Open AccessEvaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients
Author(s) -
Cespedes Feliciano Elizabeth M.,
Popuri Karteek,
Cobzas Dana,
Baracos Vickie E.,
Beg Mirza Faisal,
Khan Arafat Dad,
Ma Cydney,
Chow Vincent,
Prado Carla M.,
Xiao Jingjie,
Liu Vincent,
Chen Wendy Y.,
Meyerhardt Jeffrey,
Albers Kathleen B.,
Caan Bette J.
Publication year - 2020
Publication title -
journal of cachexia, sarcopenia and muscle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.803
H-Index - 66
eISSN - 2190-6009
pISSN - 2190-5991
DOI - 10.1002/jcsm.12573
Subject(s) - medicine , jaccard index , breast cancer , cancer , colorectal cancer , nuclear medicine , segmentation , adipose tissue , body mass index , confidence interval , radiology , artificial intelligence , computer science , pattern recognition (psychology)
Background Body composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but A utomatic B ody composition A nalyser using C omputed tomography image S egmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset. Methods Among patients with non‐metastatic colorectal ( n = 3102) and breast ( n = 2888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel‐level image overlap using Jaccard scores and agreement between methods using intra‐class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area. Results Mean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra‐class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1–2% versus manual analysis: mean differences were small at −2.35, −1.97 and −2.38 cm 2 , respectively. ABACS's performance was lowest for the <2% of patients who were underweight or had anatomic abnormalities. ABACS and manual analysis produced similar associations with mortality; comparing the lowest to highest tertile of skeletal muscle from ABACS versus manual analysis, the HRs were 1.23 (95% CI: 1.00–1.52) versus 1.38 (95% CI: 1.11–1.70) for colorectal cancer patients and 1.30 (95% CI: 1.01–1.66) versus 1.29 (95% CI: 1.00–1.65) for breast cancer patients. Conclusions In the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non‐metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.