Computed tomography: an efficient, opportunistic method for assessing body composition and predicting adverse outcomes in cancer patients

Liguori, Adriano de Araújo Lima; Fayh, Ana Paula Trussardi

doi:10.1590/0100-3984.2023.56.6e3-en

In recent years, we have seen an increase in the use of biomarkers, including those related to body composition, in the field of radiology. In that context, computed tomography (CT) and magnetic resonance imaging are considered the gold standards for evaluating body composition(¹,²). Specifically on CT image analysis, it is possible to determine the mass of the internal organs and to differentiate among specific tissues, such as visceral adipose tissue, subcutaneous adipose tissue, and muscle groups. In addition, CT image analysis can provide information on skeletal muscle mass (SMM) and skeletal muscle density (SMD), which are closely related to fatty infiltration of the muscle, and their impact on muscle function(³,⁴). Therefore, CT imaging has been widely used as an opportunistic method for assessing body composition in diseases for which imaging examinations are performed for diagnosis and follow-up, such as cancer(⁵,⁶,⁷).

In the literature, there is robust evidence of an association between low SMD and postoperative complications in patients with various types of cancer(⁸,⁹,¹⁰,¹¹,¹²). However, studies of patients with renal cancer are still scarce. One systematic review and meta-analysis that included 28 studies, with a collective total of 6,608 patients with renal cell carcinoma (RCC), sought to identify associations between body composition and clinical outcomes(¹³). Although the authors found that low SMM and low SMD were associated with higher overall mortality, the heterogeneity of the studies made it impossible to perform a meta-analysis focusing on perioperative outcomes. That demonstrates the importance of carrying out new studies seeking to investigate the effect that muscle characteristics (e.g., SMM and SMD) on surgical outcomes in this population, as was done in the study entitled “Impact of preoperative body composition in patients with renal cell carcinoma submitted to surgical treatment”, conducted by Carniatto et al.(¹⁴) and published in this issue of Radiologia Brasileira. The authors evaluated the impact of preoperative body composition in patients undergoing surgical treatment for RCC. Their study was retrospective, including 52 patients with RCCs, the majority of which were of the clear cell subtype. Although they found no association between preoperative body composition and the frequency of perioperative complications in patients undergoing partial or total nephrectomy, they did find that the skeletal muscle gauge (the product of the SMM index and the SMD) was associated with the length of hospital stay and with overall survival.

The main limitations of the Carniatto et al.(¹⁴) study are its retrospective methodology, its small sample size, and the fact that the statistical analysis did not reveal a significant association between preoperative body composition and perioperative complications. To delve deeper into this field, it is expected that prospective studies involving larger samples of patients with clear cell RCC will provide additional information.

There are some obstacles to the widespread application of knowledge in the field of body composition in daily radiology practice, such as limited access to specific software and a lack of remuneration for the time dedicated to post-processing. As a solution and future perspective, we hope that artificial intelligence algorithms(¹⁵), especially those dedicated to automated or semi-automated segmentation, will be increasingly incorporated into work routines, which could allow the definitive use of body composition data and their inclusion in radiology reports.

REFERENCES

¹ Sheean P, Gonzalez MC, Prado CM, et al. American Society for Parenteral and Enteral Nutrition clinical guidelines : the validity of body composition assessment in clinical populations. JPEN J Parenter Enteral Nutr. 2020; 44:12–43.
² Fayh APT, Sousa IM, Gonzalez MC. New insights on how and where to measure muscle mass. Curr Opin Support Palliat Care. 2020;14:316–23.
³ Rodrigues CS, Chaves GV. Skeletal muscle quality beyond average muscle attenuation: a proposal of skeletal muscle phenotypes to predict short-term survival in patients with endometrial cancer. J Natl Compr Canc Netw. 2018;16:153–60.
⁴ Alves VA, Fayh APT, Queiroz SA, et al. Muscle mass evaluation in hospitalized patients: comparison between doubly indirect methods. Clin Nutr ESPEN. 2024;59:188–93.
⁵ Lenchik L, Lenoir KM, Tan J, et al. Opportunistic measurement of skeletal muscle size and muscle attenuation on computed tomography predicts 1-year mortality in medicare patients. J Gerontol A Biol Sci Med Sci. 2019; 74:1063–9.
⁶ Zopfs D, Theurich S, Hokamp NG, et al. Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition. Eur Radiol. 2020;30:1701–8.
⁷ Cushen SJ, Power DG, Murphy KP, et al. Impact of body composition parameters on clinical outcomes in patients with metastatic castrate-resistant prostate cancer treated with docetaxel. Clin Nutr ESPEN. 2016;13:e39–e45.
⁸ Carvalho ALM, Gonzalez MC, Sousa IM, et al. Low skeletal muscle radiodensity is the best predictor for short-term major surgical complications in gastrointestinal surgical cancer : a cohort study. PLoS One. 2021; 16:e0247322.
⁹ Boer BC, de Graaff F, Brusse-Keizer M, et al. Skeletal muscle mass and quality as risk factors for postoperative outcome after open colon resection for cancer. Int J Colorectal Dis. 2016;31:1117–24.
¹⁰ Paula NS, Bruno KA, Aredes MA, et al. Sarcopenia and skeletal muscle quality as predictors of postoperative complication and early mortality in gynecologic cancer. Int J Ginecol Cancer. 2018;28:412–20.
¹¹ Zhang J, Chen L, Hu X, et al. Preoperative pectoralis muscle radiodensity as a risk factor for postoperative complications after thoracoscopic lobectomy for non-small cell lung cancer. Ann Palliat Med. 2021;10:5444–54.
¹² Xiao J, Caan BJ, Feliciano EMC, et al. Association of low muscle mass and low muscle radiodensity with morbidity and mortality for colon cancer surgery. JAMA Surg. 2020;155:942–9.
¹³ Vrieling A, Kampman E, Knijnenburg NC, et al. Body composition in relation to clinical outcomes in renal cell cancer : a systematic review and meta-analysis. Eur Urol Focus. 2018;4:420–34.
¹⁴ Carniatto LN, Bitencourt AGV, Miola TM, et al. Impact of preoperative body composition in patients with renal cell carcinoma submitted to surgical treatment. Radiol Bras. 2023;56:317–20.
¹⁵ Borrelli P, Kaboteh R, Enqvist O, et al. Artificial intelligence-aided CT segmentation for body composition analysis: a validation study. Eur Radiol Exp. 2021;5:11.

Publication Dates

Publication in this collection
04 Mar 2024
Date of issue
Nov-Dec 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

[1] ¹ Sheean P, Gonzalez MC, Prado CM, et al. American Society for Parenteral and Enteral Nutrition clinical guidelines : the validity of body composition assessment in clinical populations. JPEN J Parenter Enteral Nutr. 2020; 44:12–43.

[2] ² Fayh APT, Sousa IM, Gonzalez MC. New insights on how and where to measure muscle mass. Curr Opin Support Palliat Care. 2020;14:316–23.

[3] ³ Rodrigues CS, Chaves GV. Skeletal muscle quality beyond average muscle attenuation: a proposal of skeletal muscle phenotypes to predict short-term survival in patients with endometrial cancer. J Natl Compr Canc Netw. 2018;16:153–60.

[4] ⁴ Alves VA, Fayh APT, Queiroz SA, et al. Muscle mass evaluation in hospitalized patients: comparison between doubly indirect methods. Clin Nutr ESPEN. 2024;59:188–93.

[5] ⁵ Lenchik L, Lenoir KM, Tan J, et al. Opportunistic measurement of skeletal muscle size and muscle attenuation on computed tomography predicts 1-year mortality in medicare patients. J Gerontol A Biol Sci Med Sci. 2019; 74:1063–9.

[6] ⁶ Zopfs D, Theurich S, Hokamp NG, et al. Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition. Eur Radiol. 2020;30:1701–8.

[7] ⁷ Cushen SJ, Power DG, Murphy KP, et al. Impact of body composition parameters on clinical outcomes in patients with metastatic castrate-resistant prostate cancer treated with docetaxel. Clin Nutr ESPEN. 2016;13:e39–e45.

[8] ⁸ Carvalho ALM, Gonzalez MC, Sousa IM, et al. Low skeletal muscle radiodensity is the best predictor for short-term major surgical complications in gastrointestinal surgical cancer : a cohort study. PLoS One. 2021; 16:e0247322.

[9] ⁹ Boer BC, de Graaff F, Brusse-Keizer M, et al. Skeletal muscle mass and quality as risk factors for postoperative outcome after open colon resection for cancer. Int J Colorectal Dis. 2016;31:1117–24.

[10] ¹⁰ Paula NS, Bruno KA, Aredes MA, et al. Sarcopenia and skeletal muscle quality as predictors of postoperative complication and early mortality in gynecologic cancer. Int J Ginecol Cancer. 2018;28:412–20.

[11] ¹¹ Zhang J, Chen L, Hu X, et al. Preoperative pectoralis muscle radiodensity as a risk factor for postoperative complications after thoracoscopic lobectomy for non-small cell lung cancer. Ann Palliat Med. 2021;10:5444–54.

[12] ¹² Xiao J, Caan BJ, Feliciano EMC, et al. Association of low muscle mass and low muscle radiodensity with morbidity and mortality for colon cancer surgery. JAMA Surg. 2020;155:942–9.

[13] ¹³ Vrieling A, Kampman E, Knijnenburg NC, et al. Body composition in relation to clinical outcomes in renal cell cancer : a systematic review and meta-analysis. Eur Urol Focus. 2018;4:420–34.

[14] ¹⁴ Carniatto LN, Bitencourt AGV, Miola TM, et al. Impact of preoperative body composition in patients with renal cell carcinoma submitted to surgical treatment. Radiol Bras. 2023;56:317–20.

[15] ¹⁵ Borrelli P, Kaboteh R, Enqvist O, et al. Artificial intelligence-aided CT segmentation for body composition analysis: a validation study. Eur Radiol Exp. 2021;5:11.