Radiomic-Based Approaches in the Multi-metastatic Setting: A Quantitative Review.

Autor:	Geady C; Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada., Patel H; Biomedical Computing, Queen's University, Kingston, Ontario, Canada., Peoples J; School of Computing, Queen's University, Kingston, Ontario, Canada., Simpson A; School of Computing, Queen's University, Kingston, Ontario, Canada.; Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada., Haibe-Kains B; Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Jul 05. Date of Electronic Publication: 2024 Jul 05.
DOI:	10.1101/2024.07.04.24309964
Abstrakt:	Background: Radiomics traditionally focuses on analyzing a single lesion within a patient to extract tumor characteristics, yet this process may overlook inter-lesion heterogeneity, particularly in the multi-metastatic setting. There is currently no established method for combining radiomic features in such settings, leading to diverse approaches with varying strengths and limitations. Our quantitative review aims to illuminate these methodologies, assess their replicability, and guide future research toward establishing best practices, offering insights into the challenges of multi-lesion radiomic analysis across diverse datasets. Methods: We conducted a comprehensive literature search to identify methods for integrating data from multiple lesions in radiomic analyses. We replicated these methods using either the author's code or by reconstructing them based on the information provided in the papers. Subsequently, we applied these identified methods to three distinct datasets, each depicting a different metastatic scenario. Results: We compared ten mathematical methods for combining radiomic features across three distinct datasets, encompassing a total of 16,850 lesions in 3,930 patients. Performance of these methods was evaluated using the Cox proportional hazards model and benchmarked against univariable analysis of total tumor volume. We observed variable performance in methods across datasets. However, no single method consistently outperformed others across all datasets. Notably, while some methods surpassed total tumor volume analysis in certain datasets, others did not. Averaging methods showed higher median performance in patients with colorectal liver metastases, and in soft tissue sarcoma, concatenation of radiomic features from different lesions exhibited the highest median performance among tested methods. Conclusions: Radiomic features can be effectively selected or combined to estimate patient-level outcomes in multi-metastatic patients, though the approach varies by metastatic setting. Our study fills a critical gap in radiomics research by examining the challenges of radiomic-based analysis in this setting. Through a comprehensive review and rigorous testing of different methods across diverse datasets representing unique metastatic scenarios, we provide valuable insights into effective radiomic analysis strategies. Competing Interests: Competing interests The authors declare that they have no competing interests.
Databáze:	MEDLINE
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