Morphometry-based radiomics for predicting prognosis in soft tissue sarcomas of extremities following radiotherapy

Sherminie, L.P.G., Jayatilake, M.L., Hewavithana, P.B., Weerakoon, B.S. ORCID: https://orcid.org/0000-0003-0843-6389 and Vijithananda, S.M. 2024. Morphometry-based radiomics for predicting prognosis in soft tissue sarcomas of extremities following radiotherapy. Radiography 30 (6) , pp. 1501-1507. 10.1016/j.radi.2024.09.048

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Abstract

Introduction Cancer is a leading cause of premature death worldwide. Especially cancers like soft tissue sarcomas of extremities (STSE) pose a challenge in oncologic management. Thus, the assessment of prognosis in patients with such cancers is important to select proper management strategies. Radiomics is a promising approach that has shown a wide range of potential applications including predicting prognosis. This study focused on finding out whether the morphometry-based radiomics features could be used to predict the prognosis of patients with STSE following radiotherapy. Methods The deidentified images, contours and clinical data from The Cancer Imaging Archive (TCIA) were used to evaluate thirty patients with histologically proven STSE following radiotherapy. Twenty-nine three dimensional (3D) morphometric features were extracted for each patient and the two-sample t-test (one-tailed) with the 95% confidence level was used to determine whether there was a significant difference between the patients who developed recurrence or metastasis (RM) and patients who were recurrence or metastasis-free (RMF) following radiotherapy for each morphometric feature. Results According to the findings, only surface-to-volume ratio demonstrated a significant difference (p-value of 0.029) between the RM and RMF after receiving radiotherapy for STSE. Conclusion Only surface-to-volume ratio could be utilized as a predictor for assessing the prognosis of patients with STSE following radiotherapy. Implications for practice The ability to predict the response after radiotherapy can facilitate the decision-making process, which will ultimately improve patient outcomes, especially considering the challenges in the management of STSE. This study provides insight that the integration of morphometry-based radiomics features into radiotherapy practice could be useful to evaluate the prognosis of patients who received radiotherapy for STSE.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Healthcare Sciences
Publisher:	Elsevier
ISSN:	1078-8174
Date of Acceptance:	6 September 2024
Last Modified:	31 Jan 2025 12:15
URI:	https://orca.cardiff.ac.uk/id/eprint/175624

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