(1): Background: With the recent introduction of vesical imaging reporting and data system (VI-RADS), magnetic resonance imaging (MRI) has become the main imaging method used for the preoperative local staging of bladder cancer (BCa). However, the VI-RADS score is subject to interobserver variability and cannot provide information about tumor cellularity. These limitations may be overcome by using a quantitative approach, such as the new emerging domain of radiomics. (2) Aim: To systematically review published studies on the use of MRI-based radiomics in bladder cancer. (3) Materials and Methods: We performed literature research using the PubMed MEDLINE, Scopus, and Web of Science databases using PRISMA principles. A total of 1092 papers that addressed the use of radiomics for BC staging, grading, and treatment response were retrieved using the keywords “bladder cancer”, “magnetic resonance imaging”, “radiomics”, and “textural analysis”. (4) Results: 26 papers met the eligibility criteria and were included in the final review. The principal applications of radiomics were preoperative tumor staging (n = 13), preoperative prediction of tumor grade or molecular correlates (n = 9), and prediction of prognosis/response to neoadjuvant therapy (n = 4). Most of the developed radiomics models included second-order features mainly derived from filtered images. These models were validated in 16 studies. The average radiomics quality score was 11.7, ranging between 8.33% and 52.77%. (5) Conclusions: MRI-based radiomics holds promise as a quantitative imaging biomarker of BCa characterization and prognosis. However, there is still need for improving the standardization of image preprocessing, feature extraction, and external validation before applying radiomics models in the clinical setting.

(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.

Background and objectives: Systematic prostate biopsy (SB) has a low Gleason group (GG) accuracy when compared to final pathology. This may negatively impact the inclusion of patients into specific risk groups and treatment choice. The aim of our study was to assess the GG accuracy of magnetic resonance imaging-ultrasound (MRI-US) fusion prostate biopsy. Materials and Methods: Of a cohort of minimally invasive radical prostatectomy (RP), we selected all patients who were diagnosed with prostate cancer (PCa) via MRI-US fusion biopsy (n = 115). Results: Combined biopsy had the highest rate for GG concordance (61.7% vs. 60.4% for SB vs. 45.3% for MRI-US fusion biopsy) and the lowest for upgrading (20.9% vs. 24.5% for SB vs. 34.9% for MRI-US fusion biopsy), p < 0.0001. No clinical data were predictive for upgrading or downgrading at final pathology. Locally advanced PCa was associated with a high Prostate Imaging-Reporting and Data System (PIRADS) score (p = 0.0014) and higher percentages of positive biopsy cores (PBC)/targeted (p = 0.0002) and PBC/total (p = 0.01). Positive surgical margins were correlated with higher percentages of PBC/systematic (p = 0.003) and PBC/total (p = 0.009). Conclusions: Pre-biopsy prostate MRI improves GG concordance between biopsy and RP. Combined biopsy provides the highest grading accuracy when compared to final pathology. Targeted and systematic biopsy data are predictive for adverse pathologic outcomes.

Endometriosis is a common gynecological pathology, with an incidence of nearly 10% in patients of reproductive age, and is still underdiagnosed. A thorough and well-spread diagnostic study of endometriosis based on epigenetic factor dysregulation can highlight potential areas for improvement. To quantify the potential and utility of non-invasive tools in the early diagnosis of endometriosis, an overview of current knowledge on epigenetic factors, based on DNA and RNA, is presented. Among these tools, it is important to highlight the role of miRNAs (microRNAs), cfDNA (cell-free DNA), and rRNAs (ribosomal RNAs), which are small molecules involved in endometriosis and numerous other pathologies. To evaluate their potential and utility in endometriosis, a salivary miRNA diagnostic test was conducted, the cfDNA methylation patterns of fragmented DNA circulating in bodily fluids (e.g., plasma) were analyzed, and cervical and uterine microbiomes were profiled for bacterial rRNA in patients with clinical suspicion of incipient endometriosis. Specific molecular profiles associated with endometriosis were analyzed. The first profile, a 109-miRNA saliva signature, was validated as a product of miRNA biomarkers and artificial intelligence modeling. In addition, peripheral blood cfDNA methylation biomarkers were identified by investigating nine genes in a molecular signature that requires validation. A profile was also obtained from cervical swabs and uterine washes, including molecular analysis of 16S rRNA amplicon sequencing to evaluate alterations in the cervical bacterial community. This review aims to optimize the integration of a non-invasive diagnostic tool for early endometriosis diagnosis. Genetic biomarkers can be correlated with clinical factors to improve diagnostic accuracy. Of the assessed diagnostic tools, salivary miRNA tests, a peripheral blood cfDNA methylation biomarker, and a microbiome rRNA signature may be useful for early diagnosis of endometriosis, as well as, implicitly, therapeutic attitude and follow-up.

Background and Objectives: Acute hematologic malignancies are a group of heterogeneous blood diseases with a high mortality rate, mostly due to acute respiratory failure (ARF). Acute respiratory distress syndrome (ARDS) is one form of ARF which represents a challenging clinical condition. The paper aims to review current knowledge regarding the variable pathogenic mechanisms, as well as therapeutic options for ARDS in acute hematologic malignancy patients. Data collection: We provide an overview of ARDS in patients with acute hematologic malignancy, from an etiologic perspective. We searched databases such as PubMed or Google Scholar, including articles published until June 2022, using the following keywords: ARDS in hematologic malignancy, pneumonia in hematologic malignancy, drug-induced ARDS, leukostasis, pulmonary leukemic infiltration, pulmonary lysis syndrome, engraftment syndrome, diffuse alveolar hemorrhage, TRALI in hematologic malignancy, hematopoietic stem cell transplant ARDS, radiation pneumonitis. We included relevant research articles, case reports, and reviews published in the last 18 years. Results: The main causes of ARDS in acute hematologic malignancy are: pneumonia-associated ARDS, leukostasis, leukemic infiltration of the lung, pulmonary lysis syndrome, drug-induced ARDS, radiotherapy-induced ARDS, diffuse alveolar hemorrhage, peri-engraftment respiratory distress syndrome, hematopoietic stem cell transplantation-related ARDS, transfusion-related acute lung injury. Conclusions: The short-term prognosis of ARDS in acute hematologic malignancy relies on prompt diagnosis and treatment. Due to its etiological heterogeneity, precision-based strategies should be used to improve overall survival. Future studies should focus on identifying the relevance of such etiologic-based diagnostic strategies in ARDS secondary to acute hematologic malignancy.

Background: Prostate cancer (PCa) represents a matter at the forefront of healthcare, being divided into clinically significant (csPCa) and indolent PCa based on prognostic and treatment options. Although multi-parametric magnetic resonance imaging (mpMRI) has enabled significant advances, it cannot differentiate between the aforementioned categories; therefore, in order to render the initial diagnosis, invasive procedures such as transrectal prostate biopsy are still necessary. In response to these challenges, artificial intelligence (AI)-based algorithms combined with radiomics features offer the possibility of creating a textural pixel pattern-based surrogate, which has the potential of correlating the medical imagery with the pathological report in a one-to-one manner. Objective: The aim of the present study was to develop a machine learning model that can differentiate indolent from csPCa lesions, as well as individually classifying each nodule into corresponding ISUP grades prior to prostate biopsy, using textural features derived from mpMRI T2WI acquisitions. Materials and Methods: The study was conducted in 154 patients and 201 individual prostatic lesions. All cases were scanned using the same 1.5 Tesla mpMRI machine, employing a standard protocol. Each nodule was manually delineated using the 3D Slicer platform (version 5.2.2) and textural parameters were derived using the PyRadiomics database (version 3.1.0). We compared three machine learning classification models (Random Forest, Support Vector Machine, and Logistic Regression) in full, partial and no correlation settings, in order to differentiate between indolent and csPCa, as well as between ISUP 2 and ISUP 3 lesions. Results: The median age was 65 years (IQR: 61–69), the mean PSA value was 10.27 ng/mL, and 76.61% of the segmented lesions had a PI-RADS score of 4 or higher. Overall, the highest performance was registered for the Random Forest model in the partial correlation setting, differentiating between indolent and csPCa and between ISUP 2 versus ISUP 3 lesions, with accuracies of 88.13% and 82.5%, respectively. When the models were trained on combined clinical data and radiomic signatures, these accuracies increased to 91.11% and 91.39%, respectively. Conclusions: We developed a machine learning decision support tool that accurately predicts the ISUP grade prior to prostate biopsy, based on the textural features extracted from T2 MRI acquisitions.