用深度学习精准测算肺结节体积，提升肺癌早期筛查效率
A Machine Learning Approach to Volumetric Computations of Solid Pulmonary Nodules
- 融合多尺度3D CNN与亚型偏差修正，提升体积估计精度
- 误差仅8.0%，比现有方法降低超17个百分点，处理速度提升三倍
- 适合临床肺结节智能筛查，尤其适用于大规模影像监测
肺癌早期发现对治疗至关重要，依赖于CT扫描中肺结节体积的准确评估。传统方法如实变-肿瘤比（CTR）和球形近似受结节形状与密度变化影响，导致估测不一致。本文提出一种结合多尺度3D卷积神经网络（CNN）与亚型特异性偏差修正的先进框架，用于精确体积估算。模型在来自上海胸科医院的364例数据上训练与评估。相比人工非线性回归，平均绝对偏差仅为8.0%，单次扫描推理时间低于20秒。该方法优于现有深度学习与半自动化流程（误差通常为25至30%，处理时间超过60秒），误差降低超过17个百分点，处理速度提升三倍。研究成果为临床肺结节筛查与随访提供了高精度、高效且可扩展的工具，有望显著提升早期肺癌检测能力。
原文摘要 · Abstract (English)
Early detection of lung cancer is crucial for effective treatment and relies on accurate volumetric assessment of pulmonary nodules in CT scans. Traditional methods, such as consolidation-to-tumor ratio (CTR) and spherical approximation, are limited by inconsistent estimates due to variability in nodule shape and density. We propose an advanced framework that combines a multi-scale 3D convolutional neural network (CNN) with subtype-specific bias correction for precise volume estimation. The model was trained and evaluated on a dataset of 364 cases from Shanghai Chest Hospital. Our approach achieved a mean absolute deviation of 8.0 percent compared to manual nonlinear regression, with inference times under 20 seconds per scan. This method outperforms existing deep learning and semi-automated pipelines, which typically have errors of 25 to 30 percent and require over 60 seconds for processing. Our results show a reduction in error by over 17 percentage points and a threefold acceleration in processing speed. These advancements offer a highly accurate, efficient, and scalable tool for clinical lung nodule screening and monitoring, with promising potential for improving early lung cancer detection.
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