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Validation study of a new semi-automated software program for CT body composition analysis

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Background

Computed tomography (CT) has been increasingly used to quantify abdominal muscle and fat in clinical research studies, and multiple studies have shown importance of body composition in predicting clinical outcome. The purpose of study is to compare newly developed semi-automated software (BodyCompSlicer) to commercially available validated software (Slice-O-Matic) for CT body composition analysis.

Methods

CT scans of abdomen at L3 level in 30 patients were analyzed by two reviewers and using two softwares (BodyCompSlicer and Slice-O-Matic). Body composition analysis using BodyCompSlicer was semi-automated. The program automatically segmented subcutaneous fat (SF), skeletal muscle (SM), and visceral fat (VF) areas. Reviewers manually corrected the segmentation using computer–mouse interface as necessary. Body composition analysis using Slice-O-Matic was performed by manually segmenting each area using computer-mouse interface (brush tool). After segmentation, SM, SF, and VF areas were calculated using CT attenuation thresholds. Inter-observer and inter-software variability of measurements were analyzed using intraclass correlation coefficients (ICC) and coefficient of variation (COV).

Results

Inter-observer ICC and COV using BodyCompSlicer were 0.997 and 1.5% for SM, 1.000 and 0.8% for SF, and 1.000 and 1.0% for VF, whereas those using Slice-O-Matic were 0.993 and 2.5% for SM, 0.995 and 3.1% for SF, and 0.999 and 2.3% for VF. Inter-software ICCs and COV were 0.995–0.995 and 2.0–2.1% for SM, 0.991–0.994 and 3.4–3.9% for SF, and 0.998–0.998 and 2.8–3.3% for VF. Time to analyze 30 cases was 70–100 min and 150–180 min using BodyCompSlicer and Slice-O-Matic, respectively.

Conclusion

BodyCompSlicer is comparable to Slice-O-Matic for CT body composition analysis.

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References

  1. Martin L, Birdsell L, Macdonald N, et al. (2013) Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 31:1539–1547

    Article  PubMed  Google Scholar 

  2. Del Fabbro E, Parsons H, Warneke CL, et al. (2012) The relationship between body composition and response to neoadjuvant chemotherapy in women with operable breast cancer. Oncologist 17:1240–1245

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mir O, Coriat R, Dhooge M, et al. (2012) Feasibility of gemcitabine and oxaliplatin in patients with advanced biliary tract carcinoma and a performance status of 2. Anticancer Drugs 23:739–744

    Article  CAS  PubMed  Google Scholar 

  4. Psutka SP, Carrasco A, Schmit GD, et al. (2014) Sarcopenia in patients with bladder cancer undergoing radical cystectomy: impact on cancer-specific and all-cause mortality. Cancer 120:2910–2918

    Article  PubMed  Google Scholar 

  5. Joglekar S, Asghar A, Mott SL, et al. (2014) Sarcopenia is an independent predictor of complications following pancreatectomy for adenocarcinoma. J Surg Oncol 111:771–775

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kirihara Y, Takahashi N, Hashimoto Y, et al. (2013) Prediction of pancreatic anastomotic failure after pancreatoduodenectomy: the use of preoperative, quantitative computed tomography to measure remnant pancreatic volume and body composition. Ann Surg 257:512–519

    Article  PubMed  Google Scholar 

  7. Tan BH, Birdsell LA, Martin L, Baracos VE, Fearon KC (2009) Sarcopenia in an overweight or obese patient is an adverse prognostic factor in pancreatic cancer. Clin Cancer Res 15:6973–6979

    Article  CAS  PubMed  Google Scholar 

  8. Torres ML, Hartmann LC, Cliby WA, et al. (2013) Nutritional status, CT body composition measures and survival in ovarian cancer. Gynecol Oncol 129:548–553

    Article  PubMed  Google Scholar 

  9. Sabel MS, Lee J, Cai S, et al. (2011) Sarcopenia as a prognostic factor among patients with stage III melanoma. Ann Surg Oncol 18:3579–3585

    Article  PubMed  Google Scholar 

  10. Thoresen L, Frykholm G, Lydersen S, et al. (2013) Nutritional status, cachexia and survival in patients with advanced colorectal carcinoma. Different assessment criteria for nutritional status provide unequal results. Clin Nutr 32:65–72

    Article  CAS  PubMed  Google Scholar 

  11. Tsien C, Shah SN, McCullough AJ, Dasarathy S (2013) Reversal of sarcopenia predicts survival after a transjugular intrahepatic portosystemic stent. Eur J Gastroenterol Hepatol 25:85–93

    Article  PubMed  Google Scholar 

  12. Bray GA (2004) Medical consequences of obesity. J Clin Endocrinol Metab 89:2583–2589

    Article  CAS  PubMed  Google Scholar 

  13. Weltman A, Despres JP, Clasey JL, et al. (2003) Impact of abdominal visceral fat, growth hormone, fitness, and insulin on lipids and lipoproteins in older adults. Metabolism 52:73–80

    Article  CAS  PubMed  Google Scholar 

  14. Sironi AM, Gastaldelli A, Mari A, et al. (2004) Visceral fat in hypertension: influence on insulin resistance and beta-cell function. Hypertension 44:127–133

    Article  CAS  PubMed  Google Scholar 

  15. Thomas EL, Hamilton G, Patel N, et al. (2005) Hepatic triglyceride content and its relation to body adiposity: a magnetic resonance imaging and proton magnetic resonance spectroscopy study. Gut 54:122–127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cook MB, Greenwood DC, Hardie LJ, Wild CP, Forman D (2008) A systematic review and meta-analysis of the risk of increasing adiposity on Barrett’s esophagus. Am J Gastroenterol 103:292–300

    Article  PubMed  Google Scholar 

  17. Prado CM, Lieffers JR, McCargar LJ, et al. (2008) Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9:629–635

    Article  PubMed  Google Scholar 

  18. Mourtzakis M, Prado CM, Lieffers JR, et al. (2008) A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab 33:997–1006

    Article  PubMed  Google Scholar 

  19. Gomez-Perez SL, Haus JM, Sheean P, et al. (2015) Measuring abdominal circumference and skeletal muscle from a single cross-sectional computed tomography image: A Step-by-Step Guide for Clinicians Using National Institutes of Health ImageJ. JPEN J Parenter Enter Nutr 21:0148607115604149

    Google Scholar 

  20. Psutka SP, Boorjian SA, Moynagh MR, et al. (2015) Decreased skeletal muscle mass is associated with an increased risk of mortality after radical nephrectomy for localized renal cell cancer. J Urol 195:270–276

    Article  PubMed  Google Scholar 

  21. Mitsiopoulos N, Baumgartner RN, Heymsfield SB, et al. (1985) Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol 1998(85):115–122

    Google Scholar 

  22. Prado CM, Birdsell LA, Baracos VE (2009) The emerging role of computerized tomography in assessing cancer cachexia. Curr Opin Support Palliat Care 3:269–275

    Article  PubMed  Google Scholar 

  23. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  24. Irving BA, Weltman JY, Brock DW, et al. (2007) NIH ImageJ and Slice-O-Matic computed tomography imaging software to quantify soft tissue. Obesity 15:370–376 ((Silver Spring))

    Article  PubMed  Google Scholar 

  25. Hicks GE, Simonsick EM, Harris TB, et al. (2005) Cross-sectional associations between trunk muscle composition, back pain, and physical function in the health, aging and body composition study. J Gerontol A Biol Sci Med Sci 60:882–887

    Article  PubMed  Google Scholar 

  26. Keller A, Gunderson R, Reikeras O, Brox JI (2003) Reliability of computed tomography measurements of paraspinal muscle cross-sectional area and density in patients with chronic low back pain. Spine 28:1455–1460 ((Phila Pa 1976))

    PubMed  Google Scholar 

  27. Potretzke AM, Schmitz KH, Jensen MD (2004) Preventing overestimation of pixels in computed tomography assessment of visceral fat. Obes Res 12:1698–1701

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA

    Naoki Takahashi, Baiyu Chen & Michael R. Moynagh

  2. Department of Surgery, Mayo Clinic, 200 First Street SW, Rochester, MN, USA

    Motokazu Sugimoto

  3. Department of Urology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA

    Sarah P. Psutka

  4. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN, USA

    Rickey E. Carter

Authors
  1. Naoki Takahashi
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  2. Motokazu Sugimoto
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  3. Sarah P. Psutka
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  4. Baiyu Chen
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  5. Michael R. Moynagh
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  6. Rickey E. Carter
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Corresponding author

Correspondence to Naoki Takahashi.

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Funding

No funding was received for this study.

Conflict of interest

Mayo Clinic and one author (Naoki Takahashi) have intellectual property rights relevant to the technology studied in this paper and a potential financial interest. The other authors had control over the data and information submitted for publication.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Takahashi, N., Sugimoto, M., Psutka, S.P. et al. Validation study of a new semi-automated software program for CT body composition analysis. Abdom Radiol 42, 2369–2375 (2017). https://doi.org/10.1007/s00261-017-1123-6

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  • DOI: https://doi.org/10.1007/s00261-017-1123-6

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