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.

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.

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.

Material and Methods: Retrospective study, with 44 T1a/T1b bladder cancer patients. Each patient underwent transurethral resection. NLR was considered altered if higher than 3, average follow-up period was of 18 months.

Results: The mean age of the patients included was 73 years (IQR 64 - 77). Most of the patients had NLR<3 (30 patients). In total 29/44 (65.9 %) patients presented recurrence and 15/44 (34.1 %) patients were identified with T2 or higher stage progression during the follow-up period (average 18 months).We found no statistically significant association between NLR>3 and other clinic and pathologic factors. Progression-free survival (PFS) Kaplan-Meier analysis showed a lower PFS in the NLR>3 group, with a p=0.001 value. A total of 64.3% of patients had shown progression in the NLR>3 group and 20% in the NLR<3 group. Mean NLR was 2.67 (IQR 1.88-3.5); 2.50 (IQR 1.89-2.87) in patients that did not present any progression during the follow-up and 3.20 (IQR 1.73-5.80) in those with progression (p=0.09), ROC 0.655. Mean NLR was 2.14 (IQR 1.61-2.77) in patients that did not experience a recurrence during the follow-up and 2.76 (IQR 2.1-4.31) in those with recurrence, ROC 0.671 (p=0.06). Multivariable Cox regression analyses showed that stage T1b and NLR represent independent prognostic factors for PFS.

Conclusion: High Neutrophil-to-Lymphocyte ratio retained a statistically significant value, as an independent prognostic factor for bad prognosis of T1 bladder tumors. NLR represents a biomarker that could support a clinical decision making in case of high-risk on-muscle invasive bladder cancer.