MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study.

BACKGROUND: Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. METHOD: A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. RESULTS: All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. CONCLUSIONS: The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.

Outcomes of radical prostatectomy in a 20-year localized prostate cancer single institution series in China.

The long-term survival outcomes of radical prostatectomy (RP) in Chinese prostate cancer (PCa) patients are poorly understood. We conducted a single-center, retrospective analysis of patients undergoing RP to study the prognostic value of pathological and surgical information. From April 1998 to February 2022, 782 patients undergoing RP at Queen Mary Hospital of The University of Hong Kong (Hong Kong, China) were included in our study. Multivariable Cox regression analysis and Kaplan-Meier analysis with stratification were performed. The 5-year, 10-year, and 15-year overall survival (OS) rates were 96.6%, 86.8%, and 70.6%, respectively, while the 5-year, 10-year, and 15-year PCa-specific survival (PSS) rates were 99.7%, 98.6%, and 97.8%, respectively. Surgical International Society of Urological Pathology PCa grades (ISUP Grade Group) ≥4 was significantly associated with poorer PSS (hazard ratio [HR] = 8.52, 95% confidence interval [CI]: 1.42-51.25, P = 0.02). Pathological T3 stage was not significantly associated with PSS or OS in our cohort. Lymph node invasion and extracapsular extension might be associated with worse PSS (HR = 20.30, 95% CI: 1.22-336.38, P = 0.04; and HR = 7.29, 95% CI: 1.22-43.64, P = 0.03, respectively). Different surgical approaches (open, laparoscopic, or robotic-assisted) had similar outcomes in terms of PSS and OS. In conclusion, we report the longest timespan follow-up of Chinese PCa patients after RP with different approaches.

The diagnostic value of rapid urine test platform UF-5000 for suspected urinary tract infection at the emergency department.

Background and objective: Urine culture is time consuming, which may take days to get the results and impede further timely treatment. Our objective is to evaluate whether the fast urinalysis and bacterial discrimination system called Sysmex UF-5000 may predict urinary tract infections (UTIs) (within minutes) compared with the clinical routine test in suspected UTI patients. In addition, we aimed to explore the accuracy of microbiologic information by UF-5000. Materials and Methods: Consecutive patients who were admitted from the emergency department at Queen Mary Hospital (a tertiary hospital in Hong Kong) from June 2019 to February 2020 were enrolled in the present study. The dipstick test, manual microscopic test with culture, and Sysmex UF-5000 test were performed in the urine samples at admission. Results: A total of 383 patients were finally included in the present study. UF-5000 urinalysis (area under the receiver operator characteristic curve, AUC=0.821, confidence interval, 95%CI: 0.767-0.874) outperformed the dipstick test (AUC=0.602, 95%CI: 0.550-0.654, P=1.32×10-10) for predicting UTIs in patients without prior antibiotic treatment. A significant net benefit from UF-5000 was observed compared with the dipstick test (NRI=39.9%, 95%CI: 19.4-60.4, P=1.36 × 10-4). The urine leukocyte tested by UF-5000 had similar performance (AUC) for predicting UTI compared with the manual microscopic test (P=0.27). In patients without a prior use of antibiotics, the concordance rates between UF-5000 and culture for predicting Gram-positive or -negative bacteriuria and a negative culture were 44.7% and 96.2%, respectively. Conclusions: UF-5000 urinalysis had a significantly better predictive value than the dipstick urine test for predicting UTIs.