Applying radiomics in computed tomography data analysis to predict sarcopenia
DOI:
https://doi.org/10.21638/spbu10.2024.306Abstract
This article presents an algorithm implementing a radiomics approach to processing computed tomography (CT) data for diagnosing sarcopenia. The proposed method includes region of interest extraction, automatic muscle segmentation using deep learning models, extraction of radiomic features from CT-images, construction of correlation matrices, and selection of criteria for classification. The results show that the obtained radiomic parameters have a significant correlation with the presence of sarcopenia, allowing the construction of accurate classification models based on machine learning. This approach can significantly improve the diagnosis of sarcopenia, providing reliable non-invasive analysis methods.
Keywords:
radiomics, texture analysis, machine learning, sarcopenia
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References
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References
Chen Y.-C., Hsieh J.-W., Yang Y.-H., Lee C.-H., Yu P.-Y., Chen P.-Y., San A. S. Towards deep learning-based sarcopenia screening with body joint composition analysis. 2021 IEEE International Conference on Image Processing (ICIP). Anchorage, AK, USA, 2021, pp. 3807–3811. https://doi.org/10.1109/ICIP42928.2021.9506482
Chung H., Jo Y., Ryu D., Jeong C., Choe S. K., Lee J. Artificial-intelligence-driven discovery of prognostic biomarker for sarcopenia. Journal of Cachexia Sarcopenia Muscle, 2021, vol. 12, no. 6, pp. 2220–2230. https://doi.org/10.1002/jcsm.12840
Castillo-Olea C., Garcia-Zapirain S. B., Carballo L. C., Zuniga C. Automatic classification oflinebreaknewpagenoindent sarcopenia level in older adults: A case study at Tijuana General Hospital. International Journal of Environmental Research and Public Health, 2019, vol. 16, no. 18, p. 3275. https://doi.org/10.3390/ijerph16183275
Ackermans L. L. G. C., Rabou J., Basrai M., Schweinlin A., Bischoff S. C., Cussenot O., Cancel-Tassin G., Renken R. J., Gomez E., Sanchez-Gonzalez P., Rainoldi A., Boccia G., Reisinger K. W., Bosch J. A. T., Blokhuis T. J. Screening, diagnosis and monitoring of sarcopenia: When to use which tool? Clin. Nutr. ESPEN, 2022, vol. 48, pp. 36–44. https://doi.org/10.1016/j.clnesp.2022.01.027
Xie H., Gong Y., Kuang J., Yan L., Ruan G., Tang S., Gao F., Gan J. Computed tomography-determined sarcopenia is a useful imaging biomarker for predicting postoperative outcomes in elderly colorectal cancer patients. Cancer Research and Treatment, 2020, vol. 52, no. 3, pp. 957–972. https://doi.org/10.4143/crt.2019.695
Jalal M., Campbell J. A., Wadsley J., Hopper A. D. Computed tomographic sarcopenia in pancreatic cancer: Further utilization to plan patient management. Journal of Gastrointest Cancer, 2021 vol. 52, no. 3, pp. 1183–1187. https://doi.org/10.1007/s12029-021-00672-4
Smorchkova A. K., Petraikin A. V., Semenov D. S., Sharova D. E. Sarkopeniia: sovremennye podkhody k resheniiu diagnosticheskikh zadach [Sarcopenia: modern approaches to solving diagnosis problems]. Digital Diagnostics, 2022, vol. 3, no. 3, pp. 196–211. https://doi.org/10.17816/DD110721 (In Russian)
Ueki H., Hara T., Okamura Y., Bando Y., Terakawa T., Furukawa J., Harada K., Nakano Y., Fujisawa M. Association between sarcopenia based on psoas muscle index and the response to nivolumab in metastatic renal cell carcinoma: A retrospective study. Investig. Clin. Urol., 2022, vol. 63, no. 4, pp. 415–424. https://doi.org/10.4111/icu.20220028
Kim S., Kim T.-H., Jeong C.-W., Lee C., Noh S., Kim J. E., Yoon K.-H. Development of quantification software for evaluating body composition contents and its clinical application in sarcopenic obesity. Scientific Reports, 2020, vol. 10, art. no. 10452. https://doi.org/10.1038/s41598-020-67461-0
Chicklore S., Goh V., Siddique M., Roy A., Marsden P. K., Cook G. J. R. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. European Journal of Nuclear Medicine and Molecular Imaging, 2012, vol. 40, no. 1, pp. 133–140. https://doi.org/10.1007/s00259-012-2247-0
Cook G., Siddique M., Taylor B., Yip C., Chicklore S., Goh V. Radiomics in PET: principles and applications. Clinical and Translational Imaging, 2014, vol. 2, no. 3, pp. 269–276. https://doi.org/10.1007/s40336-014-0064-0
Schmidt I., Kotina E., Buev P. Deep learning muscle segmentation model for CT images in DICOM format. Cybernetics and Physics, 2023, vol. 12, no. 3, pp. 201–206. https://doi.org/10.35470/2226-4116-2023-12-3-201-206
Shmidt I. A., Kotina E. D., Kamyshanskaya I. G., Makarenko B. G. Radiomika v issledovanii sarkopenii po KT izobrazheniiam [Radiomics in the study of sarcopenia using CT images]. Diagnostic and Interventional Radiology, 2024, vol. 18, no. S2.1, pp. 94–99. (In Russian)
Shmidt Y. A., Kotina E. D., Kamyshanskaya I. G., Makarenko B. G. Application of radiomics criteria in the study of sarcopenia based on abdominal computed tomography data. Diagnostic Radiology and Radiotherapy, 2024, vol. S(15), pp. 195–196. Print 2079-5343.
Islam S., Kanavati F., Arain Z., Costa O. F. D., Crum W., Aboagye E. O., Rockall A. G. Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment. Clinical Radiology, 2022, vol. 77, no. 5, pp. e363–e371. https://doi.org/10.1016/j.crad.2022.01.036
Ha J., Park T., Kim H.-K., Shin Y., Ko Y., Kim D. W., Sung Y. S., Lee J., Ham S. J., Khang S., Jeong H., Koo K., Lee J., Kim K. W. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Scientific Reports, 2021, vol. 11, no. 1, p. 21656. https://doi.org/10.1038/s41598-021-00161-5
Zwanenburg A., Leger S., Vallieres M., Löck S. Image biomarker standardisation initiative. arXiv preprint, arXiv: 1612.07003. 2016.
Löfstedt T., Brynolfsson P., Asklund T., Nyholm T., Garpebring A. Gray-level invariant Haralick texture features. PLoS One, 2019, vol. 14, no. 2, p. e0212110. https://doi.org/10.1371/journal.pone.0212110
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Articles of "Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes" are open access distributed under the terms of the License Agreement with Saint Petersburg State University, which permits to the authors unrestricted distribution and self-archiving free of charge.
