OBJECTIVE: To evaluate the difference in the diagnostic efficacy of 18F-PSMA-1007 PET/CT and pelvic MRI in primary prostate cancer, as well as the correlation between the two methods and histopathological parameters and serum PSA levels.
METHODS: A total of 41 patients with suspected prostate cancer who underwent 18F-PSMA-1007 PET/CT imaging in our department from 2018 to 2023 were retrospectively collected. All patients underwent 18F-PSMA-1007 PET/CT and MRI scans. The sensitivity, PPV and diagnostic accuracy of MRI and 18F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were calculated after comparing the results of MRI and 18F-PSMA-1007 PET/CT with biopsy. The Spearman test was used to calculate the correlation between 18F-PSMA-1007 PET/CT, MRI parameters, histopathological indicators, and serum PSA levels.
RESULTS: Compared with histopathological results, the sensitivity, PPV and diagnostic accuracy of 18F-PSMA-1007 PET/CT in the diagnosis of prostate cancer were 95.1%, 100.0% and 95.1%, respectively. The sensitivity, PPV and diagnostic accuracy of MRI in the diagnosis of prostate cancer were 82.9%, 100.0% and 82.9%, respectively. There was a mild to moderately positive correlation between Gleason (Gs) score, Ki-67 index, serum PSA level and 18F-PSMA-1007 PET/CT parameters (p < 0.05). There was a moderately negative correlation between the expression of AMACR (P504S) and 18F-PSMA-1007 PET/CT parameters (p < 0.05). The serum PSA level and the Gs score were moderately positively correlated with the MRI parameters (p < 0.05). There was no correlation between histopathological parameters and MRI parameters (p > 0.05).
CONCLUSIONS: Compared with MRI, 18F-PSMA-1007 PET/CT has higher sensitivity and diagnostic accuracy in the detection of malignant prostate tumors. In addition, the Ki-67 index and AMACR (P504S) expression were only correlated with 18F-PSMA-1007 PET/CT parameters. Gs score and serum PSA level were correlated with 18F-PSMA-1007 PET/CT and MRI parameters. 18F-PSMA-1007 PET/CT examination can provide certain reference values for the clinical diagnosis, evaluation, and treatment of malignant prostate tumors.

BACKGROUND: This work aimed to investigate the potential role of abnormal lipid metabolism in the development of prostate cancer (PCa).
METHODS: A retrospective study design was used. The clinical data of 520 patients who underwent rectal prostate biopsy in our hospital from January 2020 to June 2023 were analysed. The patients were enrolled and divided into the anterior PCa group including 112 patients and benign prostatic hyperplasia (BPH) group including 408 patients. Univariate and multivariate logistic regression analyses were performed for the two patient groups, and further comparisons were made according to the Gleason score and TNM staging.
RESULTS: Low-density lipoprotein cholesterol (LDL-C) level may be an independent risk factor for PCa, and it was significantly associated with the risk of PCa (odds ratio (OR) = 1.363, p = 0.030). Patients with PCa were further divided into the low risk group and the high risk group according to the Gleason score. Univariate analysis (p = 0.047) and logistic regression analysis (OR = 2.249, p = 0.036) revealed that LDL-C was a significant factor influencing the Gleason score. Patients with PCa were categorised into four groups based on TNM staging. One-way analysis of variance (ANOVA) analysis (p = 0.015) and ordinal logistic regression analysis (OR = 2.414, p = 0.007) demonstrated that LDL-C was a significant factor influencing TNM staging.
CONCLUSIONS: This study revealed the important role of LDL-C in the development of PCa, highlighting its influence as an independent risk factor. Thus, LDL-C may promote the proliferation and invasion of PCa cells.

OBJECTIVE: The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.
METHODS: A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.
RESULTS: The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.
CONCLUSIONS: MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.

Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: -0.89% (PTV76D98%) and -2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and -1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation.

OBJECTIVE: We aimed to investigate the association between metabolic syndrome (MetS) and prostate cancer (PCa) in patients undergoing prostate biopsy.
METHODS: Between January 2018 and December 2022, MetS was investigated according to Adult Treatment Panel III (ATP III) criteria in men who underwent prostate biopsy with transrectal ultrasound (TRUS). Clinicopathological factors such as, digital rectal examination (DRE), prostate-specific antigen (PSA), prostate volume, waist circumference, body mass index (BMI), age, blood pressure, testosterone, lipid profiles, fasting blood glucose level, C-reactive protein (CRP) and MetS were analyzed.
RESULTS: A total of 908 men underwent biopsies, of which 492 (51.5%) had MetS according to ATP III criteria. The number of patients diagnosed with PCa in biopsy was 270 (29.7%). PCa cases were significantly older, with a lower prostate volume and a higher PSA value and higher blood pressure compared to patients without PCa (p < 0.001). 146 of 416 (35.0%) patients with MetS had PCa while 124 of 492 (25.2%) patients without MetS had PCa (p < 0.001). Out of 270 patients with PCa, 174 (64.4%) had Gleason score <7 and 96 (35.6%) had Gleason score ≥7. In patients with a Gleason score ≥7, PSA, DRE(+) and core positive number were significantly higher compared to patients with Gleason score <7, while glycemia and high-density lipoprotein (HDL) cholesterol levels were significantly lower (p < 0.001). Multivariate analysis showed that age, PSA, positive DRE, prostate volume (p < 0.001), diastolic blood pressure, CRP and MetS were the only independent parameters associated with a higher risk of cancer on biopsy (p < 0.05).
CONCLUSIONS: Our findings show that MetS is associated with PCa diagnosed on biopsy but not with the Gleason score and the number of cancer-positive cores. However, these results should be confirmed by larger, multicenter and prospective studies.

OBJECTIVE: Prostate cancer (PCa) with high Ki-67 expression and high Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures.
METHODS: We collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan-Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival.
RESULTS: The hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI 0.681,0.930) and 0.793 (95% CI 0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model\'s clinical usefulness was verified using DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS).
CONCLUSIONS: Both Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision-making.

OBJECTIVE: To compare the results of Gleason Grade Group (GGG) classification following central pathology review with previous local pathology assessment, and to examine the difference between using overall and worst GGG in a large patient cohort treated with radiotherapy and short-course hormone therapy.
METHODS: Patients with low- to high-risk localized prostate cancer were randomized into the multicentre CHHiP fractionation trial between 2002 and 2011. Patients received short-course hormone therapy (≤6 month) and radical intensity-modulated radiotherapy (IMRT). Of 2749 consented patients, 1875 had adequate diagnostic biopsy tissue for blinded central pathology review. The median follow-up was 9.3 years. Agreement between local pathology and central pathology-derived GGG and between central pathology-derived overall and worst GGG was assessed using kappa (κ) statistics. Multivariate Cox regression and Kaplan-Meier methods were used to compare the biochemical/clinical failure (BCF) and distant metastases (DM) outcomes of patients with GGG 1-5.
RESULTS: There was poor agreement between local pathology- and central pathology-derived GGG (κ = 0.19) but good agreement between overall and worst GGG on central pathology review (κ = 0.89). Central pathology-derived GGG stratified BCF and DM outcomes better than local pathology, while overall and worst GGG on central pathology review performed similarly. GGG 3 segregated with GGG 4 for BCF, with BCF-free rates of 90%, 82%, 74%, 71% and 58% for GGGs 1-5, respectively, at 8 years when assessed using overall GGG. There was a progressive decrease in DM-free rates from 98%, 96%, 92%, 88% and 83% for GGGs 1-5, respectively, at 8 years with overall GGG. Patients (n = 57) who were upgraded from GGG 2-3 using worst GS had BCF-free and DM-free rates of 74% and 92% at 8 years. CHHiP eligibility criteria limit the interpretation of these results.
CONCLUSIONS: Contemporary review of International Society of Urological Pathology GGG successfully stratified patients treated with short-course hormone therapy and IMRT with regard to both BCF-free and DM-free outcomes. Patients upgraded from GGG 2 to GGG 3 using worst biopsy GS segregate with GGG 3 on long-term follow-up. We recommend that both overall and worst GS be used to derive GGG.

In clinical routine, the quality of whole-slide images plays a key role in the pathologist\'s diagnosis, and suboptimal staining may be a limiting factor. The stain normalization process helps to solve this problem through the standardization of color appearance of a source image with respect to a target image with optimal chromatic features. The analysis is focused on the evaluation of the following parameters assessed by two experts on original and normalized slides: (i) perceived color quality, (ii) diagnosis for the patient, (iii) diagnostic confidence and (iv) time required for diagnosis. Results show a statistically significant increase in color quality in the normalized images for both experts (p < 0.0001). Regarding prostate cancer assessment, the average times for diagnosis are significantly lower for normalized images than original ones (first expert: 69.9 s vs. 77.9 s with p < 0.0001; second expert: 37.4 s vs. 52.7 s with p < 0.0001), and at the same time, a statistically significant increase in diagnostic confidence is proven. The improvement of poor-quality images and greater clarity of diagnostically important details in normalized slides demonstrate the potential of stain normalization in the routine practice of prostate cancer assessment.

UNASSIGNED: The extent and survival benefits of lymph node dissection (LND) in radical prostatectomy (RP) for pN1M0 prostate cancer (PCa) patients remained unclear and were controversial. This study aimed to determine the survival benefit of different lymph node yields in RP for pN1M0 PCa patients.
UNASSIGNED: pN1M0 PCa patients who received RP and LND were identified in Surveillance Epidemiology and End Results (SEER) (2010-2015). Patients were divided into two groups in SEER based on the removal of one to three regional lymph nodes (LND1 group) or four or more regional lymph nodes (LND4 group). Kaplan-Meier methods were used to calculate cancer-specific survival (CSS) and overall survival (OS).
UNASSIGNED: In total, 2,200 patients were identified; 264 patients received LND1 and 1,936 patients received LND4. CSS had no significant difference between the LND4 and LND1 groups (101mon vs. 98mon, p = 0.064), and OS was higher in LND4 patients compared with LND1 patients (97mon vs. 93mon, p = 0.024); for patients with Gleason score = 9 or 10 and T3b or T4, 5-year OS was higher in patients undergoing LND4 (80.9%; 95% CI, 79.0-82.8) compared with those undergoing LND1 (67.5%; 95% CI, 60.8-74.2) (p = 0.009).
UNASSIGNED: More lymph node yield provided better survival for patients with Gleason score = 9 or 10 and T3b or T4, but not for other pN1M0 PCa patients. The extent of LND would be determined after a comprehensive evaluation including Gleason score, tumor stage, and the general condition of the patient.

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