Purpose: The goal of the study was an assessment of the diagnostic performance of diffusion-weighted images (DWI)
and apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) in distinguishing local recurrence (LR)
of renal cell carcinoma (RCC) from benign conditions after partial nephrectomy.
Material and methods: Thirty-nine patients after partial nephrectomy for solid RCC were enrolled in the study. Patients
were followed up using MRI, which included DWI sequence (b = 800 s/mm2). All patients with MRI features of LR were included in the main group (n = 14) and patients without such features – into the group of comparison (n = 25).
Apparent diffusion coefficient (ADC) values of suspicious lesions were recorded. In all patients with signs of locally recurrent RCC, surgical treatment was performed followed by pathologic analysis.
Results: The mean ADC values of recurrent RCC demonstrated significantly higher numbers compared to benign fibrous tissues and were 1.64 ± 0.15 × 10-3 mm2/s vs. 1.02 ± 0.26 × 10-3 mm2/s (p < 0.001). The mean ADC values of RCCs’ LR and benign post-op changes in renal scar substantially differed from mean ADC values of healthy kidneys’ parenchyma; the latter was 2.58 ± 0.05 × 10-3 mm2/s (p < 0.001). In ROC analysis, the use of ADC with a threshold value of 1.28 × 10-3 mm2/s allowed us to differentiate local recurrence of RCC from benign postoperative changes with 100% sensitivity, 80% specificity, and accuracy: AUC = 0.980 (p < 0.001).
Conclusions: The apparent diffusion coefficient of DWI of MRI can be used as a potential imaging marker for the diagnosis of local recurrence of RCC.

Purpose: Prostate cancer (PCa) is the second most common cancer in men. The urge to guide treatment tactics based on personal clinical risk factors has evolved in the era of human genome sequencing. To date, personalized approaches to managing PCa patients have not yet been developed. Radiogenomics is a relatively new term, used to refer to the study of genetic variation associated with imaging features of the tumour in order to improve the prognostication of the disease course.
Material and methods: The study is a review of recent knowledge regarding potential clinical applications of radiogenomics in personalized treatment of PCa.
Results: Recent investigations have proven that by combining data on individual genetic tumour features, and radiomic profiling (radiologic-molecular correlation), with traditional staging procedures in order to personalize treatment of PCa, an improved prognostication of PCa course can be performed, and overtreatment of indolent cancer can be avoided. It was found that a combination of multiparametric MRI and gene expression data allowed the detection of radiomic features of PCa, which correlated with a number of gene signatures associated with adverse outcomes.
It was revealed that several molecular markers may drive tumour upstaging, allowed the distinction between the PCa
stages, and correlated with aggressiveness-related radiomic features.
Conclusions: The radiogenomics of PCa is not a comprehensively investigated area of oncourology. The combination
of genomics and radiomics as integrative parts of precision medicine in the future has the potential to become the
foundation for a personalized approach to the management of PCa.