A Machine Learning Approach to Volumetric Computations of Solid Pulmonary Nodules

• URL: http://arxiv.org/abs/2508.20127v1
• Date: Tue, 26 Aug 2025 03:36:32 GMT
• Title: A Machine Learning Approach to Volumetric Computations of Solid Pulmonary Nodules
• Authors: Yihan Zhou, Haocheng Huang, Yue Yu, Jianhui Shang,
• Abstract summary: We propose an advanced framework that combines a multi-scale 3D convolutional neural network (CNN) with subtype-specific bias correction for precise volume estimation.<n>Our results show a reduction in error by over 17 percentage points and a threefold acceleration in processing speed.
• Score: 12.415286588339901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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