Towards automatic pulmonary nodule management in lung cancer screening with deep learning

TL;DR

This paper introduces a deep learning system that automatically classifies lung nodules in CT scans to assist in lung cancer screening, achieving high accuracy comparable to experienced radiologists.

Contribution

It presents a novel multi-stream multi-scale convolutional network that classifies nodule types directly from raw CT data without segmentation or size information.

Findings

Outperforms classical machine learning methods in nodule classification.

Achieves accuracy within inter-observer variability among radiologists.

Validated on independent screening trial data.

Abstract

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.