Theranostic nanoparticles ZIF-8@ICG for pH/NIR-responsive drug-release and NIR-guided chemo-phototherapy against non-small-cell lung cancer.

This study leverages nanotechnology by encapsulating indocyanine green (ICG) and paclitaxel (Tax) using zeolitic imidazolate frameworks-8 (ZIF-8) as a scaffold. This study aims to investigate the chemo-photothermal therapeutic potential of ZIF-8@ICG@Tax nanoparticles (NPs) in the treatment of non-small cell lung cancer (NSCLC). An "all-in-one" theranostic ZIF-8@ICG@Tax NPs was conducted by self-assembly based on electrostatic interaction. First, the photothermal effect, stability, pH responsiveness, drug release, and blood compatibility of ZIF-8@ICG@Tax were evaluated through in vitro testing. Furthermore, the hepatic and renal toxicity of ZIF-8@ICG@Tax were assessed through in vivo testing. Additionally, the anticancer effects of these nanoparticles were investigated both in vitro and in vivo. Uniform and stable chemo-photothermal ZIF-8@ICG@Tax NPs had been successfully synthesized and had outstanding drug releasing capacities. Moreover, ZIF-8@ICG@Tax NPs showed remarkable responsiveness dependent both on pH in the tumor microenvironment and NIR irradiation, allowing for targeted drug delivery and controlled drug release. NIR irradiation can enhance the tumor cell response to ZIF-8@ICG@Tax uptake, thereby promoting the anti-tumor growth in vitro and in vivo. ZIF-8@ICG@Tax and NIR irradiation have demonstrated remarkable synergistic anti-tumor growth properties compared to their individual components. This novel theranostic chemo-photothermal NPs hold great potential as a viable treatment option for NSCLC.

Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.

BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management. METHODS: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction. RESULTS: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively. CONCLUSIONS: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.