RSNA 2006

Nov 26, 2006

An interactive panorama of McCormick Place during RSNA 2006.

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Illustrations show the role of angiosome-to-tumor volume ratio (ATR) in different clinical scenarios based on tumor and treatment angiosome volume. The pale orange oval represents the tumor within the liver, and the pie-shaped region encompassed by the black lines represents the angiosome. (A) An adequate ATR (≥16) is reached when treating tumor and an accompanying appropriate volume of expendable surrounding normal parenchyma, such as a 2.5-cm tumor within a 150-mL treatment angiosome. Lower (inadequate) ATR can occur with superselective administrations, such as (B) attempting to treat a larger (4-cm) tumor within the same 150-mL angiosome, which proves suboptimal, and (C) attempting to treat the same 2.5-cm tumor within a reduced angiosome (40 mL).

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$Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).$