Body Composition and Dose-limiting Toxicity in Colorectal Cancer Chemotherapy Treatment; a Systematic Review of the Literature. Could Muscle Mass be the New Body Surface Area in Chemotherapy Dosing?

Validation of an automated segmentation method for body composition analysis in colorectal cancer patients using diagnostic abdominal computed tomography images

BACKGROUND & AIMS

Several automated programs have been developed to facilitate body composition analysis of images from abdominal computed tomography (CT) scans. External validation in patients with colorectal cancer is necessary for use in research and clinical practice. Our aim was to validate an automatic method (AutoMATiCA) of segmenting CT images at the third lumbar level (L3) from patients with colorectal cancer, by comparing with manual segmentation.

METHODS

Diagnostic abdominal CT scans of consecutive patients with stage I-III colorectal cancer were analysed to measure cross-sectional areas and tissue densities of skeletal muscle and intra-muscular, visceral, and subcutaneous adipose tissue. Trained analysts performed manual segmentation of L3 CT images using SliceOmatic. Automatic segmentation was performed using AutoMATiCA, an open-source software. The Dice similarity coefficient (DSC) was calculated to assess segmentation accuracy. Agreement of automatic with manual segmentation was evaluated using intra-class correlation coefficients (ICCs) and Bland-Altman plots with limits of agreement.

RESULTS

A total of 292 scans were included, of which 62% were from male patients. The agreement of AutoMATiCA with the manual segmentation was excellent, with median DSC values ranging from 0.900 to 0.991 and ICCs above 0.95 for all segmented areas. No systematic deviations were observed in Bland-Altman plots for all segmented areas, with overall narrow limits of agreement.

CONCLUSIONS

AutoMATiCA provides an accurate segmentation of abdominal CT images from patients with colorectal cancer. Our findings support its use as a highly efficient automated tool for body composition analysis in research and potentially also in clinical practice.

Chemotoxicity and Associated Risk Factors in Colorectal Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

Colorectal cancer (CRC) patients experience multiple types of chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking.

OBJECTIVES

We conducted a systematic review and meta-analysis to determine chemotoxicity's prevalence and risk factors in CRC.

METHODS

A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis.

RESULTS

Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (β: -1.44) and GI toxicity (β:1.85) (p-values < 0.01). Older adults (>65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (p-values < 0.05).

CONCLUSIONS

Our findings highlight the importance of closely monitoring and managing chemotoxicity in CRC patients receiving chemotherapy.

CT-derived body composition and differential association with age, TNM stage and systemic inflammation in patients with colon cancer

Low skeletal muscle index/density (SMI/SMD) is prevalent in cancer, adversely prognostic and associated with tumour stage and the systemic inflammatory response (SIR). Age and SMI/SMD has not been widely studied. The present study analyses the association between age and SMI/SMD after adjustment for other clinicopathological factors. Patients undergoing resectional surgery for TNM Stage I-III disease within the West of Scotland between 2011 and 2014 were identified. A single CT slice was obtained from each patients staging CT scan. SMI and SMD were stratified normal/abnormal. The SIR was stratified using Systemic Inflammatory Grade (SIG). When stratified by age (< 50/50s/60s/70s/80+), 39%/38%/48%/62%/74% and 27%/48%/64%/82%/92% of patients had a low SMI and SMD respectively (both p < 0.001). Older age (OR 1.47, p < 0.001), female sex (OR 1.32, p = 0.032), lower socioeconomic deprivation (OR 1.15, p = 0.004), higher ASA (OR 1.30, p = 0.019), emergency presentation (OR 1.82, p = 0.003), lower BMI (OR 0.67, p < 0.002) and higher SIG (OR 1.23, p < 0.001) were independently associated with low SMI. Older age (OR 2.28, p < 0.001), female sex (OR 1.38, p = 0.038), higher ASA (OR 1.92, p < 0.001), emergency presentation (OR 1.71, p = 0.023), and higher SIG (OR 1.37, p < 0.001) were independently associated with lower SMD. Tumour factors were not independently associated with either SMI/SMD. Age was a major factor associated with low SMI/SMD in patients with colon cancer. Therefore, in these patients it is likely that this represents largely constitutional body composition as opposed to being a disease mediated effect. Adjustment for age is required when considering the cancer mediated effect on SMI/SMD in patients with colon cancer.

Serum tumor marker and CT body composition scoring system predicts outcomes in colorectal cancer surgical patients

OBJECTIVE

To investigate the prognostic value of preoperative body composition and serum tumor markers (STM) in patients undergoing surgical treatment for colorectal cancer (CRC) and to establish the prognostic score for patients with CRC.

METHODS

This study enrolled 365 patients (training set 245, validation set 120) with CRC who underwent surgical resection. The predictive value of various body composition features and STM for determining CRC prognosis were compared. A novel index score based on the independent risk factors from Cox regression for CRC patients was established and evaluated for its usefulness.

RESULTS

Multivariate Cox regression showed that low skeletal muscle radiodensity (SMD) (p = 0.020), low subcutaneous fat area (SFA) (p = 0.029), high carcinoembryonic antigen (CEA) (p = 0.008), and high alpha-fetoprotein (AFP) (p = 0.039) were all independent prognostic factors for poor overall survival (OS). The multifactorial analysis indicated that high intermuscular fat area (IMFA) (p = 0.033) and high CEA (p = 0.009) were independent prognostic factors for poor disease-free survival (DFS). Based on these findings, two scoring systems for OS and DFS were established in the training datasets. CRC patients who scored higher on the new scoring systems had lower OS and DFS (both p < 0.001) as shown in the Kaplan-Meier survival curves in the training and validation datasets.

CONCLUSION

In predicting the prognosis of CRC patients, SFA and SMD are superior to other body composition measurements. A scoring system based on body composition and STM can have prognostic value and clinical applicability.

CLINICAL RELEVANCE STATEMENT

This scoring system, combining body composition and serum tumor markers, may help predict postoperative survival of CRC patients and help clinicians make well-informed decisions regarding the treatment of patients.

KEY POINTS

Colorectal cancer prognosis can be related to body composition. High intermuscular fat area and CEA were independent prognostic factors for poor disease-free survival. This scoring system, based on body composition and tumor markers, can prognosticate for colorectal cancer patients.

Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients

INTRODUCTION

This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients.

METHODS

A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012-2019 at a single Australian tertiary institution, Western Health in Melbourne. A two-dimensional U-Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross-sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods.

RESULTS

The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients >0.98, respectively) and test (Dice similarity coefficients >0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944-0.999, P < 0.001).

CONCLUSIONS

Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.

The Role of Exercise in Cancer-Related Sarcopenia and Sarcopenic Obesity

One of the most common adverse effects of cancer and its therapeutic strategies is sarcopenia, a condition which is characterised by excess muscle wasting and muscle strength loss due to the disrupted muscle homeostasis. Moreover, cancer-related sarcopenia may be combined with the increased deposition of fat mass, a syndrome called cancer-associated sarcopenic obesity. Both clinical conditions have significant clinical importance and can predict disease progression and survival. A growing body of evidence supports the claim that physical exercise is a safe and effective complementary therapy for oncology patients which can limit the cancer- and its treatment-related muscle catabolism and promote the maintenance of muscle mass. Moreover, even after the onset of sarcopenia, exercise interventions can counterbalance the muscle mass loss and improve the clinical appearance and quality of life of cancer patients. The aim of this narrative review was to describe the various pathophysiological mechanisms, such as protein synthesis, mitochondrial function, inflammatory response, and the hypothalamic-pituitary-adrenal axis, which are regulated by exercise and contribute to the management of sarcopenia and sarcopenic obesity. Moreover, myokines, factors produced by and released from exercising muscles, are being discussed as they appear to play an important role in mediating the beneficial effects of exercise against sarcopenia.

Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

PURPOSE

Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients.

METHODS

203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance.

RESULTS

A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10-3). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50-0.54; males: AUC = 0.50-0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males).

CONCLUSION

Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent.

Identification of Differences in Body Composition Measures Using 3D-Derived Artificial Intelligence from Multiple CT Scans across the L3 Vertebra Compared to a Single Mid-Point L3 CT Scan

Purpose

Body composition analysis in colorectal cancer (CRC) typically utilises a single 2D-abdominal axial CT slice taken at the mid-L3 level. The use of artificial intelligence (AI) allows for analysis of the entire L3 vertebra (non-mid-L3 and mid-L3). The goal of this study was to determine if the use of an AI approach offered any additional information on capturing body composition measures.

Methods

A total of 2203 axial CT slices of the entire L3 level (4-46 slices were available per patient) were retrospectively collected from 203 CRC patients treated at Western Health, Melbourne (97 males; 47.8%). A pretrained artificial intelligence (AI) model was used to segment muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on these slices. The difference in body composition measures between mid-L3 and non-mid-L3 scans was compared for each patient, and for males and females separately.

Results

Body composition measures derived from non-mid-L3 scans exhibited a median range of 0.85% to 6.28% (average percent difference) when compared to the use of a single mid-L3 scan. Significant variation in the VAT surface area (p = 0.02) was observed in females compared to males, whereas male patients exhibited a greater variation in SAT surface area (p < 0.001) and radiodensity (p = 0.007).

Conclusion

Significant differences in various body composition measures were observed when comparing non-mid-L3 slices to only the mid-L3 slice. Researchers should be aware that considering only the use of a single midpoint L3 CT scan slice will impact the estimate of body composition measurements.

Change in Growth Status and Obesity Rates among Saudi Children and Adolescents Is Partially Attributed to Discrepancies in Definitions Used: A Review of Anthropometric Measurements

Anthropometric measurements are the first step in determining the health status in children and adolescents. Clinicians require standardized protocols for proper assessment and interpretation. Therefore, this study aims to review the literature of international and Saudi national guidelines and studies previously conducted in Saudi children and adolescents to provide recommendations to establish Saudi guidelines in line with the Saudi 2030 Vision. Systematic search was conducted in several databases: Medline, PubMed, Saudi Digital Library and Google Scholar from January 1990 to January 2021. Further, 167 studies measured anthropometrics in Saudi children/adolescents; 33 of these studies contributed to the establishment/adjustment of Saudi growth charts or specific cutoffs or studied the trend of growth in representative samples or adjusted the international curves to be used in Saudis. This review warrants updating growth charts and establishing the standard cutoffs of Saudi adolescent anthropometrics to avoid over/underreporting. This review provides insights and recommendations regarding the resources that can be used to establish national guidelines in anthropometric measurements for Saudi children/adolescents. This review will help policymakers and the Ministry of Health to establish standardized protocols to be used in Saudi Arabia for anthropometric measurements that may assist in detecting malnutrition.

Nutrition care is an integral part of patient-centred medical care: a European consensus

While healthcare is becoming more patient-centred, evidence-based nutrition interventions are still not accessible to all patients with cancer. As nutrition interventions directly improve clinical and socioeconomic outcomes, patient-centred care is not complete without nutrition care. While awareness of the negative impact of malnutrition on clinical outcomes, quality of life, and functional and emotional wellbeing in cancer is growing, there is relatively poor awareness amongst patients, clinicians, policymakers, and payers that nutrition interventions -particularly those begun in the early stages of the disease course- are an effective method for improving such outcomes. The European Beating Cancer Plan recognises the need for a holistic approach to cancer but lacks actionable recommendations to implement integrated nutrition cancer care at member state level. When considering nutrition care as a human right, the impact on quality of life and functional status must be prioritized, as these may be equally as important to patients, especially in advanced cancer where improvements in clinical outcomes such as survival or tumour burden may not be attainable. We formulate actions needed at the regional and the European level to ensure integrated nutrition care for all patients with cancer. The 4 main Take Home Messages are as follows: 1. The goals of Europe's Beating Cancer Plan cannot be achieved without integrating nutrition across the cancer care continuum. 2. Malnutrition negatively impacts clinical outcomes and has socioeconomic consequences for patients and healthcare systems. 3. Championing integrating nutrition care into cancer care is therefore the duty and ethical responsibility of clinicians (Hippocratic Oath-primum non nocere) and 4. Nutrition care is a cost effective, evidence-based therapy.

Body composition from single versus multi-slice abdominal computed tomography: Concordance and associations with colorectal cancer survival

BACKGROUND

Computed tomography (CT) scans are routinely obtained in oncology and provide measures of muscle and adipose tissue predictive of morbidity and mortality. Automated segmentation of CT has advanced past single slices to multi-slice measurements, but the concordance of these approaches and their associations with mortality after cancer diagnosis have not been compared.

METHODS

A total of 2871 patients with colorectal cancer diagnosed during 2012-2017 at Kaiser Permanente Northern California underwent abdominal CT scans as part of routine clinical care from which mid-L3 cross-sectional areas and multi-slice T12-L5 volumes of skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT) tissues were assessed using Data Analysis Facilitation Suite, an automated multi-slice segmentation platform. To facilitate comparison between single-slice and multi-slice measurements, sex-specific z-scores were calculated. Pearson correlation coefficients and Bland-Altman analysis were used to quantify agreement. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for death adjusting for age, sex, race/ethnicity, height, and tumour site and stage.

RESULTS

Single-slice area and multi-slice abdominal volumes were highly correlated for all tissues (SKM R = 0.92, P < 0.001; SAT R = 0.97, P < 0.001; VAT R = 0.98, P < 0.001; IMAT R = 0.89, P < 0.001). Bland-Altman plots had a bias of 0 (SE: 0.00), indicating high average agreement between measures. The limits of agreement were narrowest for VAT ( ±  0.42 SD) and SAT ( ±  0.44 SD), and widest for SKM ( ±  0.78 SD) and IMAT ( ±  0.92 SD). The HRs had overlapping CIs, and similar magnitudes and direction of effects; for example, a 1-SD increase in SKM area was associated with an 18% decreased risk of death (HR = 0.82; 95% CI: 0.72-0.92), versus 15% for volume from T12 to L5 (HR = 0.85; 95% CI: 0.75-0.96).

CONCLUSIONS

Single-slice L3 areas and multi-slice T12-L5 abdominal volumes of SKM, VAT, SAT and IMAT are highly correlated. Associations between area and volume measures with all-cause mortality were similar, suggesting that they are equivalent tools for population studies if body composition is assessed at a single timepoint. Future research should examine longitudinal changes in multi-slice tissues to improve individual risk prediction.

Body composition is associated with tacrolimus pharmacokinetics in kidney transplant recipients

Purpose

A population pharmacokinetic (popPK) model may be used to improve tacrolimus dosing and minimize under- and overexposure in kidney transplant recipients. It is unknown how body composition parameters relate to tacrolimus pharmacokinetics and which parameter correlates best with tacrolimus exposure. The aims of this study were to investigate which body composition parameter has the best association with the pharmacokinetics of tacrolimus and to describe this relationship in a popPK model.

Methods

Body composition was assessed using bio-impedance spectroscopy (BIS). Pharmacokinetic analysis was performed using nonlinear mixed effects modeling (NONMEM). Lean tissue mass, adipose tissue mass, over-hydration, and phase angle were measured with BIS and then evaluated as covariates. The final popPK model was evaluated using goodness-of-fit plots, visual predictive checks, and a bootstrap analysis.

Results

In 46 kidney transplant recipients, 284 tacrolimus concentrations were measured. The base model without body composition parameters included age, plasma albumin, plasma creatinine, CYP3A4 and CYP3A5 genotypes, and hematocrit as covariates. After full forward inclusion and backward elimination, only the effect of the phase angle on clearance (dOFV =  − 13.406; p < 0.01) was included in the final model. Phase angle was positively correlated with tacrolimus clearance. The inter-individual variability decreased from 41.7% in the base model to 34.2% in the final model. The model was successfully validated.

Conclusion

The phase angle is the bio-impedance spectroscopic parameter that correlates best with tacrolimus pharmacokinetics. Incorporation of the phase angle in a popPK model can improve the prediction of an individual’s tacrolimus dose requirement after transplantation.

Supplementary information

The online version contains supplementary material available at 10.1007/s00228-022-03323-0.