A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer.

作者：S. Guzmán Ortiz, R. Hurtado Ortiz, A. Jara Gavilanes, R. Ávila Faican, B. Parra Zambrano · 发表于：Revista Española de Medicina Nuclear e Imagen Molecular · 年份：2024 · DOI：10.1016/j.remnie.2024.500003 · 被引用次数：2 · 研究领域：Medicine

INTRODUCTION AND OBJECTIVES Lung cancer is the second type of cancer with the second highest incidence rate and the first with the highest mortality rate in the world. Machine learning through the analysis of imaging tests such as positron emission tomography/computed tomography (PET/CT) has become a fundamental tool for the early and accurate detection of cancer. The objective of this study was to propose an image analysis architecture (PET/CT) ordered in phases through the application of ensemble or combined machine learning methods for the early detection of lung cancer by analyzing PET/CT images. MATERIAL AND METHODS A retrospective observational study was conducted utilizing a public dataset entitled "A large-scale CT and PET/CT dataset for lung cancer diagnosis." Various imaging modalities, including CT, PET, and fused PET/CT images, were employed. The architecture or framework of this study comprised the following phases: 1. Image loading or collection, 2. Image selection, 3. Image transformation, and 4. Balancing the frequency distribution of image classes. Predictive models for lung cancer detection using PET/CT images included: a) the Stacking model, which used Random Forest and Support Vector Machine (SVM) as base models and complemented them with a logistic regression model, and b) the Boosting model, which employed the Adaptive Boosting (AdaBoost) model for comparison with the Stacking model. Quality metrics used for evaluation included accuracy, precision, rec...