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

## TL;DR

This paper presents a novel deep learning framework combining multi-scale 3D CNNs and bias correction for precise, rapid volumetric measurement of pulmonary nodules in CT scans, significantly outperforming traditional methods.

## Contribution

Introduces an advanced CNN-based method with subtype-specific bias correction for accurate lung nodule volume estimation, improving speed and accuracy over existing techniques.

## Key findings

- Achieved 8.0% mean absolute deviation in volume estimation.
- Reduced processing time to under 20 seconds per scan.
- Outperformed existing methods with over 17% error reduction.

## 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.