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

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.

Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease.

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.79 for the most precise model (FDR = 0.01). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Safety, Immunologic, and Clinical Activity of Durvalumab in Combination with Olaparib or Cediranib in Advanced Leiomyosarcoma: Results of the DAPPER Clinical Trial.

PURPOSE: Non-inflamed (cold) tumors such as leiomyosarcoma do not benefit from immune checkpoint blockade (ICB) monotherapy. Combining ICB with angiogenesis or PARP inhibitors may increase tumor immunogenicity by altering the immune cell composition of the tumor microenvironment (TME). The DAPPER phase II study evaluated the safety, immunologic, and clinical activity of ICB-based combinations in pretreated patients with leiomyosarcoma. PATIENTS AND METHODS: Patients were randomized to receive durvalumab 1,500 mg IV every 4 weeks with either olaparib 300 mg twice a day orally (Arm A) or cediranib 20 mg every day orally 5 days/week (Arm B) until unacceptable toxicity or disease progression. Paired tumor biopsies, serial radiologic assessments and stool collections were performed. Primary endpoints were safety and immune cell changes in the TME. Objective responses and survival were correlated with transcriptomic, radiomic, and microbiome parameters. RESULTS: Among 30 heavily pretreated patients (15 on each arm), grade ≥ 3 toxicity occurred in 3 (20%) and 2 (13%) on Arms A and B, respectively. On Arm A, 1 patient achieved partial response (PR) with increase in CD8 T cells and macrophages in the TME during treatment, while 4 had stable disease (SD) ≥ 6 months. No patients on Arm B achieved PR or SD ≥ 6 months. Transcriptome analysis showed that baseline M1-macrophage and B-cell activity were associated with overall survival. CONCLUSIONS: Durvalumab plus olaparib increased immune cell infiltration of TME with clinical benefit in some patients with leiomyosarcoma. Baseline M1-macrophage and B-cell activity may identify patients with leiomyosarcoma with favorable outcomes on immunotherapy and should be further evaluated.