Swedish oncology firm Elekta of Stockholm reports that demand for its clinical products and services remained strong in the first three months of fiscal year 2010 (end-July 31).
Net sales increased 15% to 1.44 billion kronor ($201 million U.S.), compared with 1.3 billion kronor ($153.7 million U.S.) in the first quarter of fiscal 2009. Operating profit rose to 89 million kronor ($12.4 million U.S.).
Elekta also noted that its efficiency improvement program is proceeding as planned. Restructuring costs totaled 11 million kronor ($1.5 million U.S.) in the first fiscal quarter. Annual savings from the program are expected to be 100 million kronor ($14 million U.S.) with the full effect in the next fiscal year.
Elekta also held to its financial outlook for net sales growth of more than 8% and operating profit of more than 35% in fiscal 2010.
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Elekta taps new North American head, September 3, 2009
Elekta scores Brazilian order, August 14, 2009
Elekta nets U.S. orders, August 12, 2009
Elekta nets FDA OK for VMAT, July 24, 2009
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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).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
