Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

Journal: AJR. American journal of roentgenology
PMID:

Abstract

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). 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. 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 ( = 848). ROC analyses and logistic regression analyses were used to identify predictors of survival, with attention to highest- and lowest-risk quartiles. Patients who died, compared with patients who survived, had lower median muscle attenuation (19.2 vs 26.2 HU, < .001), SAT area (168.4 cm vs 197.6 cm, < .001), and aortic calcium (620 vs 182, < .001). Measures with highest 5-year AUCs for predicting survival in patients without ( = 1303) and with ( = 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 ( < .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 ( < .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 ( = .75). Fully automated CT-based body composition measures including muscle attenuation, SAT area, and aortic calcium predict survival in patients with CRC. Routine pretreatment body composition evaluation could improve initial risk stratification of patients with CRC.

Authors

  • Matthew H Lee
    Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792.
  • Silas G Pickhardt
    Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792.
  • John W Garrett
    From the Departments of Medical Physics (R.Z., X.T., C.Z., D.G., J.W.G., K.L., S.B.R., G.H.C.) and Radiology (M.L.S., J.W.G., K.L., S.B.R., G.H.C.), University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Ave, Madison, WI 53705; and Department of Radiology, Henry Ford Health System, Detroit, Mich (Z.Q., N.B.B., T.K.S., J.D.N,).
  • Alberto A Perez
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
  • Ryan Zea
    Department of Radiology, The University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.
  • Kevin Franco Valle
    Department of Radiology, University of Wisconsin School of Medicine & Public Health, 600 Highland Ave, Madison, WI 53792.
  • Meghan G Lubner
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Avenue, Madison, WI 53792, USA. Electronic address: mlubner@uwhealth.org.
  • David D B Bates
    Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, Box 29, New York, NY, 10065, USA.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.
  • Perry J Pickhardt
    University of Wisconsin Medical School, Department of Radiology, Madison, Wisconsin, United States.