UNASSIGNED: Prostate cancer (PCa) is the most common cancer in men. High-risk PCa is associated with an increased risk of PCa-related death. The combined use of androgen deprivation therapy (ADT) is essential to improve oncological outcomes in patients with high-risk PCa, and relatively long-term ADT administration is preferred when radiotherapy is performed. Meanwhile, whether neoadjuvant therapy for radical prostatectomy (RP) improves oncological outcomes remains controversial. This study aimed to review the oncological outcomes of RP in high-risk PCa and emphasize the significance of neoadjuvant therapy including neoadjuvant hormonal therapy (NHT) and neoadjuvant chemohormonal therapy (NCHT) followed by RP for managing high-risk PCa.
UNASSIGNED: We searched for articles published in the PubMed and Scopus databases from January 1, 2005 to March 30, 2023 using the medical subject headings (MeSH) terms: prostate cancer, prostatectomy, radiation therapy, neoadjuvant therapy, and treatment outcome.
UNASSIGNED: The study on NHT before RP for high-risk PCa found that NHT was associated with reduced adverse pathological features, such as pT3, positive surgical margins (PSM), and lymph node involvement. However, despite shorter operative times and improved surgical outcomes, NHT did not significantly enhance biochemical recurrence (BCR) or other oncological outcomes. The combination therapy using ADT and androgen receptor signaling inhibitors (ARSI) showed varying results. Another investigation explored NCHT with taxane-based agents, indicating acceptable treatment benefits and improved BCR-free survival rates in high-risk PCa patients, demonstrating potential feasibility for this approach. Ongoing trials, like the PROTEUS trial, aim to further evaluate the therapeutic efficacy of neoadjuvant therapy in high-risk PCa.
UNASSIGNED: NHT for high-risk PCa does not contribute to improved oncological outcome and should not be administered easily for downstaging or PSM reduction. NHT in combination with ARSI has the potential advantage of improving the oncological outcome of high-risk PCa compared to RP alone, but the results are currently unsatisfactory, and the development of individualized treatment strategies using several different therapeutic approaches is needed.

UNASSIGNED: Synthetic magnetic resonance imaging (SyMRI) is a fast, standardized, and robust novel quantitative technique that has the potential to circumvent the subjectivity of interpretation in prostate multiparametric magnetic resonance imaging (mpMRI) and the limitations of existing MRI quantification techniques. Our study aimed to evaluate the potential utility of SyMRI in the diagnosis and aggressiveness assessment of prostate cancer (PCA).
UNASSIGNED: We retrospectively analyzed 309 patients with suspected PCA who had undergone mpMRI and SyMRI, and pathologic results were obtained by biopsy or PCA radical prostatectomy (RP). Pathological types were classified as PCA, benign prostatic hyperplasia (BPH), or peripheral zone (PZ) inflammation. According to the Gleason Score (GS), PCA was divided into groups of intermediate-to-high risk (GS ≥4+3) and low-risk (GS ≤3+4). Patients with biopsy-confirmed low-risk PCA were further divided into upgraded and nonupgraded groups based on the GS changes of the RP results. The values of the apparent diffusion coefficient (ADC), T1, T2 and proton density (PD) of these lesions were measured on ADC and SyMRI parameter maps by two physicians; these values were compared between PCA and BPH or inflammation, between the intermediate-to-high-risk and low-risk PCA groups, and between the upgraded and nonupgraded PCA groups. The risk factors affecting GS grades were identified via univariate analysis. The effects of confounding factors were excluded through multivariate logistic regression analysis, and independent predictive factors were calculated. Subsequently, the ADC+Sy(T2+PD) combined models for predicting PCA risk grade or GS upgrade were constructed through data processing analysis. The diagnostic performance of each parameter and the ADC+Sy(T2+PD) model was analyzed. The calibration curve was calculated by the bootstrapping internal validation method (200 bootstrap resamples).
UNASSIGNED: The T1, T2, and PD values of PCA were significantly lower than those of BPH or inflammation (P≤0.001) in both the PZ or transitional zone. Among the 178 patients with PCA, intermediate-to-high-risk PCA group had significantly higher T1, T2, and PD values but lower ADC values compared with the low-risk group (P<0.05), and the diagnostic efficacy of each single parameter was similar (P>0.05). The ADC+Sy(T2+PD) model showed the best performance, with an area under the curve (AUC) 0.110 [AUC =0.818; 95% confidence interval (CI): 0.754-0.872] higher than that of ADC alone (AUC =0.708; 95% CI: 0.635-0.774) (P=0.003). Among the 68 patients initially classified as PCA in the low-risk group by biopsy, PCA in the postoperative upgraded GS group had significantly higher T1, T2, and PD values but lower ADC values than did those in the nonupgraded group (P<0.01). In addition, the ADC+Sy(T2+PD) model better predicted the upgrade of GS, with a significant increase in AUC of 0.204 (AUC =0.947; 95% CI: 0.864-0.987) compared with ADC alone (AUC =0.743; 95% CI: 0.622-0.841) (P<0.001).
UNASSIGNED: Quantitative parameters (T1, T2, and PD) derived from SyMRI can help differentiate PCA from non-PCA. Combining SyMRI parameters with ADC significantly improved the ability to differentiate between intermediate-to-high risk PCA from low-risk PCA and could predict the upgrade of low-risk PCA as confirmed by biopsy.

OBJECTIVE: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients.
METHODS: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared.
RESULTS: This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences.
CONCLUSIONS: Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

暂无摘要。

UNASSIGNED: In 2012 the United States Preventative Services Task Force (USPSTF) changed its prostate-specific antigen (PSA) screening recommendation to a category \"D\". The purpose of this study is to examine racial, ethnic, and socioeconomic differences in risk of presentation with metastatic prostate cancer (mPCa) at time of diagnosis before and after the 2012 USPSTF category \"D\" recommendation.
UNASSIGNED: This is a population-based cohort study. We identified patients with mPCa at diagnosis within the National Cancer Database from 2004-2017. Logistic regression models were used to examine associations of mPCa with age, race, ethnicity, geographic location, education level, income, and insurance status. Linear regression models assuming underlying binomial distribution were fitted to annual percentage of mPCa at diagnosis for years 2012-2017 to evaluate the post category \"D\" recommendation era.
UNASSIGNED: From 2004 to 2017, 88,987 patients presented with mPCa. A higher percentage of mPCa was noted post-USPSTF category \"D\" recommendation, with a disproportionately greater increase observed among Hispanics and non-Hispanic Blacks [Δslope/year: Hispanics (0.0092), non-Hispanic Blacks (0.0073) and non-Hispanic Whites (0.0070)]. Insurance status impacts race/ethnicity differently: uninsured Hispanics were 3.66 times more likely to present with mPCa than insured Hispanics, while uninsured non-Hispanic Blacks were 2.62 times more likely to present with mPCa than insured non-Hispanic Blacks. Household income appears to be associated with differences in mPCa, particularly among non-Hispanic Blacks. Those earning <$30,000 were more likely to present with mPCa compared to higher income brackets.
UNASSIGNED: Since the USPSTF grade \"D\" recommendation against PSA screening, the percentage of mPCa at diagnosis has increased, with a higher rate of increase among Hispanic and non-Hispanic Blacks compared to non-Hispanic Whites.

Prostate cancer (PCa) is one of the most common malignant epithelial tumors in men worldwide. PCa patients are initially sensitive to chemotherapy, but patients in the advanced stages of PCa eventually develop resistance, leaving them with limited therapeutic options. Therefore, it is very important to screen new drugs for treating PCa. Salvia miltiorrhiza is a common Chinese herbal medicine used in some Asian countries. It has many functions and is widely used to treat a variety of diseases, including heart diseases and cancers. For the past few years, research has shown that liposoluble constituents of tanshinones (TANs), including cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I, exhibit good anticancer activity in PCa. In this study, we review the progress of TAN compounds (cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I) in treating PCa over the past decade. These compounds can act on the same molecular mechanisms, as they have a very similar structure; they are also found to work slightly differently in PCa. According to current studies, compared with other TAN compounds, TAN IIA appears to hold more potential for treating PCa. The toxicity, side effects or biodistribution of Salvia miltiorrhiza and these four TANs need to be confirmed with further research. Findings obtained in this study may provide important information for the potential clinical application of cryptotanshinone, TAN IIA, dihydrotanshinone I, and TAN I in the treatment of PCa.

UNASSIGNED: Methyltransferase-like (METTL) plays an important role in various biological processes, but its role in prostate cancer (PCa) is still unclear. This study aimed to explore the mechanism by which methyltransferase-like 14 (METTL14) inhibits the physiological activity of PCa cells by increasing the N6-methyladenosine (m6A) modification of cyclin-dependent kinase 4 (CDK4).
UNASSIGNED: Clinical samples were collected for bioinformatics analysis. A PCa mouse model was constructed. Cell counting kit-8 (CCK-8), flow cytometry, colony formation assays, scratch assays, Transwell assays, real-time quantitative polymerase chain reaction (RT-qPCR), immunofluorescence and western blotting were used to detect the corresponding indicators.
UNASSIGNED: METTL14 was found to be beneficial to inhibit the proliferation, invasion, and migration of PCa cells. When the m6A RNA increased, the half-life of CDK4 mRNA decreased after oe-METTL14 (overexpression of METTL14). Overexpression of CDK4 reversed the effect of oe-METTL14. Coimmunoprecipitation experiments revealed there were interactions between CDK4 and forkhead box M1 (FOXM1). Transfection of si-CDK4 was similar to transfection of oe-METTL14. After transfection with oe-FOXM1, the invasion and migration ability of cells increased, and cell apoptosis decreased. After transfection with si-FOXM1 alone, autophagy related 7 (ATG7) expression was significantly downregulated, and autophagy levels were reduced. The overexpression of ATG7 reversed the effect of si-FOXM1. The tumor volume and weight of the oe-METTL14 group mice were significantly reduced, and tumor proliferation was decreased in comparison to untreated tumor-bearing mice.
UNASSIGNED: METTL14 inhibits the invasion and migration of PCa cells and induces cell apoptosis by inhibiting CDK4 stability and FOXM1/ATG7-mediated autophagy.

This review investigates the complex landscape of secondary bladder cancer (SBC) after radiotherapy for prostate cancer (PCa). External beam radiotherapy (EBRT) poses an increased risk for SBC, while brachytherapy seems to be associated with smaller increased risks for SBC due to its targeted radiation delivery, sparing the surrounding bladder tissue. Secondary cancers in the bladder are the most frequently diagnosed secondary cancers in the PCa patient population treated with radiotherapy. Patient-related factors are pivotal, with age emerging as a dual-edged factor. While advanced age is a recognized risk for bladder cancer, younger PCa patients exhibit higher susceptibility to radiation-induced cancers. Smoking, a well-established bladder cancer risk factor, increases this vulnerability. Studies highlight the synergistic effect of smoking and radiation exposure, amplifying the likelihood of genetic mutations and SBC. The latency period of SBC, which spans years to decades, remains a critical aspect. There is a strong dose-response relationship between radiation exposure and SBC risk, with higher doses consistently being associated with a higher SBC risk. While specific models for therapeutic radiation-induced SBC are lacking, insights from related studies, like the Atomic Bomb survivor research, emphasize the bladder\'s sensitivity to radiation-induced cancer. Chemotherapy in combination with radiotherapy, although infrequently used in PCa, emerges as a potential risk for bladder cancer. Bladder cancer\'s complex epidemiology, encompassing risk factors, treatment modalities, and cancer types, provides a comprehensive backdrop. As research refines understanding, we hope that this review contributes to guide clinicians, inform patient care, and shape preventive strategies on SBC.

UNASSIGNED: The aggressiveness of prostate cancer (PCa) is crucial in determining treatment method. The purpose of this study was to establish a 2.5-dimensional (2.5D) deep transfer learning (DTL) detection model for the automatic detection of clinically significant PCa (csPCa) based on bi-parametric magnetic resonance imaging (bp-MRI).
UNASSIGNED: A total of 231 patients, including 181 with csPCa and 50 with non-clinically significant PCa (non-csPCa), were enrolled. Stratified random sampling was then employed to divide all participants into a training set [185] and a test set [46]. The DTL model was obtained through image acquisition, image segmentation, and model construction. Finally, the diagnostic performance of the 2.5D and 2-dimensional (2D) models in predicting the aggressiveness of PCa was evaluated and compared using receiver operating characteristic (ROC) curves.
UNASSIGNED: DTL models based on 2D and 2.5D segmentation were established and validated to assess the aggressiveness of PCa. The results demonstrated that the diagnostic efficiency of the DTL model based on 2.5D was superior to that of the 2D model, regardless of whether in a single or combined sequence. Particularly, the 2.5D combined model outperformed other models in differentiating csPCa from non-csPCa. The area under the curve (AUC) values for the 2.5D combined model in the training and test sets were 0.960 and 0.949, respectively. Furthermore, the T2-weighted imaging (T2WI) model showed superiority over the apparent diffusion coefficient (ADC) model, but was not as effective as the combined model, whether based on 2.5D or 2D.
UNASSIGNED: A DTL model based on 2.5D segmentation was developed to automatically evaluate PCa aggressiveness on bp-MRI, improving the diagnostic performance of the 2D model. The results indicated that the continuous information between adjacent layers can enhance the detection rate of lesions and reduce the misjudgment rate based on the DTL model.

Prostate cancer is a significant health concern with high mortality rates and substantial economic impact. Early detection plays a crucial role in improving patient outcomes. This study introduces a non-invasive computer-aided diagnosis (CAD) system that leverages intravoxel incoherent motion (IVIM) parameters for the detection and diagnosis of prostate cancer (PCa). IVIM imaging enables the differentiation of water molecule diffusion within capillaries and outside vessels, offering valuable insights into tumor characteristics. The proposed approach utilizes a two-step segmentation approach through the use of three U-Net architectures for extracting tumor-containing regions of interest (ROIs) from the segmented images. The performance of the CAD system is thoroughly evaluated, considering the optimal classifier and IVIM parameters for differentiation and comparing the diagnostic value of IVIM parameters with the commonly used apparent diffusion coefficient (ADC). The results demonstrate that the combination of central zone (CZ) and peripheral zone (PZ) features with the Random Forest Classifier (RFC) yields the best performance. The CAD system achieves an accuracy of 84.08% and a balanced accuracy of 82.60%. This combination showcases high sensitivity (93.24%) and reasonable specificity (71.96%), along with good precision (81.48%) and F1 score (86.96%). These findings highlight the effectiveness of the proposed CAD system in accurately segmenting and diagnosing PCa. This study represents a significant advancement in non-invasive methods for early detection and diagnosis of PCa, showcasing the potential of IVIM parameters in combination with machine learning techniques. This developed solution has the potential to revolutionize PCa diagnosis, leading to improved patient outcomes and reduced healthcare costs.