉验证/训练、内部测试和外部验证（=2261），以开发和测试自动血肿分割流程，并生成真实的二元HE注释（≥3、≥6、≥9和≥12.5 mL）。将95%的敏感性阈值应用于HE分类，显示出在大型ICH数据集中进行HE注释的实用且高效的策略。对于不同的HE定义，该阈值排除了自动分割专家审查中47-88%的测试阴性预测，在内部和外部验证队列中假阴性错误分类均少于2%。我们的流程提供了一种省时且可优化的方法，用于在大型ICH数据集中生成真实的HE分类，减少了自动血肿分割专家审查的负担，同时将错误分类率降至最低。
