Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention

Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

: Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions

Skeletal Muscle Metastases and Inferior Vena Cava Involvement in a Patient with Clear Cell Renal Cell Carcinoma and Sarcomatoid Differentiation

Introduction: Renal cell carcinoma has a propensity to propagate into the renal vein and inferior vena cava. A small percentage has distant metastasis at presentation. Pulmonary, hepatic, cerebral and bone metastases are common, but skeletal muscle involvement is rare

High Grade Uterine and Rectal Prolapse

Introduction: Pelvic floor hernias are encountered especially in elderly women. A combined genital, bladder, and rectal prolapse poses treatment challenges in aged women

Zinner’s Syndrome – The Value of Clinical Imaging and Morphopathological Findings for Diagnosis

Introduction: Cystic congenital malformations of the seminal vesicle are unusual. More than half of them are associated with ipsilateral renal agenesis. This disease was first described by Zinner in 1914, and since then, more than 200 cases have been reported. Most of the patients with this congenital disease present few symptoms until the middle-age

Easily Available Blood Test Neutrophil-To-Lymphocyte Ratio Predicts Progression in High-Risk Non-Muscle Invasive Bladder Cancer

Introduction: The inflammatory response surrounding the tumour has a major importance in the oncologic outcome of bladder cancers. One marker proved to be useful and accessible is NLR (neutrophil-to-lymphocyte ratio). The objective of the study was the analysis of NLR as a prognostic factor for recurrence and progression in pT1a and pT1b bladder cancers

Modified Glasgow Prognostic Score as a Predictor of Recurrence in Patients with High Grade Non-Muscle Invasive Bladder Cancer Undergoing Intravesical Bacillus Calmette–Guerin Immunotherapy

Background: A systemic inflammatory marker, the modified Glasgow prognostic score (mGPS), could predict outcomes in non-muscle-invasive bladder cancer (NIMBC). We aimed to investigate the predictive power of mGPS in oncological outcomes in HG/G3 T1 NMIBC patients undergoing Bacillus Calmette–Guérin (BCG) therapy. Methods: We retrospectively reviewed patient’s medical data from multicenter institutions. A total of 1382 patients with HG/G3 T1 NMIBC have been administered adjuvant intravesical BCG therapy, every week for 3 weeks given at 3, 6, 12, 18, 24, 30 and 36 months. The analysis of mGPS for recurrence and progression was performed using multivariable and univariable Cox regression models. Results: During follow-up, 659 patients (47.68%) suffered recurrence, 441 (31.91%) suffered progression, 156 (11.28%) died of all causes, and 67 (4.84%) died of bladder cancer. At multivariable analysis, neutrophil to lymphocyte ratio [hazard ratio (HR): 7.471; p = 0.0001] and erythrocyte sedimentation rate (ESR) (HR: 0.706; p = 0.006 were significantly associated with recurrence. mGPS has no statistical significance for progression (p = 0.076). Kaplan–Meier survival analysis showed a significant difference in survival among patients from different mGPS subgroups. Five-year OS was 93% (CI 95% 92–94), in patients with mGPS 0, 82.2% (CI 95% 78.9–85.5) in patients with mGPS 1 and 78.1% (CI 95% 60.4–70) in mGPS 2 patients. Five-year CSS was 98% (CI 95% 97–99) in patients with mGPS 0, 90% (CI 95% 87–94) in patients with mGPS 1, and 100% in mGPS 2 patients. Limitations are applicable to a retrospective study. Conclusions: mGPS may have the potential to predict recurrence in HG/G3 T1 NMIBC patients, but more prospective, with large cohorts, studies are needed to study the influence of systemic inflammatory markers in prediction of outcomes in NMIBC for a definitive conclusion