Bladder cancer and prostate cancer are the most common malignant tumors of the genitourinary system. Conventional strategies still face great challenges of high recurrence rate and severe trauma. Therefore, minimally invasive photothermal therapy (PTT) has been extensively explored to address these challenges. Herein, fluorescent Au nanoparticles (NPs) were first prepared using glutathione as template, which were then capped with SiO₂ shell to improve the biocompatibility. Next, Au nanoclusters were deposited on the NPs surface to obtain Au@SiO₂@Au NPs for photothermal conversion. The gaps between Au nanoparticles on their surface could enhance their photothermal conversion efficiency. Finally, hyaluronic acid (HA), which targets cancer cells overexpressing CD44 receptors, was attached on the NPs surface via 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) chemistry to improve the accumulation of NPs in tumor tissues. Photothermal experiments showed that NPs with an average size of 37.5 nm have a high photothermal conversion efficiency (47.6%) and excellent photostability, thus exhibiting potential application as a PTT agent. The temperature of the NPs (100 μg·mL⁻¹) could rapidly increase to 38.5 °C within 200 s and reach the peak of 57.6 °C with the laser power density of 1.5 W·cm⁻² and irradiation time of 600 s. In vivo and in vitro PTT experiments showed that the NPs have high biocompatibility and excellent targeted photothermal ablation capability of cancer cells. Both bladder and prostate tumors disappeared at 15 and 18 d post-treatment with HA-Au@SiO₂@Au NPs, respectively, and did not recur. In summary, HA-Au@SiO₂@Au NPs can be used a powerful PTT agent for minimally invasive treatment of genitourinary tumors.

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model - Diagnostic and Interventional Radiology

Purpose: This study aims to explore the value of the delta-radiomics (DelRADx) model in predicting the invasiveness of lung adenocarcinoma manifesting as radiological part-solid nodules (PSNs).Methods: A total of 299 PSNs histopathologically confirmed as lung adenocarcinoma (training set, n = 209; validation set, n = 90) in our hospital were retrospectively analyzed from January 2017 to December 2021. All patients underwent diagnostic noncontrast-enhanced CT (NCECT) and contrast-enhanced CT (CECT) before surgery. After image preprocessing and ROI segmentation, 740 radiomic features were extracted from NCECT and CECT, respectively, resulting in 740 DelRADx. A DelRADx model was constructed using the least absolute shrinkage and selection operator logistic (LASSO-logistic) algorithm based on the training cohort. The conventional radiomics model based on NCECT was also constructed following the same process for comparison purposes. The prediction performance was assessed using area under the ROC curve (AUC). To provide an easy-to-use tool, a radiomics-based integrated nomogram was constructed and evaluated by integrated discrimination increment (IDI), calibration curves, decision curve analysis (DCA), and clinical impact plot.Results: The DelRADx signature, which consisted of nine robust selected features, showed significant differences between the AIS/MIA group and IAC group (p < 0.05) in both training and validation sets. The DelRADx signature showed a significantly higher AUC (0.902) compared to the conventional radiomics model based on NCECT (AUC = 0.856) in the validation set. The IDI was significant at 0.0769 for the integrated nomogram compared with the DelRADx signature. The calibration curve of the integrated nomogram demonstrated favorable agreement both in the training set and validation set with a mean absolute error of 0.001 and 0.019, respectively. Decision curve analysis and clinical impact plot indicated that if the threshold probability was within 90%, the integrated nomogram showed a high clinical application value.Conclusion: The DelRADx method has the potential to assist doctors in predicting the invasiveness for patients with PSNs. The integrated nomogram incorporating the DelRADx signature with the radiographic features could facilitate the performance and serve as an alternative way for determining management.