Enhancing Pulmonary Nodule Localization Based on Latent Representations

Accurately localizing pulmonary nodules relative to other anatomical structures is crucial for disease management, guiding biopsies, and formulating effective treatment strategies. This study introduces a fully automated approach for classifying nodules detected in computed tomography (CT) images as pleural (near the pleura as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N_{p}$</tex>) or non-pleural (distant from the pleura as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D_{p}$</tex>). We propose a combination of Principal Component Analysis (PCA) and deep learning approach to determine a threshold for the optimal correlation between the original image and the latent PCA-based image reconstruction used to classify lung nodule as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N_{p}$</tex> or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D_{p}$</tex>. Applying our methodology to the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, we found that the PCA-based approach demonstrated substantial agreement with human evaluations of the proximity of the lung nodule to the pleura, achieving a Cohen's Kappa value of 0.76, outperforming an intensitybased baseline model with a Cohen's Kappa value of 0.55. Further analysis revealed significant variability in radiologists' semantic ratings for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N_{p}$</tex> versus <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D_{p}$</tex>, with the highest variability observed in the texture feature. These findings demonstrate that our approach has the potential to enhance the classification of lung nodule localization when integrated into computer-aided diagnosis (CAD) systems.