Thermoablative Treatment of De Novo Tumor in Kidney Allograft.

BACKGROUND: The overall cancer risk increases in transplant patients, including in kidney allografts. This study aimed to analyze the outcome of patients with kidney allograft malignant tumors who underwent percutaneous thermal ablation. METHODS: We included 26 renal allograft tumors, including 7 clear-cell renal cell carcinoma (RCCs), 16 papillary RCCs, 1 clear-cell papillary RCC, and 2 tubulocystic RCCs, treated in 19 ablation sessions. Outcomes of thermal ablation therapy were assessed, including technical success, adverse events, local tumor progression, development of metastases, survival after thermal ablation, and changes in renal function. RESULTS: Success rate was achieved in all ablation sessions (primary success rate: 96%; secondary success rate: 100%). No adverse events were observed in grades 3, 4, or 5. The median follow-up period was of 34 mo (15-69 mo). Two patients died during follow-up from a cause independent of renal cancer. The median decrease in estimated glomerular filtration rate 1 y after procedure was -4 (interquartile range, -7 to 0) mL/min/1.73 m 2 . One patient returned to dialysis within the year of the procedure. CONCLUSIONS: Percutaneous thermal ablation shows convincing results for treating malignant renal graft tumors and should be a useful treatment option. The shorter hospitalization time, the advantage of avoiding a potentially challenging dissection of the transplant, and the excellent preservation of allograft function appear encouraging to extend this indication.

Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?

The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g., dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging.

Crimping and deployment of balloon-expandable valved stents are responsible for the increase in the hydraulic conductance of leaflets.

OBJECTIVES: Leaflet injury has been documented to occur during the deployment of valved stents (VSs). The pathological aspects, however, of this injury are difficult to quantify. Conversely, the hydraulic conductance of a (pericardial) membrane may be easily determined. The impact of crimping and deployment of VS on this parameter was therefore investigated. METHODS: Bovine pericardial square (25 × 25 mm) patches were placed within a pressure chamber and their hydraulic conductance was determined. The influence of the pressure gradient and tissue thickness on this parameter was analysed. Six balloon-expandable VS were constructed. The hydraulic conductance of their bovine pericardial leaflets was determined before and after VS crimping and deployment in four of them. Pericardial leaflets of non-crimped VS were used as controls. RESULTS: Hydraulic conductance increased insignificantly with the pressure level within the chamber: from 128 ± 26.9 ml/h/m(2)/mmHg at a pressure of 50 mmHg to 232.3 ± 51.9 ml/h/m(2)/mmHg at a pressure of 250 mmHg (P = 0.117). Hydraulic conductance was not correlated to pericardial thickness, for thickness measurements ranging from 0.34 to 0.76 mm. The hydraulic conductance of VS leaflets significantly increased immediately after crimping from 45.2 ± 7.6 to 667.9.0 ± 527.2 ml/h/m(2)/mmHg (P < 0.001). This increase was still observed 24 h after VS deployment. No change in hydraulic conductance occurred in the control group. CONCLUSIONS: The determination of the hydraulic conductance of pericardial patches was easy to perform, reproducible and not influenced by tissue thickness. The hydraulic conductance of pericardial leaflets dramatically increased after VS crimping and deployment. This parameter might be, in the future, a useful noninvasive tool in studying leaflet trauma.