Department of Computer Science
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Browsing Department of Computer Science by Subject "cancer"
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Item An association analysis of breast cancer with carotenoids(2023) Neumann, Samuel; El-Hajj, MohamadThe environment and the exposure individuals carry throughout their lifetime can gar- ner diverse effects on their health. This paper discusses the application of association analysis, to determine relationships between carcinogenesis and the human exposome. Human exposome data from the World Health Organization was analyzed to determine associations between human exposure and breast cancer. The discovered associations outline specific factors that may be associated with the prevention or causation of breast cancer. We discovered an association between biomarkers in specific biospecimens and breast cancer. Xanthophylls, measured in two different biospecimens, were determined to be associated with American breast cancer patients. The associations discovered may be of use in future cancer studies. This research is particularly interesting because of xanthophylls’ relationship to retinol, inhibiting oncogenesis. Providing support and data for such associations will encourage more research on the exposome’s effect on breast cancer and other conditions.Item Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients(2020) Cespedes Feliciano, Elizabeth M.; Popuri, Karteek; Cobzas, Dana; Baracos, Vickie E.; Beg, Mirza Faisal; Khan, Arafat Dad; Ma, Cydney; Chow, Vincent; Chow, Vincent; Prado, Carla M.; Xiao, Jingjie; Liu, Vincent; Chen, Wendy Y.; Meyerhardt, Jeffrey; Albers, Kathleen B.; Caan, Bette J.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 Automatic Body composition Analyser using Computed tomography image Segmentation (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 cm2, 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.