Impact of subcutaneous and visceral fat adiposity in patients with colorectal cancer

Post-diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis

The adiposity influence on colorectal cancer prognosis remains poorly characterised. We performed a systematic review and meta-analysis on post-diagnosis adiposity measures (body mass index [BMI], waist circumference, waist-to-hip ratio, weight) or their changes and colorectal cancer outcomes. PubMed and Embase were searched through 28 February 2022. Random-effects meta-analyses were conducted when at least three studies had sufficient information. The quality of evidence was interpreted and graded by the Global Cancer Update Programme (CUP Global) independent Expert Committee on Cancer Survivorship and Expert Panel. We reviewed 124 observational studies (85 publications). Meta-analyses were possible for BMI and all-cause mortality, colorectal cancer-specific mortality, and cancer recurrence/disease-free survival. Non-linear meta-analysis indicated a reverse J-shaped association between BMI and colorectal cancer outcomes (nadir at BMI 28 kg/m2). The highest risk, relative to the nadir, was observed at both ends of the BMI distribution (18 and 38 kg/m2), namely 60% and 23% higher risk for all-cause mortality; 95% and 26% for colorectal cancer-specific mortality; and 37% and 24% for cancer recurrence/disease-free survival, respectively. The higher risk with low BMI was attenuated in secondary analyses of RCTs (compared to cohort studies), among studies with longer follow-up, and in women suggesting potential methodological limitations and/or altered physiological state. Descriptively synthesised studies on other adiposity-outcome associations of interest were limited in number and methodological quality. All the associations were graded as limited (likelihood of causality: no conclusion) due to potential methodological limitations (reverse causation, confounding, selection bias). Additional well-designed observational studies and interventional trials are needed to provide further clarification.

Sarcopenia in colorectal cancer is related to socio-economic deprivation and Body Mass Index alone misrepresents underlying muscle loss in the deprived

BACKGROUND & AIMS

Patients with colorectal cancer who are more socio-economically deprived have worse outcomes; deprivation is also associated with higher obesity rates, defined as a body mass index (BMI) of greater than thirty. Body composition (BC) factors such as sarcopenia and myosteatosis are also known to predispose to poorer outcomes following colorectal cancer surgery. There is limited evidence to date to relate the effect of deprivation upon these host characteristics that are linked to prognosis. We aimed to examine the relationship between deprivation and body composition in colorectal cancer.

METHODS

Analysis was performed on a prospectively collected database of preoperative primary colorectal cancer patients at St Mark's - The National Bowel Hospital, UK. Body composition characteristics were identified by analysing the L3 axial slices of Computer Tomogram (CT) slices of preoperative staging using Slice-O-Matic software with Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) L3 plug-in. Deprivation status for each patient was determined using their postal code which was linked to the Index of Multiple Deprivation (IMD). Each domain of the IMD was examined individually in relation to BC characteristics. Binary logistic regression analysis was performed on the data using a model developed from previous published analyses of this dataset.

RESULTS

Four hundred and nineteen patients were included in the final analysis, the median age was 69 years and 57% of the patient population was male. Patients who were more deprived were significantly more likely to be sarcopenic [OR 1.56 (95% CI 1.01-2.41, p = 0.045)] and myosteatotic [OR 1.69 (95% CI 1.019-2.81, p = 0.042)]. More deprived patients were also more likely to have a lower BMI [OR 0.60 (95% CI 0.38-0.94, p = 0.026)] despite no significant difference in visceral obesity between the most and least deprived.

CONCLUSIONS

Deprivation is an important independent determinant of sarcopenia in the colorectal cancer population. Identifying these patients early and addressing reversible factors may help improve post-operative surgical outcomes in this poor prognostic group. Sarcopenia may be a premorbid state in the deprived colorectal cancer patient that may not be wholly driven by tumour characteristics.

CT-based muscle and adipose measurements predict prognosis in patients with digestive system malignancy

The role of skeletal muscle and adipose tissue in the progression of cancer has been gradually discussed, but it needs further exploration. The objective of this study was to provide an in-depth analysis of skeletal muscle and fat in digestive malignancies and to construct novel predictors for clinical management. This is a retrospective study that includes data from Cancer Center, the First Hospital of Jilin University. Basic characteristic information was analyzed by T tests. Correlation matrices were drawn to explore the relationship between CT-related indicators and other indicators. Cox risk regression analyses were performed to analyze the association between the overall survivals (OS) and various types of indicators. A new indicator body composition score (BCS) was then created and a time-dependent receiver operating characteristic curve was plotted to analyze the efficacy of the BCS. Finally, a nomogram was produced to develop a scored-CT system based on BCS and other indicators. C-index and calibration curve analyses were performed to validate the predictive accuracy of the scored-CT system. A total of 575 participants were enrolled in the study. Cox risk regression model revealed that VFD, L3 SMI and VFA/SFA were associated with prognosis of cancer patients. After adjustment, BCS index based on CT was significantly associated with prognosis, both in all study population and in subgroup analysis according to tumor types (all study population: HR 2.036, P < 0.001; colorectal cancer: HR 2.693, P < 0.001; hepatocellular carcinoma: HR 4.863, P < 0.001; esophageal cancer: HR 4.431, P = 0.008; pancreatic cancer: HR 1.905, P = 0.016; biliary system malignancies: HR 23.829, P = 0.035). The scored-CT system was constructed according to tumor type, stage, KPS, PG-SGA and BCS index, and it was of great predictive validity. This study identified VFD, L3 SMI and VFA/SFA associated with digestive malignancies outcomes. BCS was created and the scored-CT system was established to predict the OS of cancer patients.

Visceral fat area is more strongly associated with arterial stiffness than abdominal subcutaneous fat area in Chinese patients with type 2 diabetes

BACKGROUND

Few studies have compared the correlation between visceral fat area (VFA) and abdominal subcutaneous fat area (SFA) with arterial stiffness (AS) in patients with type 2 diabetes (T2D). In addition, there is currently controversy regarding the correlation between VFA and SFA with AS. We aimed to investigate the relationship between VFA and SFA with AS in patients with T2D.

METHODS

In this cross-sectional study, 1475 Chinese T2D patients with an average age of 52.32 ± 10.96 years were included. VFA and SFA were determined by a dual bioelectrical impedance analyzer, and AS was determined by measurement of brachial-ankle pulse wave conduction velocity (baPWV). Atherosclerosis was deemed present in study participants with baPWV values higher than 75th percentile (1781 cm/s). Independent correlations of logVFA and logSFA with AS were assessed using multiple linear regression and multivariate logistic regression.

RESULTS

The baPWV was linked with VFA, waist circumference, and women's SFA in a general linear correlation study (P < 0.05), but not with body mass index (P = 0.3783) or men's SFA (P = 0.1899). In both men and women, VFA and SFA were positively correlated with AS, according to the generalized additive model (GAM). After fully adjusting for confounders, multiple linear regression analyses showed that for every 1-unit increase in logVFA, the beta coefficient of baPWV increased by 63.1 cm/s (95% CI: 18.4, 107.8) (P < 0.05). logSFA did not correlate significantly with baPWV (P = 0.125). In the multiple logistic regression analysis, the odds ratio (OR) of elevated baPWV was 1.8 (95% CI: 1.1, 3.1) (P = 0.019) per 1-unit increase in logVFA. logSFA did not correlate significantly with AS (P = 0.091). In the subgroup analysis, the correlation between logVFA and baPWV did not interact across subgroups (P-interaction > 0.05).

CONCLUSIONS

Compared with SFA, VFA had a stronger independent positive correlation with AS in Chinese T2D patients. Patients with T2D should pay more attention to monitoring VFA and lowering it to minimize cardiovascular events.

Anthropometrics and cancer prognosis: a multicenter cohort study

BACKGROUND

Anthropometric indicators have been shown to be associated with the prognosis of patients with cancer. However, any single anthropometric index has limitation in predicting the prognosis.

OBJECTIVES

This study aimed to observe the predictive role of 7 anthropometric indicators based on body size on the prognosis of patients with cancer.

METHODS

A principal component analysis (PCA) on 7 anthropometric measurements: height, weight, BMI, hand grip strength (HGS), triceps skinfold thickness (TSF), mid-upper arm circumference (MAC), and calf circumference (CAC) was conducted. Principal components (PCs) were derived from this analysis. Cox regression analysis was used to investigate the association between the prognosis of patients with cancer and the PCs. Subgroups and sensitivity analyses were also conducted.

RESULTS

Through PCA, 4 distinct PCs were identified, collectively explaining 88.3% of the variance. PC1, primarily characterized by general obesity, exhibited a significant inverse association with risk of cancer-related death (adjusted hazard ratio [HR]: 0.86; 95% confidence interval [CI]: 0.83, 0.88). PC2 (short stature with high TSF) was not significantly associated with cancer prognosis. PC3 (high BMI coupled with low HGS) demonstrated a significant increase with risk of cancer-related death (adjusted HR: 1.08; 95% CI: 1.05, 1.11). PC4 (tall stature with high TSF) exhibited a significant association with increased cancer risk (adjusted HR: 1.05; 95% CI: 1.02, 1.07). These associations varied across different cancer stages. The stability of the results was confirmed through sensitivity analyses.

CONCLUSIONS

Different body sizes are associated with distinct prognostic outcomes in patients with cancer. The impact of BMI on prognosis is influenced by both HGS and subcutaneous fat. This finding may influence the clinical care of cancer and improve the survival of cancer patients.

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer

BACKGROUND

The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that influence surgical difficulty. Establishing a nomogram aims to assist clinical practitioners in formulating more effective surgical plans before the procedure.

METHODS

This study included 186 patients with rectal cancer who underwent LaTME from January 2018 to December 2020. They were divided into a training cohort (n = 131) versus a validation cohort (n = 55). The difficulty of LaTME was defined based on Escal's et al. scoring criteria with modifications. We utilized Lasso regression to screen the preoperative clinical characteristic variables and intraoperative information most relevant to surgical difficulty for the development and validation of four ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT). The performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC), sensitivity, specificity, and accuracy. Logistic regression-based column-line plots were created to visualize the predictive model. Consistency statistics (C-statistic) and calibration curves were used to discriminate and calibrate the nomogram, respectively.

RESULTS

In the validation cohort, all four ML models demonstrate good performance: SVM AUC = 0.987, RF AUC = 0.953, LR AUC = 0.950, and DT AUC = 0.904. To enhance visual evaluation, a logistic regression-based nomogram has been established. Predictive factors included in the nomogram are body mass index (BMI), distance between the tumor to the dentate line ≤ 10 cm, radiodensity of visceral adipose tissue (VAT), area of subcutaneous adipose tissue (SAT), tumor diameter >3 cm, and comorbid hypertension.

CONCLUSION

In this study, four ML models based on intraoperative and preoperative risk factors and a nomogram based on logistic regression may be of help to surgeons in evaluating the surgical difficulty before operation and adopting appropriate responses and surgical protocols.

Influence of skeletal muscle and intermuscular fat on postoperative complications and long-term survival in rectal cancer patients

BACKGROUND

The body composition of patients with rectal cancer potentially affects postoperative outcomes. This study explored the correlations between skeletal muscle and adipose tissue quantified by computed tomography (CT) with postoperative complications and long-term prognosis in patients with rectal cancer after surgical resection.

METHODS

This retrospective cohort study included patients with rectal cancer who underwent surgical resection at the Wuhan Union Hospital between 2014 and 2018. CT images within 3 months prior to the surgery were used to quantify the indices of skeletal muscle and adipose tissue at the levels of the third lumbar vertebra (L3) and umbilicus. Optimal cut-off values for each index were defined separately for males and females. Associations between body composition and postoperative complications, overall survival (OS), and disease-free survival (DFS) were evaluated using logistic and Cox proportional hazards models.

RESULTS

We included 415 patients (240 males and 175 females; mean age: 57.8 ± 10.5 years). At the L3 level, a high skeletal muscle density (SMD; hazard ratio [HR]: 0.357, 95% confidence interval [CI]: 0.191-0.665, P = 0.001; HR: 0.571, 95% CI: 0.329-0.993, P = 0.047) and a high skeletal muscle index (SMI; HR: 0.435, 95% CI 0.254-0.747, P = 0.003; HR: 0.568, 95% CI: 0.359-0.897, P = 0.015) were independent prognostic factors for better OS and DFS. At the umbilical level, a large intermuscular fat area (IMFA; HR: 1.904, 95% CI: 1.068-3.395, P = 0.029; HR: 2.064, 95% CI: 1.299-3.280, P = 0.002) was an independent predictive factor for worse OS and DFS, and a high SMI (HR: 0.261, 95% CI: 0.132-0.517, P < 0.001; HR: 0.595, 95% CI: 0.387-0.913, P = 0.018) was an independent prognostic factor for better OS and DFS. The models combining body composition and clinical indicators had good predictive abilities for OS. The receiver operating characteristic areas under the curve were 0.848 and 0.860 at the L3 and umbilical levels, respectively (both P < 0.05).

CONCLUSIONS

No correlations existed between CT-quantified body composition parameters and postoperative complications. However, a high SMD and high SMI were significantly associated with longer OS and DFS at the L3 level, whereas a large IMFA and low SMI were associated with worse OS and DFS at the umbilical level. Combining CT-quantified body composition and clinical indicators could help physicians predict the prognosis of patients with rectal cancer after surgery.

Influence of abdominal fat distribution and inflammatory status on post-operative prognosis in non-small cell lung cancer patients: a retrospective cohort study

PURPOSE

To evaluate the influence of visceral fat area (VFA), subcutaneous fat area (SFA), the systemic immune-inflammation index (SII) and total inflammation-based systemic index (AISI) on the postoperative prognosis of non-small cell lung cancers (NSCLC) patients.

METHODS

266 NSCLC patients received surgery from two academic medical centers were included. To assess the effect of abdominal fat measured by computed tomography (CT) imaging and inflammatory indicators on patients' overall survival (OS) and progression-free survival (PFS), Kaplan-Meier survival analysis and Cox proportional hazards models were used.

RESULTS

Kaplan-Meier analysis showed the OS and PFS of patients in high-VFA group was better than low-VFA group (p < 0.05). AISI and SII were shown to be risk factors for OS and PFS (p < 0.05) after additional adjustment for BMI (Cox regression model II). After further adjustment for VFA (Cox regression model III), low-SFA group had longer OS (p < 0.05). Among the four subgroups based on VFA (high/low) and SFA (high/low) (p < 0.05), the high-VFA & low-SFA group had the longest median OS (108 months; 95% CI 74-117 months) and PFS (85 months; 95% CI 65-117 months), as well as the lowest SII and AISI (p < 0.05). Low-SFA was a protective factor for OS with different VFA stratification (p < 0.05).

CONCLUSION

VFA, SFA, SII and AISI may be employed as significant prognostic markers of postoperative survival in NSCLC patients. Moreover, excessive SFA levels may encourage systemic inflammation decreasing the protective impact of VFA, which may help to provide targeted nutritional support and interventions for postoperative NSCLC patients with poor prognosis.

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model

OBJECTIVES

This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images.

METHODS

CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis.

RESULTS

The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843-0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC).

CONCLUSION

Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.

Association of sex-specific abdominal adipose tissue with WHO/ISUP grade in clear cell renal cell carcinoma

OBJECTIVES

To explore the association between computed tomography (CT)-measured sex-specific abdominal adipose tissue and the pathological grade of clear cell renal cell carcinoma (ccRCC).

METHODS

This retrospective study comprised 560 patients (394 males and 166 females) with pathologically proven ccRCC (467 low- and 93 high-grade). Abdominal CT images were used to assess the adipose tissue in the subcutaneous, visceral, and intermuscular regions. Subcutaneous fat index (SFI), visceral fat index (VFI), intermuscular fat index (IFI), total fat index (TFI), and relative visceral adipose tissue (rVAT) were calculated. Univariate and multivariate logistic regression analyses were performed according to sex to identify the associations between fat-related parameters and pathological grade.

RESULTS

IFI was significantly higher in high-grade ccRCC patients than in low-grade patients for both men and women. For male patients with high-grade tumors, the SFI, VFI, TFI, and rVAT were significantly lower, but not for female patients. In both univariate and multivariate studies, the IFI continued to be a reliable and independent predictor of high-grade ccRCC, regardless of sex.

CONCLUSIONS

Intermuscular fat index proved to be a valuable biomarker for the pathological grade of ccRCC and could be used as a reliable independent predictor of high-grade ccRCC for both males and females.

CRITICAL RELEVANCE STATEMENT

Sex-specific fat adipose tissue can be used as a new biomarker to provide a new dimension for renal tumor-related research and may provide new perspectives for personalized tumor management decision-making approaches.

KEY POINTS

• There are sex differences in distribution of subcutaneous fat and visceral fat. • The SFI, VFI, TFI, and rVAT were significantly lower in high-grade ccRCC male patients, but not for female patients. • Intermuscular fat index can be used as a reliable independent predictor of high-grade ccRCC for both males and females.

Abdominal Visceral-to-Subcutaneous Fat Volume Ratio Predicts Survival and Response to First-Line Palliative Chemotherapy in Patients with Advanced Gastric Cancer

Prognosis in advanced gastric cancer (aGC) is predicted by clinical factors, such as stage, performance status, metastasis location, and the neutrophil-to-lymphocyte ratio. However, the role of body composition and sarcopenia in aGC survival remains debated. This study aimed to evaluate how abdominal visceral and subcutaneous fat volumes, psoas muscle volume, and the visceral-to-subcutaneous (VF/SF) volume ratio impact overall survival (OS) and progression-free survival (PFS) in aGC patients receiving first-line palliative chemotherapy. We retrospectively examined CT scans of 65 aGC patients, quantifying body composition parameters (BCPs) in 2D and 3D. Normalized 3D BCP volumes were determined, and the VF/SF ratio was computed. Survival outcomes were analyzed using the Cox Proportional Hazard model between the upper and lower halves of the distribution. Additionally, response to first-line chemotherapy was compared using the χ2 test. Patients with a higher VF/SF ratio (N = 33) exhibited significantly poorer OS (p = 0.02) and PFS (p < 0.005) and had a less favorable response to first-line chemotherapy (p = 0.033), with a lower Disease Control Rate (p = 0.016). Notably, absolute BCP measures and sarcopenia did not predict survival. In conclusion, radiologically assessed VF/SF volume ratio emerged as a robust and independent predictor of both survival and treatment response in aGC patients.

Prognostic role of computed tomography analysis using deep learning algorithm in patients with chronic hepatitis B viral infection

BACKGROUND/AIMS

The prediction of clinical outcomes in patients with chronic hepatitis B (CHB) is paramount for effective management. This study aimed to evaluate the prognostic value of computed tomography (CT) analysis using deep learning algorithms in patients with CHB.

METHODS

This retrospective study included 2,169 patients with CHB without hepatic decompensation who underwent contrast-enhanced abdominal CT for hepatocellular carcinoma (HCC) surveillance between January 2005 and June 2016. Liver and spleen volumes and body composition measurements including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle indices were acquired from CT images using deep learning-based fully automated organ segmentation algorithms. We assessed the significant predictors of HCC, hepatic decompensation, diabetes mellitus (DM), and overall survival (OS) using Cox proportional hazard analyses.

RESULTS

During a median follow-up period of 103.0 months, HCC (n=134, 6.2%), hepatic decompensation (n=103, 4.7%), DM (n=432, 19.9%), and death (n=120, 5.5%) occurred. According to the multivariate analysis, standardized spleen volume significantly predicted HCC development (hazard ratio [HR]=1.01, P=0.025), along with age, sex, albumin and platelet count. Standardized spleen volume (HR=1.01, P<0.001) and VAT index (HR=0.98, P=0.004) were significantly associated with hepatic decompensation along with age and albumin. Furthermore, VAT index (HR=1.01, P=0.001) and standardized spleen volume (HR=1.01, P=0.001) were significant predictors for DM, along with sex, age, and albumin. SAT index (HR=0.99, P=0.004) was significantly associated with OS, along with age, albumin, and MELD.

CONCLUSION

Deep learning-based automatically measured spleen volume, VAT, and SAT indices may provide various prognostic information in patients with CHB.

Fat quantification: Imaging methods and clinical applications in cancer

Recently, the study of the relationship between lipid metabolism and cancer has evolved. The characteristics of intratumoral and peritumoral fat are distinct and changeable during cancer development. Subcutaneous and visceral adipose tissue are also associated with cancer prognosis. In non-invasive imaging, fat quantification parameters such as controlled attenuation parameter, fat volume fraction, and proton density fat fraction from different imaging methods complement conventional images by providing concrete fat information. Therefore, measuring the changes of fat content for further understanding of cancer characteristics has been applied in both research and clinical settings. In this review, the authors summarize imaging advances in fat quantification and highlight their clinical applications in cancer precaution, auxiliary diagnosis and classification, therapy response monitoring, and prognosis.

The association between the body roundness index and the risk of colorectal cancer: a cross-sectional study

BACKGROUND

Colorectal cancer (CRC), has a link between obesity, especially visceral fat. The body roundness index (BRI) can more accurately assess body fat and visceral fat levels. It is, however, unknown whether BRI is associated with CRC risk.

METHODS

53,766 participants were enrolled from the National Health and Nutrition Examination Survey (NHANES). Analysing the corelation between BRI and CRC risk was performed using logistic regression. Stratified analyses revealed the association based on the population type. Receiver operating characteristic curve (ROC) was performed for predicting CRC risk using different anthropometric indices.

RESULTS

The risk of CRC mounting apparently with elevated BRI for participants with CRC compared to normal participants (P-trend < 0.001). The association persisted even after adjusting for all covariates (P-trend = 0.017). In stratified analyses, CRC risk increased with increasing BRI, especially among those who were inactive (OR (95% CI): Q3 3.761 (2.139, 6.610), P < 0.05, Q4 5.972 (3.347, 8.470), P < 0.01), overweight (OR (95% CI): Q3 2.573 (1.012, 7.431), P < 0.05, Q4 3.318 (1.221, 9.020), P < 0.05) or obese (OR (95% CI): Q3 3.889 (1.829, 8.266), P < 0.001, Q4 4.920 (2.349, 10.308), P < 0.001). ROC curve showed that BRI had a better ability in forecasting the risk of CRC than other anthropometric indices such as body weight etc. (all P < 0.05).

CONCLUSIONS

CRC risk and BRI have a positive and significant relationship, particularly in inactive participants with BMI ≥ 25 kg/m². It is hoped that these results will raise awareness of the importance of reducing visceral fat deposition.

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer

BACKGROUND. CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiautomated tools to evaluate body composition in patients with colorectal cancer (CRC). OBJECTIVE. The purpose of this article is to assess the utility of fully automated body composition measures derived from pretreatment CT examinations in predicting survival in patients with CRC. METHODS. This retrospective study included 1766 patients (mean age, 63.7 ± 14.4 [SD] years; 862 men, 904 women) diagnosed with CRC between January 2001 and September 2020 who underwent pretreatment abdominal CT. A panel of fully automated artificial intelligence-based algorithms was applied to portal venous phase images to quantify skeletal muscle attenuation at the L3 lumbar level, visceral adipose tissue (VAT) area and subcutaneous adipose tissue (SAT) area at L3, and abdominal aorta Agatston score (aortic calcium). The electronic health record was reviewed to identify patients who died of any cause (n = 848). ROC analyses and logistic regression analyses were used to identify predictors of survival, with attention to highest- and lowest-risk quartiles. RESULTS. Patients who died, compared with patients who survived, had lower median muscle attenuation (19.2 vs 26.2 HU, p < .001), SAT area (168.4 cm² vs 197.6 cm², p < .001), and aortic calcium (620 vs 182, p < .001). Measures with highest 5-year AUCs for predicting survival in patients without (n = 1303) and with (n = 463) metastatic disease were muscle attenuation (0.666 and 0.701, respectively) and aortic calcium (0.677 and 0.689, respectively). A combination of muscle attenuation, SAT area, and aortic calcium yielded 5-year AUCs of 0.758 and 0.732 in patients without and with metastases, respectively. Risk of death was increased (p < .05) in patients in the lowest quartile for muscle attenuation (hazard ratio [HR] = 1.55) and SAT area (HR = 1.81) and in the highest quartile for aortic calcium (HR = 1.37) and decreased (p < .05) in patients in the highest quartile for VAT area (HR = 0.79) and SAT area (HR = 0.76). In 423 patients with available BMI, BMI did not significantly predict death (p = .75). CONCLUSION. Fully automated CT-based body composition measures including muscle attenuation, SAT area, and aortic calcium predict survival in patients with CRC. CLINICAL IMPACT. Routine pretreatment body composition evaluation could improve initial risk stratification of patients with CRC.

The Impact of Pre-Chemotherapy Body Composition and Immunonutritional Markers on Chemotherapy Adherence in Stage III Colorectal Cancer Patients

Patients with colorectal cancer (CRC) often fail to complete full-course chemotherapy with a standard dose due to various reasons. This study aimed to determine whether body composition affects chemotherapy adherence in patients with CRC. The medical records of 107 patients with stage III CRC who underwent adjuvant folinic acid, fluorouracil and oxaliplatin (FOLFOX) chemotherapy at a single center between 2014 and 2018 were analyzed retrospectively. Blood test results for selected immunonutritional markers were analyzed and body composition was measured through computed tomography. Univariate and multivariate analyses were performed on low and high relative dose intensity (RDI) groups, based on an RDI of 0.85. In the univariate analysis, a higher skeletal muscle index was correlated with a higher RDI (p = 0.020). Psoas muscle index was also higher in patients with high RDI than in those with low RDI (p = 0.026). Fat indices were independent of RDI. Multivariate analysis was performed for the aforementioned factors and results showed that age (p = 0.028), white blood cell count (p = 0.024), and skeletal muscle index (p = 0.025) affected RDI. In patients with stage III CRC treated with adjuvant FOLFOX chemotherapy, a decrease in RDI was related to age, white blood cell count, and skeletal muscle index. Therefore, if we adjust the drug dosage in consideration of these factors, we can expect an increased treatment efficiency in patients by increasing chemotherapy compliance.

Associations of radiological features of adipose tissues with postoperative complications and overall survival of gastric cancer patients

OBJECTIVES

To evaluate the associations of the radiological features of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) with the postoperative complications and overall survival (OS) of patients undergoing laparoscopic radical gastrectomy for gastric cancer.

METHODS

One hundred forty-two patients underwent laparoscopic radical gastrectomy for gastric cancer from February 2013 to May 2016. The radiological features of SAT and VAT were studied by preoperative computed tomography, and the relationships between the parameters of adipose tissues and the intraoperative and postoperative conditions and OS rate of patients were evaluated.

RESULTS

A positive linear correlation was found between VAT area and operation duration, and a negative linear correlation was found between VAT density and intraoperative blood loss (p < 0.05 in both). VAT area was an independent risk factor for postoperative complications. VAT area and VAT density were independent risk factors for OS in gastric cancer.

CONCLUSIONS

A high VAT area was an independent risk factor for postoperative complications of gastric cancer, whereas a low VAT area and high VAT density were independent risk factors for poor prognosis in terms of OS in gastric cancer.

KEY POINTS

• A large visceral adipose tissue (VAT) area is an unfavourable factor affecting the outcomes of radical gastrectomy for gastric cancer. • Low VAT density may be more likely to cause intraoperative bleeding. • VAT area and VAT density were independent risk factors for the OS of patients with gastric cancer.

CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation

CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT scans for staging, treatment response, and surveillance, providing the opportunity for performing quantitative body composition assessment as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semi-automated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.