No abstract

The etiology of abdominal aortic aneurysm (AAA) includes inflammation and endothelial dysfunction. To evaluate relations between these mechanisms and AAA growth, endothelin (ET)-1, tumor necrosis factor (TNF)-alpha, interleukin (IL)-6, and CD40 ligand were related to yearly AAA growth for 2.9 +/- 1.6 years (mean +/- SD) in 178 patients with conservatively followed AAA. Total number of follow-up years was 491. Abdominal aortic aneurysm diameter increased by 3.3 +/- 4.0 mm during the first year and by 4.9 +/- 4.4 mm during the first 2 years. Median (range) growth was 2.5 (-1.0 to 30.6) mm/year. When patients with AAA growth above or below median were compared, initial AAA diameter (46.1 +/- 5.8 vs 42.0 +/- 8.3 mm; P < .0001), age (75 +/- 7 vs 72 +/- 8 years; P < .029), and initial ET-1 levels (1.31 +/- 0.50 vs 1.13 +/- 0.49 pg/mL; P < .0177) were higher in patients with growth above median. Endothelin 1 (P = .0230) and initial AAA diameter (P = .0019) predicted AAA growth above median in logistic regression. In conclusion, higher initial levels of ET-1 and initial AAA diameter independently predict AAA growth.

Matrix metalloproteinase 2 is lower and APC-PCI higher in patients with larger AAA, but the relevance of the markers for AAA growth is far from clarified.

Objectives Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was to assess whether a deep learning algorithm can replace PI-RADS 2.1 based ellipsoid formula (EF) for calculating PV. Methods Eight different measures of PV were retrospectively collected for each of 124 patients who underwent radical prostatectomy and preoperative MRI of the prostate (multicenter and multi-scanner MRI’s 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PVDL) and ellipsoid formula by two radiologists (PVEF1 and PVEF2) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PVMPE). A sensitivity analysis was performed using a prostatectomy specimen as the reference standard. Inter-reader agreement was evaluated between the radiologists using the ellipsoid formula and between the expert and inexperienced radiologists performing manual planimetry. Results PVDL showed better agreement and precision than PVEF1 and PVEF2 using the reference standard PVMPE (mean difference [95% limits of agreement] PVDL: −0.33 [−10.80; 10.14], PVEF1: −3.83 [−19.55; 11.89], PVEF2: −3.05 [−18.55; 12.45]) or the PV determined based on specimen weight (PVDL: −4.22 [−22.52; 14.07], PVEF1: −7.89 [−30.50; 14.73], PVEF2: −6.97 [−30.13; 16.18]). Inter-reader agreement was excellent between the two experienced radiologists using the ellipsoid formula and was good between expert and inexperienced radiologists performing manual planimetry. Conclusion Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. Key Points • A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. • The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set.