Background: Multimodal imaging probes have become a powerful tool for improving detection sensitivity and accuracy, which are important in disease diagnosis and treatment. Methods: In this study, novel bifunctional magnetic resonance imaging (MRI)/fluorescence probes were prepared by loading gadodiamide into fluorescent silica nanoparticles (NPs) ([email protected]@SiO₂-PEG-Ab NPs) for targeting of prostate cancer (PCa). The physicochemical characteristics, biosafety and PCa cell targeting ability of the [email protected]@SiO₂-PEG-Ab NPs were studied in vitro and in vivo. Results: The [email protected]@SiO₂-PEG-Ab NPs had a spherical morphology with a relatively uniform size distribution and demonstrated high efficiency for Gd loading. In vitro and in vivo cell-targeting experiments demonstrated a high potential for the synthesized NPs to target prostate-specific membrane antigen (PSMA) receptor-positive PCa cells, enabling MRI and fluorescence imaging. In vitro cytotoxicity assays and in vivo hematological and pathological assays showed that the prepared NPs exhibited good biological safety. Conclusion: Our study demonstrates that the synthesized [email protected]@SiO₂-PEG-Ab NPs have great potential as MRI/fluorescence contrast agents for specific identification of PSMA receptor-positive PCa cells.

Objectives: To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features. Methods: Between August 2016 and May 2021, patients with histologically proven PCa who underwent pre-operative MRI and prostate-specific antigen screening were included. The patients were grouped into different risk categories as defined by the European Association of Urology-European Association of Nuclear Medicine-European Society for Radiotherapy and Oncology-European Society of Urogenital Radiology-International Society of Geriatric Oncology guidelines. Using Artificial Intelligence Kit software, PCa regions of interest were delineated and radiomic features were extracted. Subsequently, predictable models were built by utilising five traditional ML approaches: support vector machine, logistic regression, gradient boosting decision tree, k-nearest neighbour and random forest (RF) classifiers. The classification capacity of the developed models was assessed by area under the receiver operating characteristic curve (AUC) analysis. Results: A total of 213 patients were enrolled, including 16 low-risk, 65 intermediate-risk, and 132 high-risk PCa patients. The risk stratification of PCa could be revealed by MRI radiomic features, and second-order features accounted for most of the selected features. Among the five established ML models, the RF model showed the best overall predictive performance (AUC = 0.87). After further analysis of the subgroups based on the RF model, the prediction of the high-risk group was the best (AUC = 0.89). Conclusion: This study demonstrated that the MR radiomics-based ML method could be a promising tool for predicting PCa risk stratification precisely. Advances in knowledge: The ML models have valuable prospect for accurate PCa risk assessment, which might contribute to customize treatment and surveillance strategies.

Purpose: The Ki67 index and the Gleason grade group (GGG) are vital prognostic indicators of prostate cancer (PCa). This study investigated the value of biparametric magnetic resonance imaging (bpMRI) radiomics feature-based machine learning (ML) models in predicting the Ki67 index and GGG of PCa. Methods: A total of 122 patients with pathologically proven PCa who had undergone preoperative MRI were retrospectively included. Radiomics features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Then, recursive feature elimination (RFE) was applied to remove redundant features. ML models for predicting Ki67 expression and GGG were constructed based on bpMRI and different algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN). The performances of different models were evaluated with receiver operating characteristic (ROC) analysis. In addition, a joint analysis of Ki67 expression and GGG was performed by assessing their Spearman correlation and calculating the diagnostic accuracy for both indices. Results: The ML model based on LR and ADC + T2 (LR_ADC + T2, AUC = 0.8882) performed best in predicting Ki67 expression, and ADC_wavelet-LHH_firstorder_Maximum had the highest feature weighting. The SVM_DWI + T2 (AUC = 0.9248) performed best in predicting GGG, and DWI_wavelet HLL_glcm_SumAverage had the highest feature weighting. The Ki67 and GGG exhibited a weak positive correlation (r = 0.382, p < 0.001), and LR_ADC + DWI had the highest diagnostic accuracy in predicting both (0.6230). Conclusion: The proposed ML models are suitable for predicting both Ki67 expression and GGG in PCa. This algorithm could be used to identify indolent or invasive PCa with a noninvasive, repeatable, and accurate diagnostic method.