(1) Background: Recently, Artificial Intelligence (AI)-based models have been investigated for lymph node involvement (LNI) detection and prediction in Prostate cancer (PCa) patients, in order to reduce surgical risks and improve patient outcomes. This review aims to gather and analyze the few studies available in the literature to examine their initial findings. (2) Methods: Two reviewers conducted independently a search of MEDLINE databases, identifying articles exploring AI’s role in PCa LNI. Sixteen studies were selected, and their methodological quality was appraised using the Radiomics Quality Score. (3) Results: AI models in Magnetic Resonance Imaging (MRI)-based studies exhibited comparable LNI prediction accuracy to standard nomograms. Computed Tomography (CT)-based and Positron Emission Tomography (PET)-CT models demonstrated high diagnostic and prognostic results. (4) Conclusions: AI models showed promising results in LN metastasis prediction and detection in PCa patients. Limitations of the reviewed studies encompass retrospective design, non-standardization, manual segmentation, and limited studies and participants. Further research is crucial to enhance AI tools’ effectiveness in this area.

Background: Focal therapy is a promising, minimally invasive method for the treatment of patients with localized prostate cancer. According to the existing literature, there is growing evidence for positive functional outcomes and oncological effectiveness. The aim of this review is to evaluate the technical efficacy of three minimally invasive techniques (cryoablation, electroporation, and microwave ablation) and their impact on quality of life in patients with prostate cancer. Methods: Studies between January 2020 and July 2023 were selected using PubMed, Embase, and The Cochrane Library and analyzed following PRISMA guidelines; they have not been registered. Results: Twenty-three studies investigating three different sources of energy to deliver focal therapy were found. Thirteen studies evaluated the performance of the cryoablation therapy, seven studies of the irreversible electroporation, and three studies of microwave ablation option. The majority of studies were retrospective cohort studies. Cryoablation showed excellent oncological outcomes for low-grade prostate cancer, whether performed on the lesion, on the hemigland, or on the entire gland, with the best results obtained for patients with intermediate risk. Irreversible electroporation showed promising oncological outcomes with no significant changes in functional outcomes. Microwave ablation showed great early functional outcomes. Conclusions: The oncological effectiveness of minimally invasive treatment in comparison to standard of care is still under investigation, despite encouraging results in terms of functional outcomes improvement and adverse events reduction. More comprehensive research is needed to fully understand the function of minimally invasive treatment in patients with localized PCa.

Background: Prostate cancer is a major cause of cancer-related mortality among men, with approximately 15% of newly diagnosed patients having pelvic lymph node metastasis (PLNM). For this reason, PLNM identification before localized PCa treatment would significantly impact treatment planning, clinical judgment, and patient outcome prediction. Radiomics has gained popularity for its ability to predict tumor behavior and prognosis without invasive procedures. Magnetic resonance imaging (MRI) is widely used in radiomic workups, particularly for prostate cancer. This study aims to predict lymph node invasion in prostate cancer patients using clinical information and mp-MRI radiomics features extracted from the suspicious nodule, prostate gland, and periprostatic adipose tissue (PPAT). Methods: A retrospective review of 85 patients who underwent mp-MRI at our radiology department between 2016 and 2022 was conducted. This study included patients who underwent prostatectomy and lymphadenectomy with complete histological examination and previous staging mp-MRI and were divided into two groups based on lymph node status (positive/negative). Data were collected from each patient, including clinical information, radiomics, and semantic data (such as tumor MRI characteristics, histological tumor details, and lymph node status (LNS)). MRI exams were conducted using a 1.5-T system and were used to study the prostate gland. A three-year resident manually segmented the prostate nodule, prostatic gland, and periprostatic tissue using an open-source segmentation program. A random forest (RF) machine learning model was developed and tested using Chat-GPT version 4.0 software. The model’s performance in predicting LNS was assessed using accuracy, precision, recall, F1 score, and area under the curve (AUC) receiver operating characteristic (ROC), with sensitivity and specificity evaluated using DeLong’s test. Results: Random forest demonstrated the best performance in prediction considering features extracted from DWI nodules (67% of accuracy, 0.83 AUC), from T2 fat (78% of accuracy, 0.86 AUC), and from T2 glands (78% of accuracy, 0.97 AUC). The combination of the three sequences in the nodule evaluation was more accurate compared with the single sequences (88%). Combining all the nodule features with gland and PPAT features, an accuracy of 89% with AUC near 1 was obtained. Compared with the analysis of the nodule and the PPAT, the whole-gland evaluation had the best performance (p ≤ 0.05) in predicting LNS when compared with the nodule. Conclusions: Precise nodal staging is essential for PCa patients’ prognosis and therapeutic strategy. When compared with a radiologist’s assessment, radiomics models enhance the diagnostic accuracy of lymph node staging for prostate cancer. Although data are still lacking, deep learning models may be able to further improve on this.

Learning Objectives: To provide a pictorial review of the main clinical applications of DECT with a focus on traumatic and non-traumatic emergencies.Methods: DECT is becoming increasingly common in clinical practice. The differences in attenuation between two different datasets allow to perform a qualitative and quantitative characterization of the materials contained in a single voxel. Virtual monoenergetic images, material decomposition images, electron density maps, and atomic number maps are created by post-processing algorithms and offer the radiologist useful diagnostic data.Results: We present a series of cases illustrating the utility of DECT in the diagnosis and management of acute pathologies, with a special focus on musculoskeletal imaging and neuroimaging.Conclusion: DECT is a useful imaging technique that may allow radiologists to make quicker and more precise diagnoses in the emergency setting.