Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma
Radiomics, an emerging discipline integrating imaging science, computational biology, and clinical oncology, enables the extraction of quantitative biomarkers from medical images for improved diagnosis and prognosis. However, variability in imaging protocols and insufficient validation studies hinde...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/6/576 |
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| author | Rama Vasantha Adiraju Kapula Kalyani Gunnam Suryanarayana Mohammed Zakariah Abdulaziz S. Almazyad |
| author_facet | Rama Vasantha Adiraju Kapula Kalyani Gunnam Suryanarayana Mohammed Zakariah Abdulaziz S. Almazyad |
| author_sort | Rama Vasantha Adiraju |
| collection | DOAJ |
| description | Radiomics, an emerging discipline integrating imaging science, computational biology, and clinical oncology, enables the extraction of quantitative biomarkers from medical images for improved diagnosis and prognosis. However, variability in imaging protocols and insufficient validation studies hinder the clinical reliability of these biomarkers, limiting their integration into precision medicine. This study addresses these challenges by proposing an RW-ensemble method for extracting and validating radiomic features from segmented lung nodules. Using the Lung CT-Diagnosis dataset, which comprises CT images of 61 patients with segmentation annotations, nearly 38 radiomic features were extracted, incorporating texture-based features from the Grey-Level Co-occurrence Matrix (GLCM) and Grey-Level Run Length Matrix (GLRLM), as well as histogram-based features. The extracted features were validated against ground-truth data using Spearman’s correlation coefficient (SCC), demonstrating moderate to strong correlations. These findings confirm the robustness of the RW-ensemble segmentation and reinforce the potential of radiomics in enhancing diagnostic accuracy and guiding therapeutic decisions in precision oncology. Establishing the reliability and reproducibility of these features is crucial for their seamless clinical integration, ultimately advancing the role of radiomics in the diagnosis and treatment of lung adenocarcinoma. |
| format | Article |
| id | doaj-art-6c8df56f7e4f43f28f98d3830098e57a |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-6c8df56f7e4f43f28f98d3830098e57a2025-08-20T03:27:26ZengMDPI AGBioengineering2306-53542025-05-0112657610.3390/bioengineering12060576Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung AdenocarcinomaRama Vasantha Adiraju0Kapula Kalyani1Gunnam Suryanarayana2Mohammed Zakariah3Abdulaziz S. Almazyad4Department of Electronics and Communication Engineering, Aditya University, Surampalem 533437, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, Aditya University, Surampalem 533437, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, Siddhartha Academy of Higher Education (Deemed to be a University), Vijayawada 520007, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, College of Applied Studies and Community Service, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi ArabiaDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaRadiomics, an emerging discipline integrating imaging science, computational biology, and clinical oncology, enables the extraction of quantitative biomarkers from medical images for improved diagnosis and prognosis. However, variability in imaging protocols and insufficient validation studies hinder the clinical reliability of these biomarkers, limiting their integration into precision medicine. This study addresses these challenges by proposing an RW-ensemble method for extracting and validating radiomic features from segmented lung nodules. Using the Lung CT-Diagnosis dataset, which comprises CT images of 61 patients with segmentation annotations, nearly 38 radiomic features were extracted, incorporating texture-based features from the Grey-Level Co-occurrence Matrix (GLCM) and Grey-Level Run Length Matrix (GLRLM), as well as histogram-based features. The extracted features were validated against ground-truth data using Spearman’s correlation coefficient (SCC), demonstrating moderate to strong correlations. These findings confirm the robustness of the RW-ensemble segmentation and reinforce the potential of radiomics in enhancing diagnostic accuracy and guiding therapeutic decisions in precision oncology. Establishing the reliability and reproducibility of these features is crucial for their seamless clinical integration, ultimately advancing the role of radiomics in the diagnosis and treatment of lung adenocarcinoma.https://www.mdpi.com/2306-5354/12/6/576radiomicslung adenocarcinomarandom walkensemble segmentationSpearman’s correlation coefficient |
| spellingShingle | Rama Vasantha Adiraju Kapula Kalyani Gunnam Suryanarayana Mohammed Zakariah Abdulaziz S. Almazyad Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma Bioengineering radiomics lung adenocarcinoma random walk ensemble segmentation Spearman’s correlation coefficient |
| title | Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma |
| title_full | Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma |
| title_fullStr | Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma |
| title_full_unstemmed | Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma |
| title_short | Radiomics: Assessing Significance and Correlation with Ground-Truth Data in Precision Medicine in Lung Adenocarcinoma |
| title_sort | radiomics assessing significance and correlation with ground truth data in precision medicine in lung adenocarcinoma |
| topic | radiomics lung adenocarcinoma random walk ensemble segmentation Spearman’s correlation coefficient |
| url | https://www.mdpi.com/2306-5354/12/6/576 |
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