Radiomic-based approaches in the multi-metastatic setting: a quantitative review.

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 16,894 lesions in 3,930 patients. Performance was evaluated using the Cox proportional hazards model and benchmarked against univariable analysis of total tumor volume. Results varied by dataset and lesion burden, with no single method consistently outperforming others. In colorectal liver metastases (TCIA-CRLM, 494 lesions in 197 patients), averaging methods showed the highest median performance. In soft tissue sarcoma (TH CR-406/SARC021, 1255 lesions in 545 patients), concatenating radiomic features from multiple lesions exhibited the best performance. In head and neck cancers (TCIA-RADCURE, 15,145 lesions in 3188 patients), total tumor volume remained a strong predictor. These findings highlight dataset-specific influences, including tumor type and lesion burden, on the effectiveness of radiomic feature aggregation 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.