Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

Abstract

Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.

Keywords: Artificial intelligence (AI); machine learning (ML); pulmonary nodule.

Figure 1

Euler diagram demonstrating the AI hierarchy. ML is a subfield of AI where algorithms can recognize and learn patterns using complex data sets to produce without explicit programming. DL can be conceptualized as a class of ML where algorithms are organized into many processing layers based on artificial neural networks, similar to the human brain. CNN is the type of DL model most commonly used for medical imaging presently. AI, artificial intelligence; ML, machine learning; DL, deep learning; CNN, convolutional neural network.

Figure 2

Schematic representation of a convolutional neural network containing an input layer, three hidden connected layers, and an output layer. The computational strength of such network lies in the integration of multiple “neurons” (represented by the circles) within the deep hidden layers between the input and output layers. Typically the outputs of one layer serve as the input of the next layer.

Figure 3

General illustration of a feature based machine learning nodule classification and risk stratification model [modified from Computer-Aided Nodule Assessment and Risk Yield (CANARY), Mayo Clinic, Rochester, MN, USA]. (A) Nodule Classification. After nodule segmentation, radiomic features are extracted from the images. Following feature-pathology correlation and feature selection, the machine learning model is trained to classify pulmonary nodules. There is typically a validation step with a separate set of data to further refine the algorithm before final testing prior to use. (B) Risk stratification. Multiple nodules with features representative of the population and the corresponding survival data are used to train the algorithm, which in turn classifies the features into three main groups in the case of CANARY and performs survival analysis of each group of nodules (the orange group has good, the green group has intermediate and the purple group has poor prognosis).