Multimodal Radiomics Model for Predicting Gold Nanoparticles Accumulation in Mouse Tumors

TL;DR

This study develops a multimodal radiomics model that predicts gold nanoparticle accumulation in mouse tumors, addressing heterogeneity and aiding clinical translation of nanomedicine.

Contribution

The paper introduces a novel radiomics-based predictive model combining multimodal imaging data to forecast nanoparticle uptake in tumors, which was not previously available.

Findings

The model achieved high accuracy with AUC 0.93 in training and 0.78 in testing.

Tumor heterogeneity significantly affects nanoparticle accumulation.

Nanoparticle size was not a primary factor influencing uptake.

Abstract

Background: Nanoparticles can accumulate in solid tumors, serving as diagnostic or therapeutic agents for cancer. Clinical translation is challenging due to low accumulation in tumors and heterogeneity between tumor types and individuals. Tools to identify this heterogeneity and predict nanoparticle accumulation are needed. Advanced imaging techniques combined with radiomics and AI may offer a solution. Methods: 183 mice were used to create seven subcutaneous tumor models, with three sizes (15nm, 40nm, 70nm) of gold nanoparticles injected via the tail vein. Accumulation was measured using ICP-OES. Data were divided into training and test sets (7:3). Tumors were categorized into high and low uptake groups based on the median value of the training set. Before injection, multimodal imaging data (CT, B-mode ultrasound, SWE, CEUS) were acquired, and radiomics features extracted. LASSO and RFE algorithms built a radiomics signature. This, along with tumor type and mean values from CT and SWE, constructed the best model using SVM. For each tumor in the test set, the radiomics signature predicted gold nanoparticle uptake. Model performance was evaluated by AUC. Results: Significant variability in gold nanoparticle accumulation was observed among tumors (P < 0.001). The median accumulation in the training set was 3.37% ID/g. Nanoparticle size was not a main determinant of uptake (P > 0.05). The composite model based on radiomics signature outperformed the basic model in both training (AUC 0.93 vs. 0.68) and testing (0.78 vs. 0.61) datasets. Conclusion: The composite model identifies tumor heterogeneity and predicts high uptake of gold nanoparticles, improving patient stratification and supporting nanomedicine's clinical application.