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

AI Article Synopsis

  • Radiomics usually analyzes individual tumors but often misses differences between multiple tumors in patients with multiple metastases, leading to a need for new methods that can integrate data from various lesions.* -
  • A literature review was conducted to find and replicate methods for combining radiomic data from multiple lesions, applied to three different datasets involving nearly 17,000 lesions across about 4,000 patients.* -
  • Ten mathematical methods were compared for effectiveness, revealing that while no single method excelled in all scenarios, averaging methods performed better for colorectal liver metastases, and concatenation of features fared best for soft tissue sarcoma.*

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11245050PMC
http://dx.doi.org/10.1101/2024.07.04.24309964DOI Listing

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