| Autor: |
Rodrigo Delgadillo, Anthony M. Deana, John C. Ford, Matthew T. Studenski, Kyle R. Padgett, Matthew C. Abramowitz, Alan Dal Pra, Benjamin O. Spieler, Nesrin Dogan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-60281-6 |
| Popis: |
Abstract Extracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient’s anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients’ data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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