Prognostic value of low skeletal muscle mass in patient treated by exclusive curative radiochemotherapy for a NSCLC

Unveiling the Intricate Dance: How Cancer Orchestrates Muscle Wasting and Sarcopenia

Sarcopenia is a prevalent and clinically significant condition, particularly among older age groups and those with chronic disease. Patients with cancer frequently suffer from sarcopenia and progressive loss of muscle mass, strength, and function. The complex interplay between cancer and its treatment, including medical therapy, radiotherapy, and surgery, significantly contributes to the onset and worsening of sarcopenia. Cancer induces muscle wasting through inflammatory processes, metabolic alterations, and hormonal imbalance. Moreover, medical and radiation therapies exert direct toxic effects on muscles, contributing to the impairment of physical function. Loss of appetite, malnutrition, and physical inactivity further exacerbate muscle wasting in cancer patients. Imaging techniques are the cornerstones for sarcopenia diagnosis. Magnetic resonance imaging, computed tomography, and dual-energy X-ray absorptiometry provide valuable insights into muscle structure and quality. Although each modality has advantages and limitations, magnetic resonance imaging produces high-resolution images and provides dynamic information about muscle function. Despite these challenges, addressing sarcopenia is essential for optimizing treatment outcomes and improving survival rates in patients with cancer. This review explored the factors contributing to sarcopenia in oncologic patients, emphasizing the importance of early detection and comprehensive management strategies.

Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer

BACKGROUND

Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy.

METHODS

We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpoint-inhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used. Anthropometric parameters were measured three-dimensionally (3D) by a deep learning software (Anthropometer3DNet) allowing an automatic multislice measurement of lean body mass, fat body mass (FBM), muscle body mass (MBM), visceral fat mass (VFM) and sub-cutaneous fat mass (SFM). Body mass index (BMI) and weight loss (WL) were also retrieved. Receiver operator characteristic (ROC) curve analysis was performed and overall survival was calculated using Kaplan-Meier (KM) curve and Cox regression analysis.

RESULTS

In the overall cohort, 1-year mortality rate was 0.496 (95% CI: 0.457 to 0.537) for 309 events and 5-year mortality rate was 0.196 (95% CI: 0.165 to 0.233) for 477 events. In the univariate Kaplan-Meier analysis, prognosis was worse (p<0.001) for patients with low SFM (<3.95 kg/m2), low FBM (<3.26 kg/m2), low VFM (<0.91 kg/m2), low MBM (<5.85 kg/m2) and low BMI (<24.97 kg/m2). The same parameters were significant in the Cox univariate analysis (p<0.001) and, in the multivariate stepwise Cox analysis, the significant parameters were MBM (p<0.0001), SFM (0.013) and WL (0.0003). In subanalyses according to the type of cancer, all body composition parameters were statistically significant for NSCLC in ROC, KM and Cox univariate analysis while, for melanoma, none of them, except MBM, was statistically significant. In multivariate Cox analysis, the significant parameters for NSCLC were MBM (HR=0.81, p=0.0002), SFM (HR=0.94, p=0.02) and WL (HR=1.06, p=0.004). For NSCLC, a KM analysis combining SFM and MBM was able to separate the population in three categories with the worse prognostic for the patients with both low SFM (<5.22 kg/m2) and MBM (<6.86 kg/m2) (p<0001). On the external validation cohort, combination of low SFM and low MBM was pejorative with 63% of mortality at 1 year versus 25% (p=0.0029).

CONCLUSIONS

3D measured low SFM and MBM are significant prognosis factors of NSCLC treated by immune checkpoint inhibitors and can be combined to improve the prognostic value.

Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs

Background: Body composition could help to better define the prognosis of cancers treated with anti-angiogenics. The aim of this study is to evaluate the prognostic value of 3D and 2D anthropometric parameters in patients given anti-angiogenic treatments. Methods: 526 patients with different types of cancers were retrospectively included. The software Anthropometer3DNet was used to measure automatically fat body mass (FBM3D), muscle body mass (MBM3D), visceral fat mass (VFM3D) and subcutaneous fat mass (SFM3D) in 3D computed tomography. For comparison, equivalent two-dimensional measurements at the L3 level were also measured. The area under the curve (AUC) of the receiver operator characteristics (ROC) was used to determine the parameters’ predictive power and optimal cut-offs. A univariate analysis was performed using Kaplan−Meier on the overall survival (OS). Results: In ROC analysis, all 3D parameters appeared statistically significant: VFM3D (AUC = 0.554, p = 0.02, cutoff = 0.72 kg/m2), SFM3D (AUC = 0.544, p = 0.047, cutoff = 3.05 kg/m2), FBM3D (AUC = 0.550, p = 0.03, cutoff = 4.32 kg/m2) and MBM3D (AUC = 0.565, p = 0.007, cutoff = 5.47 kg/m2), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, p = 0.034). In log-rank tests, low VFM3D (p = 0.014), low SFM3D (p < 0.0001), low FBM3D (p = 0.00019) and low VFA2D (p = 0.0063) were found as a significant risk factor. Conclusion: automatic and 3D body composition on pre-therapeutic CT is feasible and can improve prognostication in patients treated with anti-angiogenic drugs. Moreover, the 3D measurements appear to be more effective than their 2D counterparts.

Adiposity and cancer survival: a systematic review and meta-analysis

PURPOSE

The increasing availability of clinical imaging tests (especially CT and MRI) that directly quantify adipose tissue has led to a rapid increase in studies examining the relationship of visceral, subcutaneous, and overall adiposity to cancer survival. To summarize this emerging body of literature, we conducted a systematic review and meta-analysis of imaging-measured as well as anthropometric proxies for adipose tissue distribution and cancer survival across a wide range of cancer types.

METHODS

Using keywords related to adiposity, cancer, and survival, we conducted a systematic search of the literature in PubMed and MEDLINE, Embase, and Web of Science Core Collection databases from database inception to 30 June 2021. We used a random-effect method to calculate pooled hazard ratios (HR) and corresponding 95% confidence intervals (CI) within each cancer type and tested for heterogeneity using Cochran's Q test and the I2 test.

RESULTS

We included 203 records for this review, of which 128 records were utilized for quantitative analysis among 10 cancer types: breast, colorectal, gastroesophageal, head and neck, hepatocellular carcinoma, lung, ovarian, pancreatic, prostate, and renal cancer. We found that imaging-measured visceral, subcutaneous, and total adiposity were not significantly associated with increased risk of overall mortality, death from primary cancer, or cancer progression among patients diagnosed with these 10 cancer types; however, we found significant or high heterogeneity for many cancer types. For example, heterogeneity was similarly high when the pooled HRs (95% CI) for overall mortality associated with visceral adiposity were essentially null as in 1.03 (0.55, 1.92; I2 = 58%) for breast, 0.99 (0.81, 1.21; I2 = 71%) for colorectal, versus when they demonstrated a potential increased risk 1.17 (0.85, 1.60; I2 = 78%) for hepatocellular carcinoma and 1.62 (0.90, 2.95; I2 = 84%) for renal cancer.

CONCLUSION

Greater adiposity at diagnosis (directly measured by imaging) is not associated with worse survival among cancer survivors. However, heterogeneity and other potential limitations were noted across studies, suggesting differences in study design and adiposity measurement approaches, making interpretation of meta-analyses challenging. Future work to standardize imaging measurements and data analyses will strengthen research on the role of adiposity in cancer survival.

The Effect of Sarcopenia and Metabolic PET-CT Parameters on Survival in Locally Advanced Non-Small Cell Lung Carcinoma

The treatment of stage III non-small cell lung cancer (NSCLC) is complex. Here, we aimed to examine the prognostic utility of sarcopenia and metabolic muscle volumes and evaluate their relationship with oncological treatments in patients with locally advanced NSCLC. Patients with unresectable stage III NSCLC were evaluated retrospectively. Muscle fields were measured, and metabolic parameters of the psoas were obtained. The skeletal muscle index (SMI), sarcopenia, sarcopenic obesity, and body mass index (BMI)-associated sarcopenia were evaluated. Fifty-three (94.6%) patients were men, and three (5.4%) were women. Sarcopenia was identified in 36 (64.3%) patients. Pretreatment sarcopenia and BMI-associated sarcopenia negatively affected overall survival (p = 0.040 and 0.023, respectively). A high psoas SUVmean (Standardized Uptake Value mean) and low mean psoas HU (Hounsfield unit) were poor prognostic factors (p = 0.009 and 0.014, respectively). SMI and muscle mass decreased after oncological treatment. Advanced age, inability to complete treatment, administration of chemoradiotherapy after chemotherapy, presence of sarcopenia, and a low mean psoas HU decreased survival. In conclusion, sarcopenia and BMI-associated sarcopenia are poor prognostic factors in patients with lung cancer. Oncological treatments can adversely affect muscle mass. The metabolic parameters of the psoas muscle can predict patient prognosis.

The association between skeletal muscle measures and chemotherapy-induced toxicity in non-small cell lung cancer patients

BACKGROUND

Chemotherapy-induced toxicities frequently occur in non-small cell lung cancer (NSCLC) patients treated with platinum-based chemotherapy. Low skeletal muscle mass (SMM) has been associated with a higher incidence of toxicities for several types of cancers and cytostatics. The aim of this study was to evaluate the association between skeletal muscle measures and chemotherapy-induced toxicity in a large cohort of NSCLC patients.

METHODS

A multicentre prospective follow-up study (PGxLUNG, NTR number NL5373610015) in NSCLC patients was conducted. Included were patients diagnosed with NSCLC (stage II-IV) treated with first-line platinum-based (cisplatin or carboplatin) chemotherapy of whom pretreatment imaging was available. Skeletal muscle area (SMA) segmentation was performed on abdominal imaging at the level of the third lumbar vertebra (L3). SMA at the level of L3 was corrected for squared height (m2 ) to yield the lumbar skeletal muscle mass index (LSMI). Skeletal muscle density (SMD) was calculated as the mean Hounsfield Unit (HU) of the segmented SMA. SMM and SMD were categorized as low, intermediate, and high, based on LSMI and mean HU tertiles, respectively. Chemotherapy-induced toxicity was scored using CTCAE v4.03 and categorized into haematological (anaemia, leukocytopenia, neutropenia, and thrombocytopenia), non-haematological (nephrotoxicity, neurotoxicity, and esophagitis), and dose-limiting toxicity (DLT) (treatment switch, delay, de-escalation, discontinuation, or hospitalization). The relationship between SMM, SMD, and toxicities was assessed with logistic regression modelling taking into account potential confounders like gender and body mass index (BMI).

RESULTS

In total, 297 patients (male n = 167, median age 64 years) were included. Haematological toxicity grade 3/4 was experienced in 36.6% (n = 108) of the patients, 24.6% (n = 73) experienced any non-haematological toxicity grade ≥2, and 55.6% (n = 165) any DLT. Multivariate logistic regression analysis showed that low SMM (ORadj 2.41, 95% CI 1.31-4.45, P = 0.005) and age at diagnosis >65 years (ORadj 1.76, 95% CI 1.07-2.90, P = 0.025) were statistically significantly associated with overall haematological toxicity grade 3/4. No statistically significant associations were found between low SMM or low SMD and non-haematological toxicities. Low SMM (ORadj 2.23, 95% CI 1.23-4.04, P = 0.008) and high SMD (ORadj 0.41, 95% CI 0.23-0.74, P = 0.003) were statistically significantly associated with a higher respectively lower risk of DLT.

CONCLUSIONS

Non-small cell lung cancer patients with pretreatment low SMM are at significant higher risk for haematological toxicities grade 3/4 and DLT. NSCLC patients with high SMD are at significant lower risk for DLT. Further studies should be aimed to investigate whether platinum dosing based on skeletal muscle measurements and/or improvement of pretreatment SMM/SMD could reduce the risk of toxicity without compromising efficacy.

Cancer, Phase Angle and Sarcopenia: The Role of Diet in Connection with Lung Cancer Prognosis

Lung cancer is the most common cause of cancer death and is associated with malnutrition and sarcopenia. The detection of sarcopenia and conduction of simple body composition measurements, such as the phase angle (PhA) deriving from bioelectrical impedance analysis (BIA), can help to early identify, monitor, prevent and treat malnutrition. The present review aims to clarify the relationship between PhA and sarcopenia with the pathophysiology, clinical outcomes, and therapeutic aspects of lung cancer. PhA and sarcopenia are connected to lung cancer prognosis through various mechanisms including inflammation and oxidative stress, although more research is needed to identify the critical thresholds for increased mortality risk. Moreover, emphasis is given on the role of dietary interventions (oral nutritional supplementation, and dietary counseling) to manage sarcopenia and related variables in patients with lung cancer. Oral nutritional supplements and/or those containing n - 3 polyunsaturated fatty acids may have a positive effect on physical strength measures and muscle mass if administered at the beginning of chemotherapy. Data on sole dietary counseling or multimodal interventions are less promising so far. In the future, sophisticated body composition phenotypes deriving from the described methods along with artificial intelligence techniques could be used to design personalized nutrition interventions and timely treat these patients.

Utility of Noncancerous Chest CT Features for Predicting Overall Survival and Noncancer Death in Patients With Stage I Lung Cancer Treated With Stereotactic Body Radiotherapy

Background: Noncancerous imaging markers can be readily derived from pretreatment diagnostic and radiotherapy planning chest CT examinations. Objective: To explore the ability of noncancerous features on chest CT to predict overall survival (OS) and noncancer-related death in patients with stage I lung cancer treated with stereotactic body radiation therapy (SBRT). Methods: This retrospective study included 282 patients (168 female, 114 male; median age, 75 years) with stage I lung cancer treated with SBRT between January 2009 and June 2017. Pretreatment chest CT was used to quantify coronary artery calcium (CAC) score, pulmonary artery (PA)-to-aorta ratio, emphysema, and body composition in terms of the cross-sectional area and attenuation of skeletal muscle and subcutaneous adipose tissue at the T5, T8, and T10 vertebral levels. Associations of clinical and imaging features with OS were quantified using a multivariable Cox proportional hazards (PH) model. Penalized multivariable Cox PH models to predict OS were constructed using clinical features only and using both clinical and imaging features. Models' discriminatory ability was assessed by constructing time-varying ROC curves and computing AUC at prespecified times. Results: After a median OS of 60.8 months (95% CI 55.8-68.9), 148 (52.5%) patients died, including 83 (56.1%) with noncancer deaths. Higher CAC score (11-399: hazard ratio [HR] 1.83 [95% CI 1.15-2.91], P=.01; ≥400: HR 1.63 [95% CI 1.01-2.63], P=.04), higher PA-to-aorta ratio (HR 1.33 [95% CI 1.16-1.52], P<.001, per 0.1-unit increase), and lower thoracic skeletal muscle index (HR 0.88 [95% CI 0.79-0.98], P=.02, per 10 cm²/m² increase) were independently associated with shorter OS. Discriminatory ability for 5-year OS was greater for the model including clinical and imaging features than for the model including clinical features only (AUC, 0.75 [95% CI 0.68-0.83] versus 0.61 [95% CI 0.53-0.70], p < .01). The model's most important clinical or imaging feature based on mean standardized regression coefficients was the PA-to-aorta ratio. Conclusions: In patients undergoing SBRT for stage I lung cancer, higher CAC score, higher PA-to-aorta ratio, and lower thoracic skeletal muscle index independently predicted worse OS. Clinical Impact: Noncancerous imaging features on chest CT performed before SBRT improve survival prediction compared with clinical features alone.

Construction and Validation of Two Novel Nomograms for Predicting the Overall Survival and Cancer-Specific Survival of NSCLC Patients with Bone Metastasis

Background

Bone metastasis (BM) is the most common site of metastasis in non-small cell lung carcinoma (NSCLC). We aimed to construct and validate 2 novel nomograms predicting the 3-, 6-, and 12-months overall survival (OS) and cancer-specific survival (CSS).

Methods

The clinical data of 7480 patients between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Results database (SEER). The patients were allocated randomly to training and validation cohorts in a 7:3 ratio. Cox proportional hazards regression models were used to identify prognostic risk factors and establish 2 nomograms. The prediction accuracy of nomograms was assessed by C-index, the area under the ROC curve (AUC), and calibration curves.

Results

A total of 244998 NSCLC patients were identified between 2010 and 2016, with 7480 found with BM, accounting for 3.1%. Overall, 7480 patients were enrolled in the OS nomogram construction and were randomized to the training set (n = 5236) and the validation set (n = 2244). Age, sex, race, marital status, histology, grade, primary site, T stage, N stage, liver metastasis, surgery, radiotherapy, and chemotherapy were found to correlate with OS. A total of 7422 samples were included in the CSS nomogram construction, randomly grouped into training set (n = 5195) and the validation set (n = 2227). Age, sex, race, histology, grade, primary site, T stage, N stage, brain metastasis, liver metastasis, surgery, radiotherapy, and chemotherapy were associated with CSS. Two nomograms were conducted to predict the 3-, 6-, and 12-months OS and CSS. The ROC curves and exhibited good performance for predicting OS and CSS.

Conclusion

We established and validated 2 high-performance nomograms to assist clinical doctors in making personalized treatment decisions.