(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.

Background and Objectives: Over decades, prostate cancer (PCa) has become one of the leading causes of cancer mortality in men. Extensive evidence exists that microRNAs (miRNAs or miRs) are key players in PCa and a new class of non-invasive cancer biomarkers. Materials and Methods: We performed miRNA profiling in plasma and tissues of PCa patients and attempted the validation of candidate individual miRs as biomarkers. Results: The comparison of tissue and plasma profiling results revealed five commonly dysregulated miRs, namely, miR-130a-3p, miR-145-5p, miR-148a-3p, miR-150-5p, and miR-365a-3p, of which only three show concordant changes—miR-130a-3p and miR-150-5p were downregulated and miR-148a-3p was upregulated in both tissue and plasma samples, respectively. MiR-150-5p was validated as significantly downregulated in both plasma and tissue cancer samples, with a fold change of −2.697 (p < 0.001), and −1.693 (p = 0.035), respectively. ROC analysis showed an area under the curve (AUC) of 0.817 (95% CI: 0.680–0.995) for plasma samples and 0.809 (95% CI: 0.616–1.001) for tissue samples. Conclusions: We provide data indicating that miR-150-5p plasma variations in PCa patients are associated with concordant changes in prostate cancer tissues; however, given the heterogeneous nature of previous findings of miR-150-5p expression in PCa cells, additional future studies of a larger sample size are warranted in order to confirm the biomarker potential and role of miRNA-150-5p in PCa biology.

Introduction: A renal biopsy represents the gold standard in the diagnosis, prognosis, and management of patients with glomerulonephritis. So far, non-invasive elastographic techniques have not confirmed their utility in replacing a biopsy; however, the new and improved software from Hologic Supersonic Mach 30 is a promising method for assessing the renal tissue’s stiffness and viscosity. We investigated whether this elastography technique could reveal renal tissue fibrosis in patients with chronic glomerulonephritis. Materials and methods: Two-dimensional-shear wave elastography (SWE) PLUS and viscosity plane-wave ultrasound (Vi PLUS) assessments were performed in 40 patients with chronic glomerulopathies before being referred for a renal biopsy. For each kidney, the mean values of five stiffness and viscosity measures were compared with the demographic, biological, and histopathological parameters of the patients. Results: In total, 26 men and 14 women with a mean age of 52.35 ± 15.54 years, a mean estimated glomerular filtration rate (eGFR) of 53.8 ± 35.49 mL/min/1.73m², and a mean proteinuria of 6.39 ± 7.42 g/24 h were included after providing their informed consent. Out of 40 kidney biopsies, 2 were uninterpretable with inappropriate material and were divided into four subgroups based on their fibrosis percentage. Even though these elastography techniques were unable to differentiate between separate fibrosis stages, when predicting between the fibrosis and no-fibrosis group, we found a cut-off value of <20.77 kPa with the area under the curve (AUC) of 0.860, a p < 0.001 with 88.89% sensitivity, and a 75% specificity for the 2D SWE PLUS measures and a cut-off value of <2.8 Pa.s with an AUC of 0.792, a p < 0.001 with 94% sensitivity, and a 60% specificity for the Vi PLUS measures. We also found a cut-off value of <19.75 kPa for the 2D SWE PLUS measures (with an AUC of 0.789, p = 0.0001 with 100% sensitivity, and a 74.29% specificity) and a cut-off value of <1.28 Pa.s for the Vi PLUS measures (with an AUC 0.829, p = 0.0019 with 60% sensitivity, and a 94.29% specificity) differentiating between patients with over 40% fibrosis and those with under 40%. We also discovered a positive correlation between the glomerular filtration rate (eGFR) and 2D-SWE PLUS values (r = 0.7065, p < 0.0001) and Vi PLUS values (r = 0.3637, p < 0.0211). C reactive protein (CRP) correlates with the Vi PLUS measures (r = −0.3695, p = 0.0189) but not with the 2D SWE PLUS measures (r = −0.2431, p = 0.1306). Conclusion: Our findings indicate that this novel elastography method can distinguish between individuals with different stages of renal fibrosis, correlate with the renal function and inflammation, and are easy to use and reproducible, but further research is needed for them to be employed routinely in clinical practice.

Background and Objectives: This study aimed to assess the clinical-pathological profile of patients with invasive cutaneous melanomas and to identify the parameters with a prognostic role in the lymph nodal spread of this malignant tumor. Materials and Methods: We performed a retrospective study on patients with invasive cutaneous melanomas who underwent surgery in the “Pius Brînzeu” County Clinical Emergency Hospital from Timișoara, Romania, and were evaluated for the status of loco-regional lymph nodes. We selected and analyzed some parameters searching for their relationship with lymph node metastases. Results: We identified 79 patients with invasive cutaneous melanomas (29 men and 50 women, mean age 59.36 years). A percentage of 58.3% of melanomas had Breslow tumor thickness >2 mm; 69.6% of melanomas showed a Clark level IV–V. Tumor ulceration was present in 59.5% of melanomas. A mitotic rate of ≥5 mitoses/mm² was observed in 48.1% of melanomas. Tumor-infiltrating lymphocytes (TILs), non-brisk, were present in 59.5% of cases and 22.8% of patients had satellite/in-transit metastasis (SINTM). Tumor regression was identified in 44.3% of cases. Lymph nodes metastases were found in 43.1% of patients. Statistical analysis showed that lymph node metastases were more frequent in melanomas with Breslow thickness >2 mm (p = 0.0002), high Clark level (p = 0.0026), mitotic rate >5 mitoses/mm² (p = 0.0044), ulceration (p = 0.0107), lymphovascular invasion (p = 0.0182), SINTM (p = 0.0302), and non-brisk TILs (p = 0.0302). Conclusions: The Breslow thickness >2 mm, high Clark level, high mitotic rate and ulceration are the most important prognostic factors for lymph nodal spread in cutaneous melanomas. However, some melanomas without these clinical-pathological features can have an unexpected, aggressive evolution, which entails the necessity of close and prolonged clinical follow-up of patients, including those with lesions considered without risk.