Call for standardization in assessment and reporting of muscle and adipose change using computed tomography analysis in oncology: A scoping review

Abstract

Investigators are increasingly measuring skeletal muscle (SM) and adipose tissue (AT) change during cancer treatment to understand impact on patient outcomes. Recent meta‐analyses have reported high heterogeneity in this literature, representing uncertainty in the resulting estimates. Using the setting of palliative‐intent chemotherapy as an exemplar, we aimed to systematically summarize the sources of variability among studies evaluating SM and AT change during cancer treatment and propose standards for future studies to enable reliable meta‐analysis. Studies that measured computed tomography‐defined SM and/or AT change in adult patients during palliative‐intent chemotherapy for solid tumours were included, with no date or geographical limiters. Of 2496 publications screened by abstract/title, 83 were reviewed in full text and 38 included for extraction, representing 34 unique cohorts across 8 tumour sites. The timing of baseline measurement was frequently defined as prior to treatment, while endpoint timing ranged from 6 weeks after treatment start to time of progression. Fewer than 50% specified the actual time interval between measurements. Measurement error was infrequently discussed (8/34). A single metric (cm²/m², cm² or %) was used to describe SM change in 18/34 cohorts, while multiple metrics were presented for 10/34 and no descriptive metrics for 6/34. AT change metrics and sex‐specific reporting were available for 10/34 cohorts. Associations between SM loss and overall survival were evaluated in 24 publications, with classification of SM loss ranging from any loss to >14% loss over variable time intervals. Age and sex were the most common covariates, with disease response in 50% of models. Despite a wealth of data and effort, heterogeneity in study design, reporting and statistical analysis hinders evidence synthesis regarding the severity and outcomes of SM and AT change during cancer treatment. Proposed standards for study design include selection of homogenous cohorts, clear definition of baseline/endpoint timing and attention to measurement error. Standard reporting should include baseline SM and AT by sex, actual scan interval, SM and AT change using multiple metrics and visualization of the range of change observed. Reporting by sex would advance understanding of sexual dimorphism in SM and AT change. Evaluating the impact of tissue change on outcomes requires adjustment for relevant covariates and concurrent disease response. Adoption of these standards by researchers and publishers would alter the current paradigm to enable meta‐analysis of future studies and move the field towards meaningful application of SM and AT change to clinical care.

Introduction

The measurement of skeletal muscle (SM) and adipose tissue (AT) in oncology research has become increasingly relevant in an era of precision medicine. These measures have potential to inform personalized cancer care and treatment planning—increasing safety and identifying those who require additional multidisciplinary care. Computed tomography (CT) scans that are routinely performed in the oncology setting for tumour evaluation have secondary value as an accurate means of body composition measurement. SM and AT cross‐sectional areas measured from single magnetic resonance imaging (MRI)/CT images in the lumbar region are linearly related to whole‐body SM and AT, both in healthy adults and in cancer patients. ¹ , ² The specifics of this method have been thoroughly described elsewhere. ² , ³ In short, trained observers select a single axial image slice at the middle of the third lumbar vertebra (L3), which is imported into image analysis software. SM and AT are semi‐automatically delineated based on established Hounsfield unit ranges and manually corrected as required.

Several metrics are used to describe SM and AT based on CT analysis at L3; SM and total AT cross‐sectional areas at L3 (in cm²) are the raw output of single slice analysis and correlate with whole‐body SM mass and whole‐body fat mass, respectively, according to published regression equations. ¹ , ² SM area at L3 can be normalized for height as SM index (SMI; cm²/m²) for comparison between people of different heights. ⁴ With respect to body composition change, cross‐sectional area (cm²) and index units (cm²/m²) are valid metrics of absolute change, while relative change (%) from baseline is relevant for assessing severity of loss.

Using CT‐based analysis of SM and AT, researchers have defined various thresholds of SMI at cancer diagnosis associated with poor prognosis. ⁵ , ⁶ , ⁷ , ⁸ , ⁹ , ¹⁰ The subsequent measurement of direction and intensity of change in SM and AT during cancer treatment may enhance the prediction of treatment outcomes. For most patients with solid tumours, more than one routine CT scan is taken over the cancer trajectory, enabling opportunistic measures of SM and AT over time. Investigators are increasingly measuring SM and AT change during chemotherapy treatment to explore associations with patient outcomes. These analyses have resulted in an abundance of reported data, yet heterogeneity precludes valid meta‐analysis, which requires that studies be sufficiently congruent with respect to population, exposure, outcome measure and time interval of measurement. ¹¹

Three recent meta‐analyses of body composition change during cancer treatment have been published; all report significant heterogeneity among included studies, suggesting uncertainty of the resulting estimates. Jang et al. endeavoured to determine the mean change in SM observed during any chemotherapy regimen (neoadjuvant, adjuvant, curative or palliative) for any cancer type. ¹² Of the potential 92 studies, the authors excluded 77 because of insufficient reporting (i.e., change was not reported in cm²/m², or medians and ranges were reported instead of mean and standard deviations). Significant heterogeneity (I ² = 86.83%) was found among included studies, related to variations in cancer type, stage, treatment regimen and treatment duration. The results of this meta‐analysis are difficult to apply to any specific patient group, given the inclusion of all disease sites, stages and chemotherapy regimens.

Another meta‐analysis was performed by Xu et al. with the aim of describing the prognostic impact of SM change during neoadjuvant treatment for gastrointestinal cancer. ¹³ These authors clearly defined the disease site (gastrointestinal) and setting (neoadjuvant) but found heterogeneity related to population studied, treatment protocol, outcome measurement and reporting. The meta‐analysis included studies measuring SM change with bioelectric impedance analysis, psoas muscle on CT and cross‐sectional muscle area on CT, and SM change was reported variously in total lean body mass change (kg), L3 psoas or SMI change (cm²/m²), L3 area change (cm²), and using variable classifications of ‘muscle loss’. Finally, the time between measurements in each study was not addressed as a source of heterogeneity.

Finally, Wang et al. conducted a meta‐analysis of studies describing SM change and its prognostic value during neoadjuvant therapy for oesophageal and oesophagogastric junction cancers. ¹⁴ While this protocol had the most specific treatment and disease‐site criteria of the three reviews mentioned, the authors chose to process the reported results from each included study to estimate the observed change in SMI units (cm²/m²), as few studies reported these units. The overall estimate of change during neoadjuvant treatment was found to have high heterogeneity, with an I ² value of 88.3%, in part due to treatment regimen. The authors did not describe or discuss the impact of time between measurements on the meta‐analysis results.

In summary, despite an abundance of publications describing SM change during cancer treatment, meta‐analysis estimates of this change and its impact are uncertain due to heterogeneity in population, disease site, treatment regimen, measurement method and metrics reported. When primary data are still emerging, scoping reviews are a rigorous systematic form of evidence synthesis that can be used to explore the size and extent of existing literature, summarize what is presently known in a general sense, identify under‐represented populations and suggest elements of study design and reporting that will enable future meta‐analysis. ¹¹ , ¹⁵ , ¹⁶ No systematic attempt has been made to synthesize current knowledge on body composition change in the setting of palliative‐intent chemotherapy. A preliminary search confirmed that this literature is heterogeneous with respect to population, time interval of measurement and reporting of metrics, like those cited above. We applied scoping review methodology using the palliative‐intent setting, to illustrate the heterogeneity impeding reliable meta‐analysis of SM and AT change and associated outcomes and to suggest standards to facilitate evidence synthesis. The specific objectives were to (1) demonstrate the methodological variability in measurement and reporting of SM or AT change and their associated outcomes during cancer‐directed treatment and (2) propose a strategy with respect to design, reporting and publication standards for studies measuring body composition change, which will enable evidence synthesis.

A preliminary search of PROSPERO, MEDLINE, the Cochrane Database of Systematic Reviews and JBI Evidence Synthesis was conducted and no current or in‐progress scoping reviews or systematic reviews on the topic were identified.

Methods

This scoping review followed a pre‐defined protocol, registered on 5 April 2022 on Open Science Framework (registration DOI: https://doi.org/10.17605/OSF.IO/MXVTK). It was conducted in accordance with the JBI methodology for scoping reviews. ¹⁵ , ¹⁶

Inclusion/exclusion criteria

Studies from any geographical setting were included if they (1) evaluated adult patients (≥18 years old) during receipt of palliative‐intent chemotherapy for a solid tumour and (2) measured change in SM and/or AT during palliative‐intent chemotherapy, without intervention intended to attenuate SM and AT loss, by analysis of axial CT images at the L3 vertebra.

Exclusions were intended to narrow the scope of the review to demonstrate that even within a defined setting, CT‐based analysis of SM and AT change is not standardized. Studies were excluded if ≥25% of patients in the sample were receiving neoadjuvant therapy, radiotherapy, surgical resection, or exclusive targeted therapy or immunotherapy. Retrospective and prospective observational designs were considered, along with studies describing a standard care control group of a clinical trial.

Search strategy

The search aimed to locate both published and unpublished studies (such as theses) and is reported according to the PRISMA‐ScR extension of the PRISMA (2020) guidelines. ¹⁷ An initial limited search of MEDLINE (1946 to present via Ovid) was undertaken to identify index terms used to describe relevant articles, which were used to develop a full search strategy for MEDLINE (Ovid) (see supporting information); it employed both controlled vocabularies, such as MeSH and EMTREE, and keywords representing key concepts. The search strategy, including all identified keywords and index terms, was adapted for each database, and reference lists of articles selected for full‐text review were used to screen for additional papers. MEDLINE (1946 to present via Ovid), CINAHL Plus with Full Text (EBSCO), Embase (1974 to present via Ovid), Web of Science‐All Databases (Clarivate Analytics), which in itself includes Web of Science Core Collection, BIOSIS Citation Index, BIOSIS Previews, CABI: CAB Abstracts, Derwent Innovations Index, KCI‐Korean Journal Database, Russian Science Citation Index, SciELO Citation Index and Zoological Record, and Cochrane Library (Wiley Online Library) were searched. Cochrane Library (Wiley Version) was also searched independently. Sources of unpublished studies and grey literature included Dissertations and Theses Global (ProQuest) and websites such as beta.asco.org, www.esmo.org and https://society‐scwd.org. The search strategy did not include any limiters. The search was re‐run on 26 April 2022 prior to final analysis.

Study selection and data extraction

Following the search, all identified records were uploaded into Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) and duplicates removed. Titles and abstracts were screened by two independent reviewers (S. S. and P. K.) for assessment against the inclusion criteria. Abstracts that did not reflect the inclusion criteria were not reviewed in full text. Potentially relevant papers were retrieved in full and were assessed against the inclusion/exclusion criteria by two independent reviewers (S. S. and P. K.). Reasons for exclusion of full‐text papers are reported in Figure 1 . Data were extracted by two independent reviewers (S. S. and P. K.) using a data extraction tool developed by the reviewers using Covidence software. The data extraction template was registered with the protocol and revised once during the extraction process to create additional space for reported body composition metrics. Any disagreements that arose between the reviewers during the process were resolved through discussion or by a third reviewer (V. M.).

Figure 1.

PRISMA flow diagram detailing the selection of sources of evidence. CT, computed tomography.

Results of the search strategy

The systematic search identified 2869 publications, containing 372 duplicates. A total of 2496 publications were screened by abstract and title, with 2413 deemed irrelevant based on inclusion and exclusion criteria. Full text was reviewed for 83 publications, with 47 excluded for reasons related to (1) setting (neoadjuvant, adjuvant, curative‐intent or surgical treatment) or (2) outcome measurement (tissue change was not measured or described; single time point only; not during chemotherapy; or not measured using CT). A total of 38 publications were included for data extraction (Figure  1 ). Of these, five were re‐analyses of the Dutch Colorectal Cancer Group CAIRO3 cohort, for which the design and description of longitudinal body composition change was primarily reported by Kurk et al. ¹⁸ The remaining four CAIRO3 analyses ¹⁹ , ²⁰ , ²¹ , ²² were considered secondary analyses of associations with body composition change as previously reported. These were considered collectively as ‘CAIRO3’ to describe cohort characteristics, study design and reporting of body composition change, while each analysis was considered individually for the purpose of describing associations between body composition change and clinical outcomes.

Characteristics of included cohorts

Characteristics of the 34 unique cohorts representing a total of 3933 patients are summarized in Table 1 . The majority of patients included were from Asia (43%), followed by Europe (41%) and North America (16%). Colorectal cancer was the most represented disease site, followed by pancreas cancer and cholangiocarcinoma. Advanced genitourinary and breast cancers represented 2.9% and 3.6% of the total sample, respectively (Figure  2 ). By design, disease sites for which monitoring does not include CT scans and/or the L3 level, such as head and neck cancers and haematological malignancies, were not represented.

Table 1.

Characteristics of 34 cohorts in which body composition change was measured during palliative‐intent chemotherapy

Author, year	Disease site	Region	n	Male (%)	Majority chemotherapy regimen	Multiple regimens (Y/N)
Rier, 2018 23	Breast	Netherlands	98	0	Paclitaxel or 5‐FU + DOX + cyclophosphamide	No
Solomayer, 2019 24	Breast	Germany	29	0	NR	—
Antoun, 2019 25	Colorectal	France	76	50	XELIRI/FOLFIRI ± bevacizumab	No
Best, 2021 26	Colorectal	USA	226	53	FOLFOX ± bevacizumab	Yes
Blauwhoff‐Buskermolen, 2016 27	Colorectal	Netherlands	63	63	CAPOX ± bevacizumab	Yes
CAIRO3 18 , 19 , 20 , 21 , 22	Colorectal	Netherlands	450	63	CAP(OX) ± bevacizumab	No
Dolly, 2020 28	Colorectal	France	72	63	FOLFIRI	Yes
Gallois, 2021 29	Colorectal	France	137	55	FOLFOX or FOLFIRI ± bevacizumab	Yes
Maddalena, 2021 30	Colorectal	Italy	56	59	NR	—
Malik, 2021 31	Colorectal	Poland	78	55	Trifluridine + tipiracil hydrochloride	No
Sasaki, 2019 32	Colorectal	Japan	219	65	FOLFOX ± bevacizumab	Yes
Van der Werf, 2020 33	Colorectal	Netherlands	54	55	CAPOX ± bevacizumab	Yes
De Jong, 2019 34	Lung	Netherlands	116	55	Platinum + paclitaxel + bevacizumab	No
Kakinuma, 2018 35	Lung	Japan	44	71	Multiple	Yes
Lee, 2021 36	Lung	Korea	70	89	Platinum + gemcitabine	Yes
Murphy, 2010 37	Lung	Canada	41	46	Platinum + vinorelbine	Yes
Murphy, 2011 38	Lung	Canada	24	50	Platinum‐based doublet	Yes
Naito, 2017 39	Lung	Japan	30	63	Platinum‐based doublet	Yes
Stene, 2015 40	Lung	Netherlands	35	51	Platinum + gemcitabine	Yes
Birgitte Stene, 2019 41	Lung/pancreas	UK/Norway	46	57	NR	—
Babic, 2019 42	Pancreas	USA	164	58	Gemcitabine‐based or FOLFIRINOX	Yes
Basile, 2019 43	Pancreas	Italy	94	55	Gemcitabine‐based or FOLFIRINOX	Yes
Choi, 2015 44	Pancreas	Korea	484	61	Gemcitabine	Yes
Salinas‐Miranda, 2021 45	Pancreas	Canada	105	61	FOLFIRINOX	Yes
Kays, 2018 46	Pancreas	USA	53	62	FOLFIRINOX	No
Uemura, 2021 47	Pancreas	Japan	69	55	FOLFIRINOX	No
Rollins, 2016 48	Pancreas/cholangio	UK	98	56	Gemcitabine‐based	Yes
Cho, 2017 49	Cholangio	Korea	524	66	Gemcitabine + platinum	Yes
Dijksterhuis, 2019 50	Gastro‐oesophageal	Netherlands	65	75	CAPOX	Yes
Feng, 2020 51	Gastric	China	46	63	Epirubicin + oxaliplatin + fluorouracil	No
Park, 2020 52	Gastric	Korea	111	72	FOLFOX or CAPOX	Yes
Rimini, 2021 53	Gastric	Italy	40	60	FOLFOX	Yes
Fukushima, 2018 54	Genitourinary	Japan	72	74	Gemcitabine + platinum	Yes
Nagai, 2019 55	Genitourinary	Korea	44	NR	Gemcitabine + docetaxel	No

Abbreviations: 5‐FU, 5‐fluorouracil; CAPOX, capecitabine, oxaliplatin; DOX, doxorubicin; FOLFIRI, leucovorin, 5‐fluorouracil, irinotecan; FOLFIRINOX, leucovorin, 5‐fluorouracil, irinotecan, oxaliplatin; FOLFOX, leucovorin, 5‐fluorouracil, oxaliplatin; NR, not reported.

Figure 2.

(A) Disease site and (B) geographical distribution of 3933 patients from 34 cohorts in which body composition change was measured during palliative‐intent chemotherapy. GE, gastroesophageal; GU, genitourinary.

The chemotherapy regimen of majority for each study is shown in Table 1 . Majority regimens were multi‐agent in 30/34 studies, single agent in one study and not reported in three instances. Eight studies (25.8%) were limited to patients on a single regimen, or presented results disaggregated by regimen, while the remainder (74.2%) included patients on more than one regimen and presented aggregate results.

Challenges

Presentation of the aggregate data of patients on multiple therapy regimens limits exploration of regimen‐specific changes in SM and AT during treatment. Palliative chemotherapy regimens vary within disease sites, and thus, even meta‐analysis by disease site would result in heterogeneity due to regimens used, as previously identified. ¹² , ¹³ , ¹⁴ Recent narrative reviews suggest regimen‐specific changes in SM ⁵⁶ , ⁵⁷ , ⁵⁸ ; this can only be confirmed with systematic review and meta‐analysis of studies that report SM and AT change by regimen. If reports indicate that tissue change does not differ by regimen within a disease site, similar regimens could be legitimately aggregated in subsequent work.

Proposed standard

• Report regimen‐specific changes in SM and AT, concurrent with presentation of overall changes.

Study design: Scan timing and intervals

Study designs with respect to the timing and reporting of body composition measurement are graphically summarized in Figure 3 , ordered from most specific to least specific timing of baseline scan. Baseline scan was reported as occurring prior to palliative chemotherapy in most designs (25/34); of these, 12 clearly specified a time frame. The baseline scan was described as ‘at diagnosis’ or ‘first CT’ or ‘staging’ in six designs. ²⁴ , ²⁶ , ³⁶ , ⁴⁶ , ⁴⁸ , ⁵³ In three instances, the baseline scan occurred within a range of days before or after treatment initiation. ³⁸ , ⁴³ , ⁵²

Figure 3.

Variation in study design and reporting of skeletal muscle (SM) and adipose tissue (AT) change during palliative‐intent chemotherapy. Scan interval: reported median or ^amean days between scans. Checkmark: metric was reported. Dash: metric was not reported. %: relative SM change. cm²: change in cross‐sectional area. cm²/m²: change in skeletal muscle index. Cut‐off: the proportion of patients who reached an SM change cut‐off of interest. By sex: sex‐specific reporting. CT, computed tomography scan; d, days; Dx, diagnosis; IMAT, intramuscular AT; Measure. error, measurement or precision error; mo, months; NA, not applicable; SAT, subcutaneous AT; TAT, total AT; VAT, visceral AT.

Endpoint scan timing was similarly variable, defined in seven instances by a single time point (e.g., 6 weeks) after treatment start. ²⁵ , ²⁶ , ²⁹ , ³³ , ⁴⁵ , ⁴⁷ , ⁵⁹ Five studies defined endpoint as a window of time (e.g., 60–120 days) after treatment start. ²⁸ , ³² , ³⁷ , ³⁹ , ⁴² Seven designs defined endpoint CT timing using a set number of chemotherapy cycles, ranging from one to six cycles; however, the length of one cycle was not often reported. ²³ , ³⁴ , ⁴⁰ , ⁵⁰ , ⁵¹ , ⁵⁴ , ⁵⁵ Ten designs specified a treatment or disease milestone as the endpoint, including first reassessment, ³⁰ , ³¹ , ⁴³ , ⁵³ progression ¹⁸ , ⁴⁴ , ⁴⁹ , ⁵² and last CT. ³⁶ , ⁴⁶ Non‐specific descriptors for the endpoint, such as ‘during treatment’ or ‘after treatment’, were applied in five designs. ²⁴ , ²⁷ , ³⁵ , ³⁸ , ⁴⁸ The actual mean or median time between scans was reported for 15/34 cohorts, ranging from 64 to 362 days.

Challenges

The timing of baseline and endpoint measurement provides context for the interpretation of change in SM and AT. Lack of a clearly defined time frame for the pre‐chemotherapy baseline scan means that scans taken long prior to palliative‐intent chemotherapy may be included, and not account for change that may have primarily occurred prior to the current treatment. Similarly, endpoint delineation based on disease or treatment milestones represents the greatest challenge to interpretation and reproducibility of results; this is particularly true when last CT or CT indicating progression is the endpoint, given inter‐individual variation in time to these milestones. Finally, infrequent reporting of actual scan interval impedes interpretation of the presented data, particularly when baseline and endpoint timing is not clearly defined.

Recognizing that any prospective or retrospective review of imaging acquired during standard of care will result in variable CT scan timing, clear definition of the time over which change is measured must be a priority. For example, ‘the CT scan within 45 days prior to palliative chemotherapy initiation was selected as the baseline; if multiple scans were available in this window, the closest to treatment initiation was used,’ and ‘the CT scan within 90–120 days after chemotherapy initiation was selected as the endpoint scan; if multiple scans were available in this window, the closest to 120 days was used.’

Proposed standard

• Study protocols must clearly describe inclusion criteria for baseline and endpoint CT scans in units of time from a common reference point.

Measurement error

In all included studies, measurements of SM and AT were based on the total cross‐sectional areas at L3, rather than specific muscles such as psoas only. The measurement error of CT analysis for SM was reported in eight instances. Five reports referenced measurement error from prior literature, either 2% error ¹⁸ , ²⁸ , ³⁷ , ⁴⁰ or 1.3% error ³⁴ (Figure  3 ). Four reports provided a calculated inter‐observer coefficient of variation for SM measurements, ranging from 0.6% to 2.4%. ²¹ , ²⁷ , ³³ , ⁴² Of these, one also differentiated between intra‐observer variability (difference in repeated measures by the same observer) and inter‐observer variability (difference between observers). ²¹

Challenges

The concept of measurement error of CT analysis has not been acknowledged by most investigators. Among included studies, the precision error of observers on repeated measures was only described in one report. Measurement of precision error enables classification of patients with true tissue loss, stable tissue or true tissue gain according to a least significant change value. Further, considering the least significant change when defining scan interval ensures that the interval is long enough to allow for detection of changes beyond measurement error.

Proposed standard

• The least significant change must be reported, as determined by precision error testing following a published method for repeated measures such as that described by Arribas et al. ⁶⁰

Reporting of skeletal muscle and adipose tissue metrics

At baseline, SM status was described using at least one metric for 33/34 cohorts, using SMI (cm²/m²) in 22/33 instances and SM area (cm²) in 12/33. Total body muscle mass (kg) was estimated using regression equations in three instances. Thirteen publications included sex‐specific reporting of baseline SM. Two cohorts were entirely female; thus, reporting by sex was not applicable. Baseline AT and weight metrics were reported less often than SM. Eleven publications contained at least one baseline AT metric, reported by sex in three instances. Visceral, subcutaneous and total AT areas (cm²) or indices (cm²/m²) were used variably. Intermuscular AT and estimated fat mass (kg) were each reported in one publication. Baseline body mass index (kg/m²) was presented for 22 cohorts, and in seven instances, this was reported categorically. Baseline mean weight (kg) was reported in four instances.

The metrics used to report SM change in each study are visualized in Figure 3 . Six publications did not quantify the change observed, but rather reported the proportion of patients who reached a particular cut‐off of interest for SM loss. Metrics used to describe mean/median SM change included cm²/m² (16/34), % (13/34) and cm² (11/34). SM change was comprehensively described with three metrics in two publications (2/34), two metrics in eight publications (8/34) and one metric in the remainder (18/34). Eight publications normalized SM change to a specific time period, ranging from 30 days to 1 year. Eight publications disaggregated SM change by sex, while the remainder pooled males and females.

AT changes were described for 10 cohorts, with two disaggregated by sex. In one instance, total AT change was described singularly, while the remaining nine specified subcutaneous, visceral and/or intermuscular AT change. Weight change was described for 16/34 cohorts using variable metrics including kg, % or kg/m².

Challenges

Reporting of SM and AT at baseline is necessary to contextualize the sample population and the changes observed. Sex‐specific reporting of baseline SM and AT is uncommon, despite differing central tendencies and distributions of SM area and SMI between males and females. ⁶¹

Reporting of SM and AT change has been limited to one or two metrics, and in some cases, not even described, particularly if the primary outcome is related to a pre‐defined cut‐off of SM or AT loss. AT changes are less commonly reported than SM, representing a gap in the literature. Normalization of observed change (i.e., to a standard number of days) has been applied in data sets with widely variable scan intervals, which assumes that the rate of body composition change is constant, even in the last days of life, or that it can be extrapolated from short scan intervals. This method introduces uncertainty and estimation to the reported data.

Finally, reporting of sex‐specific changes in SM and AT is rare. Muscle mass and biological characteristics of muscle are known to be different between males and females. ⁶¹ The distribution, function and behaviour of AT are also divergent between males and females. ⁶² , ⁶³ , ⁶⁴ Whether change in each of these tissues over time is uniform or whether it differs between males and females remains to be characterized and will only be determined if outcomes are reported by sex, which is rarely done. At present, this remains an unrecognized potential source of variation in studies evaluating longitudinal changes in SM and AT.

Proposed standards

• Report baseline SM and AT in cm² and cm²/m², by sex.

• Clearly report actual time between scans (mean/median and range of days) for each analysis to allow the reader to interpret the observed changes in the context of time. Normalization of change over a standard time period is not an alternative to clearly defining baseline and endpoint scan inclusion criteria.

• Report changes in SM and AT by sex using multiple metrics, including both absolute and relative changes, using supplementary materials if required. Consider including a waterfall plot to visualize the distribution and central tendency of change for each tissue. ⁴⁰ , ⁶⁵

Application of skeletal muscle and adipose change to clinical outcomes

Survival was the most frequent outcome investigated in relation to SM change (n = 24), with 22 publications including clearly presented Cox proportional hazards models using SM and/or AT change as an independent variable. A summary of the models used for survival analyses is presented in Table 2 . In most instances, change was measured within the first 100 days of palliative treatment.

Table 2.

Summary of Cox proportional hazards models evaluating the independent association between skeletal muscle change during palliative chemotherapy and overall survival

Reference	Scan interval	Classification of SM change for model	Rationale for classification	n	Model covariates
23	6 cycles	SM loss > 0 cm2	Unspecified	98	Age, ER/PR positive; number of metastases; stage at diagnosis, regimen
25	64 days	‘SM score’, not clearly described	Unspecified	57	Age, sex, metastases, regimen, BMI
26	89 days	SM loss > 5%	Miyamoto et al. (2015)	193	Age, sex, tumour mutational status, weight loss, total AT loss
27	78 days	SM loss > 9%	Tertiles	67	Sex, age, LDH, comorbidity, metastases, treatment line, response
20	To progression	Continuous SMI change (per standard deviation)	NA	450	Age, sex, PS, stage, primary site, resection, initial disease response, LDH, metastases, dose reduction, scan interval
29	60 days	SM loss > 14%	Log‐rank maximization	149	Hypoalbuminemia, nutrition risk score, response
30	To first reassessment	SM loss > 5%	Miyamoto et al. (2015)	56	Model not presented
31	105 days	SM loss ≥ 5%	Unspecified	78	Histological differentiation, CEA
32	60–120 days	SM loss > 9%	Blauwhoff‐Buskermolen et al. (2016)	142	Age, sex, BMI, PS, prior resection
36	First to last CT	Top tertile rate of SM loss, cm2/30 days	Tertiles	70	Age, stage, PS, disease response
40	88 days	SM loss > 2%	Mourtzakis et al. (2008)	35	Sex, PS, stage, response, quality of life and appetite loss at baseline, BMI, regimen
42	80 days	(a) Top quartile SM loss (b) Top quartile AT loss	Sex‐specific quartiles	164	Age, study site, race, baseline SM/AT, sex, year, stage, BMI, diabetes, smoking, regimen
43	To first reassessment	SM loss ≥ 10%	Sugiyama et al. (2017)	94	Tumour stage, visceral AT, PS change
44	To progression	SM loss > 2 cm2/m2	Unspecified	484	Age, sex, PS, disease extent, BMI, sarcopenia, BMI change, best response
45	77 days	Continuous SMI change (per cm 2 /m 2 /30 days)	Log‐rank maximization	105	Disease response
46	First to last CT	SM loss > 5% plus AT loss > 5%	Unspecified	53	Age, sex, disease extent, response, sarcopenia, obesity, sarcopenic obesity, myosteatosis, tumour location
47	71 days	(a) SM loss ≥ 7.9% (b) AT loss ≥ 5.4%	Median	69	Age, sex, metastases, jaundice, obstruction, diabetes, tumour size/location, CA19‐9, CEA, BMI, albumin, UGT1A1 heterozygous, response, AT index, AT change, SMI, SMI change, sarcopenia
49	To progression	SM loss > 7%	Unspecified	524	Age, sex, primary site, PS, regimen, stage, SMI, BMI, change in BMI, response
50	79 days	Continuous SMI change (per cm 2 /m 2 )	NA	65	Age, sex, PS, metastatic sites
51	2 cycles	SM loss > 8% and/or VAT loss > 20%	Quartiles	46	Model not presented
52	To progression	SM loss > 6.5 cm2/m2	Tertiles	111	Sarcopenia at baseline (Korean specific), PS, overall response rate
53	To first reassessment	SM loss > 10%	Unspecified	40	PS
54	2 cycles	Continuous SMI change (per cm 2 /m 2 )	NA	72	Disease sub‐type, C‐reactive protein
55	First to last CT	Rate of SM loss > 1%/30 days	Unspecified	44	Age, sex, PS change, comorbidities, response, HGB, weight change, metastatic site, time

Abbreviations: AT, adipose tissue; BMI, body mass index; CA19‐9, cancer antigen 19‐9; CEA, carcinoembryonic antigen; CT, computed tomography; ER/PR, oestrogen receptor/progesterone receptor; HGB, haemoglobin; LDH, lactate dehydrogenase; NA, not applicable; PS, performance status; SM, skeletal muscle; SMI, SM index; VAT, visceral AT.

Continuous SMI change was used as a predictor of survival in three studies, while most specified a cut‐off for SM loss to create a dichotomous variable using % (n = 14), cm² (n = 3), cm²/m² (n = 2) or unspecified (n = 1). Commonly applied cut‐offs were 5% SM loss (n = 4), 9% SM loss (n = 2) and 10% SM loss (n = 2). Cut‐offs were applied with no stated rationale in eight instances or selected based on the sample data in six instances (i.e., median, tertiles or quartiles). Five authors referenced prior literature when applying cut‐offs, and two defined cut‐offs based on the log‐rank maximization method.

Among 22 presented Cox proportional hazards models, covariates applied included age, sex, disease response, disease stage/status (e.g., locally advanced and metastatic), metastatic spread (e.g., location and number), baseline body mass index or weight, biological values (e.g., albumin), treatment type, tumour‐specific factors (e.g., mutational status, size and primary location), comorbidities, baseline SM, baseline AT and time. The prevalence of use for each of these covariates is visualized in Figure 4 . Three models included co‐occurring AT change as a covariate. ²⁶ , ⁴⁶ , ⁴⁷ No sample size assumptions or calculations were presented for multivariable survival analyses; however, several authors noted inadequate sample size as a limitation.

Figure 4.

Prevalence of covariate inclusion among 22 Cox proportional hazards survival models evaluating the association between skeletal muscle or adipose change and survival in patients with advanced cancer. BMI, body mass index.

The relationship between SM change and chemotherapy toxicity was explored in eight studies. Of these, five publications compared the incidence of treatment toxicities between ‘SM losers’ and ‘SM non‐losers’ (i.e., univariable), ²⁵ , ²⁷ , ³¹ , ³² , ⁴⁰ and three included SM loss in multivariable logistic regression to predict odds of toxicity. ²² , ²⁹ , ⁵⁰ The relationship between SM loss and changes in health‐related quality of life or physical function was explored in four studies. ¹⁹ , ³⁹ , ⁴⁰ , ⁵⁹

Challenges

Multiple definitions of SM loss have been evaluated for association with survival, ranging from any loss to 14% loss, over variable periods of time. Rationale for the selection of these cut‐offs is rarely provided. Given the wide range of cut‐offs used to categorize SM loss published in the literature for survival prognostication, studies defining a new cut‐off for categorization of the independent variable in survival analysis should be well powered and clearly indicate where the selected cut‐off falls within the distribution of observed change. The use of a continuous independent variable (SM change and/or AT change) in a survival model should be done with consideration of precision error. For example, if measured least significant change is 2.0 cm², a model using continuous SM loss per cm ² may be unreliable. The ideal interval for evaluation of SM or AT change to inform prognostication will vary based on the expected survival of the cohort; however, prognostication based on early change is the most feasible due to attrition.

Survival analyses are often underpowered and presented as exploratory, thus are at high risk of overfitting. ⁶⁶ The prognostic impact of CT‐defined SM loss may be related to concurrent disease progression, ⁴⁰ , ⁶⁷ yet several survival models did not account for disease response. Co‐occurring total adipose loss is rarely considered as a covariate, even though AT loss and SM loss together represent the main components of total tissue loss, or a comprehensive view of habitus change over time.

In studies evaluating the relationship between SM loss and toxicity, inconsistent definitions of treatment toxicity have been used. Further, the time of SM change measurement often occurred during the period in which toxicity was evaluated, making it impossible to ascertain exposure versus outcome. Finally, the relationship between SM loss and patient‐reported outcomes is a clear gap in the literature that will require prospective studies. As data availability in small disease sites may preclude adequate power, standardized design and reporting, data repository deposit, and/or multi‐centre collaboration will strengthen meta‐analysis of prognostic models.

Proposed standards

• Measure change over a consistent time period for all patients in the sample.

• Provide clear rationale if using a single tissue change cut‐off for prognostication, and ensure the cut‐off is greater than the least significant change.

• Include known covariates (age and sex) and account for concurrent changes (disease response and total adipose change) in survival models.

• Clearly differentiate between the period in which tissue loss occurs and the period in which the risk of toxicity is evaluated when attempting to define a causal relationship between SM change and treatment toxicity. For example, ‘this analysis evaluated the association between SM loss > 5% in the first 3 months of chemotherapy and the risk of toxicity in the subsequent 2 months of chemotherapy.’

Conclusions

Since Mourtzakis et al. presented CT image analysis as a ‘practical and precise approach to quantification of body composition measurement in cancer’, ² this method has been applied worldwide to measure body composition status and change during cancer treatment. CT‐defined SM and AT change have been measured across multiple cancer sites and treatment plans, limited only when abdominal CT scans are not routinely used for monitoring. A variety of landmarks have emerged and been validated for measurement in regions other than L3, and the same challenges apply. ⁶⁰ Despite an abundance of data, reliable meta‐analysis describing change in body composition during palliative chemotherapy, and its related outcomes, remains challenging. Using systematic scoping review methodology, literature from a single cancer treatment setting was used to demonstrate barriers to meta‐analysis and to propose a minimum standard for future reports. Our results demonstrate wide variability within and between studies related to treatment protocols, scan intervals (time over which change is measured), change metrics reported and the treatment of SM and AT change in prognostic models.

The setting of palliative‐intent chemotherapy was used as a sample to narrow the scope of literature, which may reduce the generalizability of our findings to other settings such as curative‐intent cancer treatment or non‐cancer settings. Measurement of SM and AT from CT scans taken as part of standard oncological care naturally leads to inconsistent measurement timing, as clinicians rather than researchers select the timing of CT evaluations. This issue is particularly accentuated in palliative settings, where patients who decline rapidly may have early re‐evaluation of CT scans due to worsening status, while others are re‐evaluated according to a standard schedule. Defining and reporting the boundaries of inclusion for baseline and endpoint scans is increasingly important in groups such as this, where scan intervals are variable and influenced by the clinical imperatives for repeat scanning.

Other challenges identified by this review are not specific to the use of routinely acquired CT scans for SM and AT measurement. While prospectively planned studies or those using dual‐energy X‐ray absorptiometry (DEXA), bioelectrical impedance analysis (BIA) or another technique with the sole purpose of longitudinal body composition measurement will have greater control over measurement timing, it remains imperative for authors to report measurement error, baseline/endpoint timing and the actual interval between measurements.

Regardless of the setting, key principles for study design, measurement, reporting and statistical analysis should be applied to future reports (Figure  5 ). Consensus discussions would further enhance these recommendations to produce publication standards, enabling accurate meta‐analysis of body composition change and associated clinical outcomes. Meta‐analysis of carefully designed and clearly reported measurements of SM and AT change will move this body of research towards meaningful application of findings to clinical care.

Figure 5.

Principles for measurement and reporting of computed tomography (CT)‐defined skeletal muscle (SM) and adipose tissue (AT) change and associated outcomes in patients receiving cancer‐directed treatment.

Conflict of interest statement

The authors declare no conflicts of interest.

Supporting information

Data S1. Search strategy

Klassen PN, Mazurak VC, Thorlakson J, Servais S (2023) Call for standardization in assessment and reporting of muscle and adipose change using computed tomography analysis in oncology: A scoping review, Journal of Cachexia, Sarcopenia and Muscle, 14, 1918–1931, 10.1002/jcsm.13318