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Guzmán Ortiz S, Hurtado Ortiz R, Jara Gavilanes A, Ávila Faican R, Parra Zambrano B. A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer. Rev Esp Med Nucl Imagen Mol 2024; 43:500003. [PMID: 38636827 DOI: 10.1016/j.remnie.2024.500003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 01/29/2024] [Indexed: 04/20/2024]
Abstract
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, recall, and F1-score. RESULTS This study showed a general performance of 94% with the Stacking method and a general performance of 77% with the Boosting method. CONCLUSIONS The Stacking method proved to be a model with high performance and quality for lung cancer detection when analyzing PET/CT images.
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Affiliation(s)
- S Guzmán Ortiz
- Servicio de Medicina Nuclear, Hospital Universitario General de Toledo, Toledo, Spain.
| | - R Hurtado Ortiz
- Grupo de investigación en Inteligencia Artificial y Tecnologías de Asistencia (GI-IATA), Universidad Politécnica Salesiana, Cuenca, Azuay, Ecuador
| | - A Jara Gavilanes
- Grupo de investigación en Inteligencia Artificial y Tecnologías de Asistencia (GI-IATA), Universidad Politécnica Salesiana, Cuenca, Azuay, Ecuador
| | - R Ávila Faican
- Grupo de investigación en Inteligencia Artificial y Tecnologías de Asistencia (GI-IATA), Universidad Politécnica Salesiana, Cuenca, Azuay, Ecuador
| | - B Parra Zambrano
- Grupo de investigación en Inteligencia Artificial y Tecnologías de Asistencia (GI-IATA), Universidad Politécnica Salesiana, Cuenca, Azuay, Ecuador
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Opposits G, Nagy M, Barta Z, Aranyi C, Szabó D, Makai A, Varga I, Galuska L, Trón L, Balkay L, Emri M. Automated procedure assessing the accuracy of HRCT-PET registration applied in functional virtual bronchoscopy. EJNMMI Res 2021; 11:69. [PMID: 34312736 PMCID: PMC8313651 DOI: 10.1186/s13550-021-00810-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/11/2021] [Indexed: 12/02/2022] Open
Abstract
Background Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix.
Results For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert’s scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist.
Conclusion A computer-aided solution was proposed in order to reduce reliance on radiologist’s contribution for the approval of acceptable image registrations.
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Affiliation(s)
- Gábor Opposits
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary.
| | - Marianna Nagy
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary.,Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - Zoltán Barta
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - Csaba Aranyi
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - Dániel Szabó
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - Attila Makai
- Department of Pulmonology, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - Imre Varga
- Department of Pulmonology, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - László Galuska
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - Lajos Trón
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - László Balkay
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - Miklós Emri
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
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