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. 2023 Nov 15;15(22):4792. doi: 10.3390/nu15224792
Cancer Location Authors (Year) Country Study Design Objective Sample Size (n) Gender (n/%) BMI (kg/m2) Mean Age ± SD BIA Device Frequencies (kHz)/ Equation Sensitivity/
Specificity 		Main Conclusions
Head and neck cancer Ding et al., 2018 [32] China Prospective study To investigate body composition changes in patients with nasopharyngeal carcinoma undergoing concurrent chemoradiotherapy and assess the use of the PG-SGA 1 and the ESPEN diagnostic criteria as evaluation methods. 48 M: 36 (75%)
F: 12 (25%)		 23.34 ± 0.6 47 InBody S10 Biospace Model JMW140, Seoul, Republic of Korea NM/NM NM 2 Body composition parameters, specifically FFMI 3, are important in the diagnosis of malnutrition. BIA 4 should be implemented for nutritional assessment.
Grossberg et al., 2021 [5] USA Prospective study To explore if BIA is useful to identify sarcopenia associated with decreased survival in HNC 5 patients treated with RT 6. 48 M: 40 (83%)
F: 8 (17%)		 M: 30 ± 5
F: 24 ± 5		 60 ± 12 SECA mBCA 515 scale, Hamburg, Germany NM/NM SMMI 7 (M)
92%/ 93%
SMM 8 (F)
100%/ 0%		 BIA showed high sensitivity and specificity to identify patients with sarcopenia, a negative prognostic factor in HNC. BIA seems a practical solution to identify patients with sarcopenia in routine clinical practice.
Jager-Wittenaar et al., 2014 [9] Netherlands Prospective study To assess the validity of BIA with Geneva equation, for the assessment of FFM 9 in patients with HNC in pretreatment and posttreatment. 24 M: 20 (83%)
F: 4 (17%)		 23.7 ± 4.7 60.4 ± 8.3 BodyStat QuadScan 4000, BodyStat, Douglas, Isle of Man, UK 5, 50, 200/
Geneva equation 		NM BIA seems to be valuable to assess FFM in HNC patients in the clinic, with good concordance in group mean-level comparisons.
Lundberg et al., 2017 [16] Finland Prospective study To describe BIA measures in Finnish patients with HNC at diagnosis. 41 M: 32 (78%)
F: 9 (22%)		 M: 25.2
F: 27.0		 62.5 SECA mBCA 515 scale, Hamburg, Germany 50/NM NM BIA was fast, non-invasive, inexpensive tool and both PhA 10 and BIVA 11 are easily analyzed by an inexperienced clinician. PhA and BIVA seemed useful and also provided information on body composition.
Lundberg et al., 2019 [25] Finland Prospective study To evaluate correlation of BIA with complication rate and other related indicators after major HNC surgery. 61 M: 47 (77%)
F: 14 (23%)		 PhA low: 23.2
PhA normal range: 27.3		 61 SECA mBCA 515 scale, Hamburg, Germany NM/NM NM BIA is cheap, quick, easy, non-invasive and feasible to analyze body composition in patients with cancer. BIA can be of clinical value in preoperative risk evaluation and might reduce complications and hospital stay.
Breast
cancer 		Bell et al., 2020 [42] Canada & USA Cross-sectional study To compare the ability of previously published SF-BIA 12 equations that predict FFM with a reference method (DXA 13) in a group of patients with BC 14 undergoing treatment. 48 F: 48 (100%) 27.5 ± 5.5 52 ± 10 BIA Quantum IV, Clinton Township, MI, USA 50/NM NM BIA overestimated FFM, and underestimated FM 15 in patients with BC. Future studies are needed to develop and validate BIA prediction equations specific to BC throughout the disease trajectory.
Jung et al., 2020 [33] Republic of Korea Prospective study To analyze changes in weight, body composition, and physical activity in patients with BC under adjuvant chemotherapy. 37 F: 37 (100%) 23.42 ± 3.06 50.9 ± 9.4 Inbody S10, Seoul, Republic of Korea 50/NM NM No significant change in weight, body composition, and physical activity during adjuvant chemotherapy in patients with BC. Using BIA could provide more concrete and objective results.
Wilczyński et al., 2020 [46] Poland Cross-sectional
study		 To investigate body composition of women following radical mastectomy. 60 F: 60 (100%)
SG 16: 30
CG 17: 30 		SG: 27.56
CG: 24.96		 SG: 55.07 ± 4.71
CG: 50.27 ± 5.13		 TANITA MC-780, Tokyo, Japan NM/NM NM The use of BIA does not cause ionisation and is a gold standard in the field of body composition analysis.
Esophageal cancer Powell et al., 2020 [26] United Kingdom Prospective
study 		To assess the association between BIA defined low FFM, in patients undergoing surgery for OC 18 and clinical outcomes, related to post-operative morbidity graded by Clavien- Dindo MSS 19, and both Disease-Free and OS 20. 122 M: 104 (85.2%)
F: 18 (14.8%)		 NMV 21: 28.1
LMV: 20.3		 65 Maltron
Bioscan 920, Essex, UK		 0.5, 50, 100/
NM		 NM BIA derived LMV 22 was a prognostic indicator in patients undergoing potentially curative oesophagectomy for cancer.
Hepatocellular cancer Lee et al., 2021 [30] Republic of Korea Prospective
Study		 To explore if PhA, presence of sarcopenia, and EI 23, measured through BIA, affect postoperative complications and prognosis after liver resection in patients with HCC 24. 79 M: 66 (83.5%)
F: 13 (16.5%)		 23.7 ± 3.3 56.1 ± 10.9 InBody 770 scanner, Seoul, Republic of Korea 1, 5, 50, 260, 500, 1000/
NM		 EI (ECW 25/TBW 26)
68.6%/ 70.5%		 BIA can provide additional clinical information regarding postoperative complications in patients with HCC scheduled for surgery.
Skroński et al., 2018 [27] Poland Prospective
Study		 To evaluate changes in body composition before and after resection of liver tumors and radiofrequency ablation of lesions. 50 M: 23 (46%)
F: 27 (54%)		 NM 60 BIA 101 Anniversary analyzer, Akern, Florence, Italy NM/NM NM BIA is a suitable method to assess changes in body composition of patients undergoing liver resection.
Pancreatic cancer Mikamori et al., 2016 [28] Japan Prospective
Study		 To explore postoperative changes in body composition of patients submitted to PG 27 Vs GT 28, and assess nutrition with BIA postoperatively. 60 M: 43 (71.7%)
F: 17 (28.3%)		 PD:21.4 ± 2.7
TG 29:21.6 ± 3.0
DG 30:21.9 ± 3.7		 65.8 ± 7.4 InBody 720, Tokyo, Japan NM/NM NM BIA can be used to assess body composition in patients who have undergone surgery.
Gastric
cancer 		Gao et al., 2020 [43] China Cross-sectional
Study		 To investigate the accuracy of BIA in estimating VFA 31 in individuals with GC 32 in the Chinese population, as well as to determine the threshold for diagnosing visceral obesity using BIA. 157 M: 109 (69.4%)
F: 48 (30.6%)		 23.28 ± 2.93 60.61 ± 11.95 InBody 720, Seoul, Republic of Korea NM/NM VFA
65.6/88.2%		 VFA given by CT 33 and BIA had significant correlation and satisfactory reliability. Nevertheless, the absolute values of the two methods were not interchangeable directly.
Colorectal cancer Jones et al., 2020 [34] United Kingdom & Australia Prospective
study 		To determine the agreement between BIA and MAMC 34 against CT scans for the measurement of muscle mass and identification of sarcopenia in patients with CRC 35. 100 M: 67 (67%)
F: 33 (33%)		 25.8 ± 4.7 69.6 ± 11.5 Bodystat 1500 machine, Douglas, Isle of Man, UK 50/NM BIA low MM 36
80%/52%
MAMC low ‘MM
38% 88% 		BIA and MAMC were inadequate to measure muscle mass in CRC patients Vs CT measurements at L3. Neither method can match the high precision of CT scans.
Kim et al., 2020 [31] Republic of Korea Prospective
Study		 To explore relationships between CT scans at the L3 level for muscle assessment and total SMM assessed by BIA in CRC patients. 50 M: 28 (56%)
F: 22 (44%)		 24.3 ± 3.4 63.4 InBody 770, Seoul, Republic of Korea 50, 1000/
NM		 NM BIA could be an alternative method to CT scan, and it could be a non-invasive and cost-effective tool for the assessment of body composition, including SMM in a CRC patient that could be associated with clinical results.
Palle et al., 2016 [35] Denmark Prospective
Study		 To assess associations between single cross-sectional thighs given by MRI 37, SMM as reference and multi-frequency BIA FFM in CRC patients undergoing chemotherapy. 18 M: 10 (56%)
F: 8 (44%)		 M: 25.3 ± 2.6
F: 23.1 ± 3.9		 67 ± 6 Tanita MC780MA, Tokyo, Japan NM/NM NM BIA and ST 38 were the best alternatives to MRI since they showed constant and subsequently corrected errors.
Ræder et al., 2018 [40] Norway Prospective study To evaluate two different BIA devices, a whole-body BIA and a segmental BIA device, against DXA in CRC patients, and to investigate the ability of 14 empiric equations to predict DXA FFM. 43 M: 17 (39.5%)
F: 26 (60.5%)		 25.8 67.0 BIA, BIA-101, Würzburg, Germany
Seca mBCA515, Birmingham, UK		 50/
Geneva equation 		Whole-body BIA
78.6%/100%
Segmental BIA
85.7%/77.8%		 Both BIA-devices showed good ability to detect low FFM with an optimal equation. We recommend using one of these combinations of device and equation to determine FFM in this population.
Song et al., 2019 [41] Republic of Korea Retrospective
study 		To determine the relationship between body composition and PLR 39 in patients with CRC. 110 M: 77 (70%)
F: 33 (30%)		 NM 68.3 ± 9.6 InBody 770, Biospace, Seoul, Republic of Korea NM/NM NM Fat and muscle indices measured by InBody 770 were related to PLR in CRC. These results suggest that low muscle and fat may be related to poor prognosis of CRC.
Souza et al., 2020 [36] Brazil & Sweden Prospective
Study		 To assess the agreement between computed tomography (CT) and surrogate methods employed in clinical practice for the assessment of low muscle mass. 188 M: 108 (57%)
F: 80 (43%)		 27.1 ± 5.4 61.0 ± 11.4 Quantum II, RJL Systems, Detroit, MI, USA 50/
Janssen equation		 SMI-BIA
93.9%/54.2%		 Physical examination Vs CT had the best agreement to assess low muscle mass. Low muscle mass given by PG-SGA, BIA, and CT showed similar prognostic values for survival.
Szefel et al., 2020 [37] Poland Prospective
Study		 To determine the effectiveness of BIA to detect and monitor cancer cachexia CC 40 in patients with CRC. 158 M: 72 (46%)
F: 86 (54%)		 NM NM Seca mBCA525, seca GmbH and Co., Hamburg, Germany 50/NM FFMI-BIA (M)
100%/39%
FFMI-BIA (F)
88%/50%		 BIA identified differences in body composition according to cancer stage and advancement of CC. After CRC diagnosis, periodic assessment by BIA seems useful.
Pancreatic, gastric and colorectal cancer Dzierżek et al., 2020 [38] Poland Prospective
Study		 To assess body composition and its impact on patients undergoing surgery due to GC, PC 41, and CRC. 56 M: 31 (55.4%)
F: 25 (44.6%)		 25.8 66.0 BIA-101 Akern, Italy 50/NM NM BIA can be easy and effective to assess body composition and its change in patients undergoing major surgery.
Lung cancer Hansen et al., 2021 [44] Denmark Cross-sectional
Study		 To investigate the agreement between body composition recorded with BIA and software analysis of CT scans of patients with cancer with a particular emphasis on MM. 60 M: 35 (58.3%)
F: 25 (41.7%) 		23.96 ± 3.78 67.07 ± 7.54 Tanita Segmental Body Composition Analyzer (BC-418), Tokyo, JapanC 50/NM NM BIA and CT image analysis were not comparable to assess body composition. BIA overestimated MM and underestimated FM with LoA 42 outside that of the clinically acceptable difference. Bias was lower in the subgroup analysis, but not to acceptable levels.
Skin cancer Zopfs et al., 2020 [29] Germany Cross-sectional study To analyze if anthropometric measures, and body composition derived from BIA, as well as clinical anthropometric data, can be estimated from simple and reliable 2D measurements in routine CT scans. 62 M: 31 (50%)
F: 31 (50%)		 NM 63.32 ± 15.92 Seca mBCA 515, Hamburg, Germany NM/NM NM Using simple measurements in a single axial CT slice, body composition can accurately be determined in clinical examinations by using simple measurements.
All cancer types Cereda et al., 2021 [39] Italy & Germany Prospective
study 		To assess the potential prognostic role of FFMI in addition to BMI 43 and WL 44). The association with QoL 45 was also explored. 1217 M: 713 (58.6%)
F: 504 (41.4%)		 23.6 ± 4.3 63.0 ± 12.6 NUTRILAB Akern srl, Florence, Italy
Nutriguard-M Data Input GmbH, Darmstadt, Germany		 NM/
Equation of Sun et al. [77]		 NM In all patients with cancer, altered body composition should always be considered as an additional phenotypic criterion of poor prognosis and BIA provides the possibility of multiple, non-invasive bedside assessments.
Mueller et al., 2020 [45] Germany Cross-sectional
Study		 To determine if BIA is a reliable diagnostic tool even in patients with cancer with and without malnutrition, and could thus be safely used for short-term follow-up or in non-specialized/out-patient settings. 118
NMG: 64
MG: 54		 NMG 46
M: 29 (45.3%)
F: 35 (54.7%)
MG 47
M: 28 (51.9%)
F: 26 (48.1%)		 NMG: 25.0
MG: 22.5		 NMG: 56
MG: 63		 BIA 101 anniversary SE, Akern Bioresearch, Italy 50/NM NM BIA is a reliable diagnostic tool for the assessment of muscle and FM, even in patients with malnutrition, and could be utilized for the early detection and short-term follow-up of malnutrition and cachexia.
1 Patient Generated Subjective Global Assessment. 2 Not mentioned. 3 Fat-free mass index. 4 Bioelectrical impedance analysis. 5 Head and neck cancer. 6 Radiotherapy. 7 Skeletal muscle mass index. 8 Skeletal muscle mass. 9 Fat-free mass. 10 Phase angle. 11 Bioelectrical impedance vector analysis. 12 Single-frequency BIA. 13 Dual-energy X-ray absorptiometry. 14 Breast cancer. 15 Fat mass. 16 Study group. 17 Control group. 18 Oesophageal Cancer. 19 Morbidity Severity Score. 20 Overall Survival. 21 Normal muscle volume. 22 Low muscle volume. 23 Edema index. 24 Hepatocellular carcinoma. 25 Extracellular water. 26 Total body water. 27 Pancreaticoduodenectomy. 28 Gastrectomy. 29 Total gastrectomy. 30 Distal gastrectomy. 31 Visceral fat area. 32 Gastric cancer. 33 Computed tomography. 34 Mid arm muscle circumference. 35 Colorectal cancer. 36 Muscle mass. 37 Magnetic resonance imaging. 38 Skin-fold thickness. 39 Platelet-to-lymphocyte ratio. 40 Cancer cachexia. 41 Pancreatic cancer. 42 Limits of agreement. 43 Body mass index. 44 Weight loss. 45 Quality of life. 46 No Malnutrition Group. 47 Malnutrition Group.