Are radiomics features universally applicable to different organs?
Autor: | Hwan-Ho Cho, Junmo Kwon, Ho Yun Lee, Seung-Hak Lee, Hyunjin Park |
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Rok vydání: | 2021 |
Předmět: |
lcsh:Medical physics. Medical radiology. Nuclear medicine
Adult Male Organs at Risk medicine.medical_specialty Percentile lcsh:R895-920 Feature selection lcsh:RC254-282 030218 nuclear medicine & medical imaging 03 medical and health sciences Magnetic resonance imaging 0302 clinical medicine Radiomics medicine Humans Radiology Nuclear Medicine and imaging Radiometry Computed tomography Survival analysis Aged Training set Radiological and Ultrasound Technology Tumor region business.industry General Medicine Macroscale tumor features Middle Aged lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Tumor microenvironment Oncology Feature (computer vision) 030220 oncology & carcinogenesis Female Radiology business Target organ Research Article |
Zdroj: | Cancer Imaging Cancer Imaging, Vol 21, Iss 1, Pp 1-10 (2021) |
ISSN: | 1470-7330 |
Popis: | Background Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments. Methods Four datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated. Results Each organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified. Conclusion Although the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties. |
Databáze: | OpenAIRE |
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