| Autor: |
Lee MH; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792., Pickhardt SG; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792., Garrett JW; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792., Perez AA; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792., Zea R; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792., Valle KF; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792., Lubner MG; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792., Bates DDB; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY., Summers RM; Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD., Pickhardt PJ; Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792. |
| Abstrakt: |
BACKGROUND. CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiautomated tools to evaluate body composition in patients with colorectal cancer (CRC). OBJECTIVE. The purpose of this article is to assess the utility of fully automated body composition measures derived from pretreatment CT examinations in predicting survival in patients with CRC. METHODS. This retrospective study included 1766 patients (mean age, 63.7 ± 14.4 [SD] years; 862 men, 904 women) diagnosed with CRC between January 2001 and September 2020 who underwent pretreatment abdominal CT. A panel of fully automated artificial intelligence-based algorithms was applied to portal venous phase images to quantify skeletal muscle attenuation at the L3 lumbar level, visceral adipose tissue (VAT) area and subcutaneous adipose tissue (SAT) area at L3, and abdominal aorta Agatston score (aortic calcium). The electronic health record was reviewed to identify patients who died of any cause ( n = 848). ROC analyses and logistic regression analyses were used to identify predictors of survival, with attention to highest- and lowest-risk quartiles. RESULTS. Patients who died, compared with patients who survived, had lower median muscle attenuation (19.2 vs 26.2 HU, p < .001), SAT area (168.4 cm 2 vs 197.6 cm 2 , p < .001), and aortic calcium (620 vs 182, p < .001). Measures with highest 5-year AUCs for predicting survival in patients without ( n = 1303) and with ( n = 463) metastatic disease were muscle attenuation (0.666 and 0.701, respectively) and aortic calcium (0.677 and 0.689, respectively). A combination of muscle attenuation, SAT area, and aortic calcium yielded 5-year AUCs of 0.758 and 0.732 in patients without and with metastases, respectively. Risk of death was increased ( p < .05) in patients in the lowest quartile for muscle attenuation (hazard ratio [HR] = 1.55) and SAT area (HR = 1.81) and in the highest quartile for aortic calcium (HR = 1.37) and decreased ( p < .05) in patients in the highest quartile for VAT area (HR = 0.79) and SAT area (HR = 0.76). In 423 patients with available BMI, BMI did not significantly predict death ( p = .75). CONCLUSION. Fully automated CT-based body composition measures including muscle attenuation, SAT area, and aortic calcium predict survival in patients with CRC. CLINICAL IMPACT. Routine pretreatment body composition evaluation could improve initial risk stratification of patients with CRC. |
