Background. Liver involvement secondary to multiple myeloma is a rare and uncommon radiologic finding. Such extraosseous secondary lesions as well as tongue involvement require pathohistological confirmation to prevent misdiagnosis. Clinical and laboratory diagnostics are challenging in patients with COVID-19 and underlying multiple myeloma and its secondary lesions, leading to difficulties in treatment and outcomes.
Case Report. A 64-year-old male patient, not vaccinated against COVID-19, with a history of multiple myeloma presented with symptoms of headache, fatigue, dyspnea, cough, and fever. The patient’s medical history was intricate, involving cholecystectomy and a diagnosis of multiple myeloma, which was
subsequently treated with chemotherapy and radiation therapy. Additionally, uncommon liver and tongue involvement secondary to multiple myeloma was found. Upon admission, the patient’s peripheral oxygen saturation was 90%, accompanied by increasing shortness of breath and a respiratory rate of 26 breaths per minute. A positive COVID-19 test was recorded. A lung computed tomography revealed bilateral multifocal areas of ground-glass opacity and consolidation, encompassing the entire pulmonary regions, corresponding to CO-RADS 6. The patient was admitted to the intensive care unit. Despite initiating oxygen support and symptomatic therapy, the patient’s death occurred. Autopsy confirmed the development of severe acute respiratory distress syndrome and bilateral hemorrhagic pneumonia, with multiple myeloma as a contributing factor.
Conclusions. This case report highlighted the rare occurrence of secondary liver involvement in multiple myeloma, characterized by nodules with distinct imaging features. It underscored the importance of identifying coexisting lesions, such as tongue involvement, and the diagnostic challenges they pose. Additionally, the case emphasized the need for comprehensive clinical assessment in patients with concurrent COVID-19 and underlying multiple myeloma, as it may lead to the development of acute respiratory distress syndrome.

Purpose: This study aimed to assess the diagnostic performance of multiphase contrast-enhanced computed tomography (MCECT) in differentiating benign and malignant solid and cystic small renal masses (SRMs), predicting histologic subtypes, and grading, using signal intensity (SI) and tumour-to-cortex signal intensity (TCSI) ratio. Material and methods: A retrospective analysis was conducted on 181 patients with solid and cystic SRMs (≤ 4 cm).
MCECT imaging across 4 phases (non-contrast, corticomedullary, nephrographic, and excretory) was performed. SI and TCSI values were measured, and their diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Solid, Bosniak IIF, III, and IV SRMs underwent histopathological confirmation.
Results: Among solid SRMs, excretory phase SI achieved an area under the curve (AUC) of 0.848 for differentiating RCC from other SRMs, with 100% sensitivity and 61.3% specificity. For distinguishing renal cell carcinoma (RCC) from benign SRMs, the most effective parameter was the TCSI ratio obtained from computed tomography excretory phase (88.6% sensitivity, 52.4% specificity, 0.763 AUC). For Bosniak IIF cysts, the corticomedullary phase SI provided an AUC of 0.902, with 93% sensitivity and 87.5% specificity. RCC subtyping showed distinct SI characteristics across phases, particularly for clear cell RCC. Nephrographic phase SI differentiated low- versus high-grade RCC, with an AUC of 0.901, 90.2% sensitivity, and 86.4% specificity.
Conclusions: MCECT-derived imaging biomarkers, particularly SI and TCSI, are effective non-invasive tools for characterising SRMs, aiding in the differentiation of benign and malignant lesions, histological subtypes, and tumour grades. Their integration with advanced radiomics could further enhance diagnostic accuracy.
Key words: renal cell carcinoma, small renal masses, radiomics, imaging marker, tumour-to-cortex signal intensity ratio, multiphase contrast-enhanced CT.

Introduction Stress urinary incontinence (SUI) is a common complication following radical prostatectomy, affecting up to 60.0% of men. The artificial urinary sphincter (AUS) has been the gold standard for treating severe SUI since its introduction in 1973. Despite its efficacy, long-term complications such
as device failure and recurrent incontinence are relatively common, often necessitating revision surgeries. This review focuses on cuff downsizing as a revision strategy for non-mechanical AUS failure.
Material and methods A literature review was conducted using PubMed/Medline, covering studies published between January 2000 and December 2023. Key words included: “artificial urinary sphincter”, “cuff downsizing”, “urethral atrophy”, “non-mechanical failure” and "male urinary incontinence revision”.
Inclusion criteria were studies addressing cuff downsizing as a primary revision for non-mechanical failures. Only English-language studies were reviewed. We analyzed the timing of revisions, follow-up duration, and outcomes such as continence rates, complication rates, and device survival.
Results Six retrospective studies involving 206 patients were included in the present review. Cuff downsizing was performed as the sole intervention in 3 studies and in combination with other approaches in the remaining 3 studies. The median cuff size decreased from 4.5 cm preoperatively to 4.0 cm postoperatively, with 8.0–12.0% of patients receiving a cuff downsized by more than 1.0 cm. Across all studies, continence rates after revision surgery ranged from 52.0% to 90.0% based on patientreported outcome measures (PROMs). Device survival rates varied from 64.0% to 95.0%, with infection and urethral erosion being the leading causes of device explantation.
Conclusions Cuff downsizing is a reasonable revision strategy for non-mechanical AUS failure, offering similar continence outcomes and complication rates compared to alternative techniques.

Background Renal cell carcinoma (RCC) is a prevalent malignancy with highly variable outcomes. MicroRNA-15a
(miR-15a) has emerged as a promising prognostic biomarker in RCC, linked to angiogenesis, apoptosis, and proliferation.
Radiogenomics integrates radiological features with molecular data to non-invasively predict biomarkers, offering
valuable insights for precision medicine. This study aimed to develop a machine learning-assisted radiogenomic
model to predict miR-15a expression in RCC.
Methods A retrospective analysis was conducted on 64 RCC patients who underwent preoperative multiphase
contrast-enhanced CT or MRI. Radiological features, including tumor size, necrosis, and nodular enhancement, were
evaluated. MiR-15a expression was quantified using real-time qPCR from archived tissue samples. Polynomial regression
and Random Forest models were employed for prediction, and hierarchical clustering with K-means analysis
was used for phenotypic stratification. Statistical significance was assessed using non-parametric tests and machine
learning performance metrics.
Results Tumor size was the strongest radiological predictor of miR-15a expression (adjusted R2 = 0.8281, p < 0.001).
High miR-15a levels correlated with aggressive features, including necrosis and nodular enhancement (p < 0.05),
while lower levels were associated with cystic components and macroscopic fat. The Random Forest regression
model explained 65.8% of the variance in miR-15a expression ( R2 = 0.658). For classification, the Random Forest classifier
demonstrated exceptional performance, achieving an AUC of 1.0, a precision of 1.0, a recall of 0.9, and an F1-score
of 0.95. Hierarchical clustering effectively segregated tumors into aggressive and indolent phenotypes, consistent
with clinical expectations.
Conclusions Radiogenomic analysis using machine learning provides a robust, non-invasive approach to predicting
miR-15a expression, enabling enhanced tumor stratification and personalized RCC management. These findings
underscore the clinical utility of integrating radiological and molecular data, paving the way for broader adoption
of precision medicine in oncology.

Purpose: Inferior vena cava (IVC) involvement by renal cell carcinoma (RCC) is associated with a higher disease stage and is considered a risk factor for poor prognosis. This study aimed to investigate the role of the apparent diffusion coefficient (ADC) of MRI 3D texture analysis in the differentiation of solid and friable tumour thrombus in patients with RCC.

Materials and methods: The study involved 27 patients with RCC with tumour thrombus in the renal vein or IVC, surgically treated with nephrectomy and thrombectomy and in whom preoperatively abdominal MRI including the DWI sequence was conducted. For 3D texture analysis, the ADC map was used, and the first-order radiomic features were calculated from the whole volume of the thrombus. All tumour thrombi were histologically clas sified as solid or friable.

Results: The solid and friable thrombus was detected in 51.9 % and 48.1 % of patients, respectively. No differences in mean values of range, 90th percentile, interquartile range, kurtosis, uniformity and variance were found between groups. Equal sensitivity and specificity (93 % and 69 %, respectively) of ADC mean, median and entropy in differentiation between solid and friable tumour thrombus, with the highest AUC for entropy (0.808), were observed. Applying the skewness threshold value of 0.09 allowed us to achieve a sensitivity of 86 % and a specificity of 92 %.

Conclusions: In patients with RCC and tumour thrombus in the renal vein or IVC, the 3D texture analysis based on ADC-map allows for precise differentiation of a solid from a friable thrombus.