# A Machine Learning Approach to Volumetric Computations of Solid Pulmonary Nodules

arXiv: https://arxiv.org/abs/2508.20127 · HTML: https://mlsanity.com/paper/2508.20127/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Towards automatic pulmonary nodule management in lung cancer screening with deep learning](https://mlsanity.com/paper/1610.09157/) (2016)
- [Neural network models and deep learning - a primer for biologists](https://mlsanity.com/paper/1902.04704/) (2019)
- [Detection-Guided Deep Learning-Based Model with Spatial Regularization for Lung Nodule Segmentation](https://mlsanity.com/paper/2410.20154/) (2024)

## Abstract (arXiv)

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.