Purpose: To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials: In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results: The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions: The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.

Benign prostatic hyperplasia (BPH) is a common urological condition affecting elderly men worldwide. In 2021, there were 112,502 (95% UI: 88,131.8–142,634.2) thousand prevalent cases globally, compared to 50,705.8 (95% UI: 38,735.5–65,693.4) thousand cases in 1990, representing a 122% increase. Over the past 30 years, the burden of BPH in low and low-middle socio-demographic index (SDI) regions have shown an upward trend and is projected to continue increasing over the next 15 years. Middle-SDI regions are facing the heaviest absolute burden of BPH. Despite declining prevalence, incidence, and disability-adjusted life years rates in high-middle SDI regions, the absolute BPH burden remains high, ranking second among the five SDI regions. In contrast, high-SDI regions exhibit a relatively low and stable BPH burden, though significant variations exist even among countries within the high-SDI category. Additionally, the global 65–69 age group bears the highest burden, with the 40–44 and 80+ age groups showing increasing trends. The burden of BPH varies significantly across regions, socioeconomic statuses, and countries, yet the absolute burden is generally increasing. The substantial regional differences in BPH burden underscore its widespread impact and potential controllability, indicating the need for more targeted healthcare efforts to address the growing BPH burden.

Myeloid-derived suppressor cells (MDSC) potently inhibit antitumor immune responses, and thereby promoti tumor progression and metastasis. However, the nature of human tumor-infiltrating MDSC remains poorly characterized. Here, we find B7-H3 is exclusively expressed on a subset of intratumoral CD14+HLA-DR−/low MDSC but absent from adjacent normal lung tissues of patients with non-small cell lung carcinoma (NSCLC). Cytokine analysis revealed that B7-H3+CD14+HLA-DR−/low MDSC (B7-H3+MDSC) produced higher levels of IL-10 and TNFα but lower levels of IL-1β and IL-6 when compared with B7-H3−CD14+HLA-DR−/low myeloid-derived suppressor cells (B7-H3−MDSC). In a murine lung cancer model, B7-H3+MDSCs were found only in the tumor microenvironment and their frequencies increased during tumor progression. Clinical data analysis indicated that a higher frequency of B7-H3+MDSCs was associated with reduced recurrence-free survival in patients with NSCLC. Taken together, we identify a novel subset of MDSCs within the tumor microenvironment that fosters tumor progression.

A high performance ratiometric fluorescent probe, namely TPE-RNS, has been developed for detecting exogenous and endogenous HClO/ClO⁻ in living cells and discriminating cancer cells from normal cells.

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.