Quality is more important than quantity: pre-operative sarcopenia is associated with poor survival in advanced ovarian cancer

Sarcopenia in gynaecological cancers

Gynaecological cancers (GCs) comprise a group of cancers that originate in the female reproductive organs. Each GC is unique, with different signs and symptoms, risk factors and therapeutic strategies. Worldwide, the majority of GCs are still associated with high mortality rates, especially ovarian, due to difficulty in early detection. Despite numerous studies on the underlying pathophysiology, research in the field of GCs poses unique scientific and technological challenges. These challenges require a concerted multi- and inter-disciplinary effort by the clinical, scientific and research communities to accelerate the advancement of prognostic, diagnostic, and therapeutic approaches. Sarcopenia is a multifactorial disease which leads to the systemic loss of skeletal muscle mass and function. It can be caused by malignancies, as well as due to malnutrition, physical inactivity, ageing and neuromuscular, inflammatory, and/or endocrine diseases. Anorexia and systemic inflammation can shift the metabolic balance of patients with cancer cachexia towards catabolism of skeletal muscle, and hence sarcopenia. Therefore, sarcopenia is considered as an indicator of poor general health status, as well as the possible indicator of advanced cancer. There is a growing body of evidence showing the prognostic significance of sarcopenia in various cancers, including GCs. This review will outline the clinical importance of sarcopenia in patients with GCs.

Sarcopenia diagnosed by computed tomography predicts postoperative complications in advanced epithelial ovarian cancer

BACKGROUND & AIMS

Surgery combined with chemotherapy remains the mainstay of treatment for advanced epithelial ovarian cancer. It is important to evaluate the occurrence of postoperative complications before operation and to prevent them. The purpose of this study is to investigate the role of sarcopenia diagnosed by CT scans in predicting postoperative complications in patients with ovarian cancer.

METHODS

Patients with PDS or IDS in the First Affiliated Hospital of Wenzhou Medical University from August 2017 to August 2022 were included. Sarcopenia was identified using CT scans at the T12 level. According to the Clavien-Dindo classification, postoperative complications are considered to have occurred in grades 2 and above. Logistic regression analysis was used to identify risk factors contributing to postoperative complications. P < 0.05 was considered statistically significant.

RESULTS

A total of 139 patients were included. Sarcopenia was observed in 24(17.27%) patients with advanced epithelial ovarian cancer. Compared to the non-sarcopenia group, the sarcopenia group had a higher incidence of postoperative complications (62.50% VS 42.61%, p = 0.032). Multivariate logistic analysis confirmed sarcopenia (OR = 3.241, p = 0.026), age over 65 years (OR = 3.296, p = 0.005), and intraoperative bleeding (OR = 1.002, p < 0.001) as independent predictors of postoperative complications in ovarian cancer.

DISCUSSION & CONCLUSIONS

Sarcopenia diagnosed based on CT body composition analysis may serve as a potential predictor for postoperative complications. Further research is warranted to explore preventive strategies and interventions to improve outcomes in this population.

The association of body composition phenotypes before chemotherapy with epithelial ovarian cancer mortality

BACKGROUND

The association of body composition with epithelial ovarian carcinoma (EOC) mortality is poorly understood. To date, evidence suggests that high adiposity is associated with decreased mortality (an obesity paradox), but the impact of muscle on this association has not been investigated. Herein, we define associations of muscle and adiposity joint-exposure body composition phenotypes with EOC mortality.

METHODS

Body composition from 500 women in the Body Composition and Epithelial Ovarian Cancer Survival Study was dichotomized as normal or low skeletal muscle index (SMI), a proxy for sarcopenia, and high or low adiposity. Four phenotypes were classified as fit (normal SMI and low adiposity; reference; 16.2%), overweight or obese (normal SMI and high adiposity; 51.2%), sarcopenia and overweight or obese (low SMI and high adiposity; 15.6%), and sarcopenia or cachexia (low SMI and low adiposity; 17%). We used multivariable Cox models to estimate associations of each phenotype with mortality for EOC overall and high-grade serous ovarian carcinoma (HGSOC).

RESULTS

Overweight or obesity was associated with up to 51% and 104% increased mortality in EOC and HGSOC [Hazard Ratio (HR)] = 1.51, 95% CI = 1.05 to 2.19 and HR = 2.04, 95% CI = 1.29 to 3.21). Sarcopenia and overweight or obesity was associated with up to 66% and 67% increased mortality in EOC and HGSOC (HR = 1.66, 95% CI = 1.13 to 2.45 and HR = 1.67, 95% CI = 1.05 to 2.68). Sarcopenia or cachexia was associated with up to 73% and 109% increased mortality in EOC and HGSOC (HR = 1.73, 95% CI = 1.14 to 2.63 and HR = 2.09, 95% CI = 1.25 to 3.50).

CONCLUSIONS

Overweight or obesity, sarcopenia and overweight or obesity, and sarcopenia or cachexia phenotypes were each associated with increased mortality in EOC and HGSOC. Exercise and dietary interventions could be leveraged as ancillary treatment strategies for improving outcomes in the most fatal gynecological malignancy with no previously established modifiable prognostic factors.

Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer

PURPOSE

Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with consistent protocols and labor-intensive processes. This study aimed to use interpretable machine learning (ML) to predict muscle radiodensity loss.

METHODS

This study included 723 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy between 2010 and 2019 at two tertiary centers (579 in cohort 1 and 144 in cohort 2). Muscle radiodensity was assessed from pre- and post-treatment CT acquired with consistent protocols, and a decrease in radiodensity ≥ 5% was defined as loss. Six ML models were trained, and their performances were evaluated using the area under the curve (AUC) and F1-score. The SHapley Additive exPlanations (SHAP) method was applied to interpret the ML models.

RESULTS

The CatBoost model achieved the highest AUC of 0.871 (95% confidence interval, 0.870-0.874) and F1-score of 0.688 (95% confidence interval, 0.685-0.691) among the models in the training set and outperformed in the external validation set, with an AUC of 0.839 and F1-score of 0.673. Albumin change, ascites, and residual disease were the most important features associated with a higher likelihood of muscle radiodensity loss. The SHAP force plot provided an individualized interpretation of model predictions.

CONCLUSION

An interpretable ML model can assist clinicians in identifying ovarian cancer patients at risk of muscle radiodensity loss after treatment and understanding the contributors of muscle radiodensity loss.

Assessment of myosteatosis and functionality in pretreatment gynecological cancer patients

PURPOSE

We aimed to investigate the relationship between pretreatment gynecologic cancer survival and the physical function of patients with myosteatosis. Understanding this relationship prior to treatment would help healthcare providers identify and refer patients with poor muscle quality to an exercise program prior to treatment.

METHODS

We conducted a cross-sectional analysis of 73 GC patients. Physical function was quantified using handgrip strength and an adapted version of the Senior Fitness Test (aerobic endurance not included). The EORTC QLC-C30 was used to evaluate general health quality. Myosteatosis (values below the median muscle radiodensity), muscle mass, and adipose tissue variables were calculated from the computed tomography (CT) scan at the third lumbar vertebra using specific software.

RESULTS

Seventy patients (50.9 ± 15.2) were included; 41.5% had stage III or IV disease, and 61.4% had cervical cancer. The myosteatosis group was 11.9 years older and showed reduced functioning compared to the normal-radiodensity group. Age and Timed Up and Go (TUG) test results were shown to be the most reliable predictors of muscle radiodensity in pretreatment gynecological patients according to multivariate regression analysis (R2 = 0.314).

CONCLUSION

Gynecological healthcare professionals should be aware that prompt exercise programs might be especially beneficial for older patients with reduced TUG performance to preserve muscle function and quality.

Predictive Value of a New Muscle Parameter in Patients with Resectable Gastric Cancer: A Pooled Analysis of Three Prospective Trials

BACKGROUND

Sarcopenia is closely associated with gastric cancer (GC) prognosis. However, its exact definition remains controversial.

METHODS

This study included computed tomography images and clinical data of patients from three prospective studies. The skeletal muscle index (SMI) and skeletal muscle radiation attenuation (SMRA) were analyzed, and a new muscle parameter, skeletal muscle gauge (SMG), was obtained by multiplying the two parameters. The values of the three indices for predicting the prognosis of patients with GC were compared.

RESULTS

The study included 717 patients. The findings showed median values of 42 cm2/m2 (range, 36.8-48.2 cm2/m2) for SMI, 45 HU (range, 41-49 HU) for SMRA, and 1842 (range, 1454-2260) for SMG. Postoperatively, 111 patients (15.5%) experienced complications. The 3-year overall survival (OS), disease-free survival (DFS), and recurrence-free survival (RFS) were 74.3%, 68.2%, and 70%, respectively. Univariate logistic analysis showed that postoperative complications were associated with SMI (odds ratio [OR] 0.94; 95% confidence interval [CI] 0.92-0.96), SMRA (OR, 0.87; 95% CI 0.84-0.90), and SMG (OR 0.99; 95% CI 0.98-0.99). After a two-step multivariate analysis, only SMG (OR 0.98, 95% CI 0.97-0.99) was an independent protective factor of postoperative complications. Multivariate analysis showed that SMG also was an independent protective factor of OS, DFS, and RFS. The patients were divided into low-SMG (L-SMG) group and high-SMG (H-SMG) groups. Chemotherapy benefit analysis of the patients with stage II low SMG showed that the OS, DFS, and RFS of the chemotherapy group were significantly better than those of the non-chemotherapy group (p < 0.05).

CONCLUSION

The prospective large sample data showed that the new muscle parameter, SMG, can effectively predict the short-term outcome and long-term prognosis of patients with resectable gastric cancer. As a new muscle parameter index, SMG is worthy of further study.

Adding the latency period to a muscle contraction model coupled to a membrane action potential model

Introduction: Skeletal muscle is responsible for multiple functions for maintaining energy homeostasis and daily activities. Muscle contraction is activated by nerve signals, causing calcium release and interaction with myofibrils. It is important to understand muscle behavior and its impact on medical conditions, like in the presence of some diseases and their treatment, such as cancer, which can affect muscle architecture, leading to deficits in its function. For instance, it is known that radiotherapy and chemotherapy also have effects on healthy tissues, leading to a reduction in the rate of force development and the atrophy of muscle fibers. The main aim is to reproduce the behavior of muscle contraction using a coupled model of force generation and the action potential of the cell membrane, inserting the latency period observed between action potential and force generation in the motor unit.

Methods: Mathematical models for calcium dynamics and muscle contraction are described, incorporating the role of calcium ions and rates of reaction. An action potential initiates muscle contraction, as described by the Hodgkin–Huxley model. The numerical method used to solve the equations is the forward Euler method.

Results and Discussion: The results show dynamic calcium release and force generation, aligning with previous research results, and the time interval between membrane excitation and force generation was accomplished. Future work should suggest simulating more motor units at the actual scale for the possibility of a comparison with real data collected from both healthy individuals and those who have undergone cancer treatment.

The microbiome and ovarian cancer: insights, implications, and therapeutic opportunities

Ovarian cancer is the leading cause of gynecologic cancer death in the United States. Most ovarian cancer patients are diagnosed with advanced-stage disease, which poses a challenge for early detection and effective treatment. At present, cytoreductive surgery and platinum-based chemotherapy are foundational for patients with newly diagnosed ovarian cancer, but unfortunately, most patients will recur and die of their disease. Therefore, there is a significant need to seek innovative, novel approaches for early detection and to overcome chemoresistance for ovarian cancer patients. The microbiome, comprising diverse microbial communities inhabiting various body sites, is vital in maintaining human health. Changes to the diversity and composition of the microbial communities impact the microbiota-host relationship and are linked to diseases, including cancer. The microbiome contributes to carcinogenesis through various mechanisms, including altered host immune response, modulation of DNA repair, upregulation of pro-inflammatory pathways, altered gene expression, and dysregulated estrogen metabolism. Translational and clinical studies have demonstrated that specific microbes contribute to ovarian cancer development and impact chemotherapy’s efficacy. The microbiome is malleable and can be altered through different approaches, including diet, exercise, medications, and fecal microbiota transplantation. This review provides an overview of the current literature regarding ovarian cancer and the microbiome of female reproductive and gastrointestinal tracts, focusing on mechanisms of carcinogenesis and options for modulating the microbiota for cancer prevention and treatment. Advancing our understanding of the complex relationship between the microbiome and ovarian cancer may provide a novel approach for prevention and therapeutic modulation in the future.

Re-Evaluating Chemotherapy Dosing Strategies for Ovarian Cancer: Impact of Sarcopenia

We investigated the impact of sarcopenia on adjuvant chemotherapy dosing in advanced epithelial ovarian cancer (EOC). The chemotherapy dosing and toxicity of 173 eligible patients who underwent cytoreductive surgery and adjuvant chemotherapy at a single institution were analyzed. Patients with a skeletal muscle index less than 39 cm2/m2 measured on a CT scan were considered sarcopenic. Sarcopenic and non-sarcopenic patients were compared with regard to relative dose intensity (RDI), completion of scheduled chemotherapy, toxicity, and survival. A total of 62 (35.8%) women were sarcopenic. Sarcopenic women were less likely to complete at least six cycles of chemotherapy (83.9% vs. 95.5%, p = 0.02). The mean RDI for both carboplatin (80.4% vs. 89.4%, p = 0.03) and paclitaxel (91.9% vs. 104.1%, p = 0.03) was lower in sarcopenic patients compared to non-sarcopenic patients. Despite these differences in chemotherapy, there was no difference in neutropenia or median overall survival (3.99 vs. 4.57 years, p = 0.62) between the sarcopenic and non-sarcopenic women, respectively. This study highlights the importance of considering lean body mass instead of body weight or surface area in chemotherapy dosing formulas for sarcopenic women with advanced EOC. Further research is needed to optimize chemotherapy strategies based on individual body composition, potentially leading to improved dosing strategies in this population.

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer

BACKGROUND

Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method.

METHODS

This study included the data of 617 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy at a tertiary centre between 2010 and 2019. The cohort data were split into training and test sets based on the treatment time. External validation was performed using 140 patients from a different tertiary centre. The skeletal muscle index (SMI) was measured from pre- and post-treatment CT scans, and a decrease in SMI ≥ 5% was defined as muscle loss. We evaluated five ML models to predict muscle loss, and their performance was determined using the area under the receiver operating characteristic curve (AUC) and F1 score. The features for analysis included demographic and disease-specific characteristics and relative changes in body mass index (BMI), albumin, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The SHAP method was applied to determine the importance of the features and interpret the ML models.

RESULTS

The median (inter-quartile range) age of the cohort was 52 (46-59) years. After treatment, 204 patients (33.1%) experienced muscle loss in the training and test datasets, while 44 (31.4%) patients experienced muscle loss in the external validation dataset. Among the five evaluated ML models, the random forest model achieved the highest AUC (0.856, 95% confidence interval: 0.854-0.859) and F1 score (0.726, 95% confidence interval: 0.722-0.730). In the external validation, the random forest model outperformed all ML models with an AUC of 0.874 and an F1 score of 0.741. The results of the SHAP method showed that the albumin change, BMI change, malignant ascites, NLR change, and PLR change were the most important factors in muscle loss. At the patient level, SHAP force plots demonstrated insightful interpretation of our random forest model to predict muscle loss.

CONCLUSIONS

Explainable ML model was developed using clinical data to identify patients experiencing muscle loss after treatment and provide information of feature contribution. Using the SHAP method, clinicians may better understand the contributors to muscle loss and target interventions to counteract muscle loss.

Association of malignant ascites with systemic inflammation and muscle loss after treatment in advanced-stage ovarian cancer

BACKGROUND

Malignant ascites is prevalent in advanced-stage ovarian cancer and may facilitate identification of the drivers of muscle loss. This study aimed to evaluate the association of ascites with changes in systemic inflammation and muscle after treatment of advanced-stage ovarian cancer.

METHODS

We evaluated 307 patients with advanced-stage (III/IVA) ovarian cancer who underwent primary debulking surgery and adjuvant platinum-based chemotherapy between 2010 and 2019. The changes in skeletal muscle index (SMI) and radiodensity (SMD) were measured using pre-surgery and post-chemotherapy portal-venous phase contrast-enhanced computed tomography scans at L3. Systemic inflammation was measured using albumin levels, prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR). Primary endpoint was the changes in SMI and SMD after treatment. Linear regression analysis was used to test associations between muscle change and other covariates. Mediation analysis was used to determine the mediator.

RESULTS

The median (range) age was 53 (23-83) years. The median duration (range) of follow-up was 5.2 (1.1-11.3) years. Overall, 187 (60.9%) patients had ascites. The changes in muscle and systemic inflammatory markers after treatment were significantly different between patients with and without ascites (SMI: -3.9% vs. 2.2%, P < 0.001; SMD: -4.0% vs. -0.4%, P < 0.001; albumin: -4.4% vs. 2.1%, P < 0.001; PNI: -8.4% vs. -0.1%, P < 0.001; NLR: 20.6% vs. -29.4%, P < 0.001; and PLR: 1.7% vs. -19.4%, P < 0.001). The changes in SMI and SMD were correlated with the changes in albumin, PNI, NLR, and PLR (all P < 0.001). In multiple linear regression, ascites and NLR changes were negatively while albumin change was positively correlated with SMI change (ascites: β = -3.19, P < 0.001; NLR change: β = -0.02, P = 0.003; albumin change: β = 0.37, P < 0.001). Ascites and NLR changes were negatively while PNI change was positively correlated with SMD change (ascites: β = -1.28, P = 0.02; NLR change: β = -0.02, P < 0.001; PNI change: β = 0.11, P = 0.04). In mediation analysis, ascites had a direct effect on SMI change (P < 0.001) and an indirect effect mediated by NLR change (indirect effects = -1.61, 95% confidence interval [CI]: -2.22 to -1.08) and albumin change (indirect effects = -2.92, 95% CI: -4.01 to -1.94). Ascites had a direct effect on SMD change (P < 0.001) and an indirect effect mediated by NLR change (indirect effects = -1.76, 95% CI: -2.34 to -1.22) and PNI change (indirect effects = -2.00, 95% CI: -2.79 to -1.36).

CONCLUSIONS

Malignant ascites was associated with enhanced systemic inflammation and muscle loss after primary debulking surgery and adjuvant chemotherapy in advanced-stage ovarian cancer. The association between ascites and muscle loss may be mediated by systemic inflammation.

Malnutrition, sarcopenia, and cancer cachexia in gynecologic cancer

Patients with gynecologic cancers are at risk for malnutrition, cancer cachexia, and sarcopenia. Accumulating data supports that malnourished patients with gynecologic cancer have worse overall survival, increased healthcare utilization and costs, and a higher incidence of postoperative complications and treatment toxicity than those who are not malnourished. Malnutrition is defined as insufficient energy intake, leading to altered body composition and subsequent impaired physical and cognitive function, and can result in sarcopenia and cachexia, defined as the loss of lean body mass and loss of body weight respectively. The etiology of cancer-related malnutrition is complex, resulting from a systemic pro-inflammatory state of malignancy with upregulation of muscle degradation pathways and metabolic derangements, including lipolysis and proteolysis, that may not respond to nutritional repletion alone. Numerous validated scoring systems and radiographic measures have been described to define and quantify the severity of malnutrition and muscle loss in both clinical and research settings. "Prehabilitation" and optimization of nutrition and functional status early in therapy may combat the development or worsening of malnutrition and associated syndromes and ultimately improve oncologic outcomes, but limited data exist in the context of gynecologic cancer. Multi-modality nutrition and physical activity interventions have been proposed to combat the biophysical losses related to malnutrition. Several trials are underway in gynecologic oncology patients to address these aims, but significant gaps in knowledge persist. Pharmacologic interventions and potential immune targets for combating cachexia related to malignancy are discussed in this review and may provide opportunities to target disease and cachexia. This article reviews currently available data regarding the implications, diagnostics, physiology, and intervention strategies for gynecologic oncology patients with malnutrition and its associated conditions.

Association of prognostic nutritional index with muscle loss and survival in patients with ovarian cancer treated with primary debulking surgery and chemotherapy

PURPOSE

Sarcopenia is prevalent in ovarian cancer and contributes to poor survival. This study is aimed at investigating the association of prognostic nutritional index (PNI) with muscle loss and survival outcomes in patients with ovarian cancer.

METHODS

This retrospective study analyzed 650 patients with ovarian cancer treated with primary debulking surgery and adjuvant platinum-based chemotherapy at a tertiary center from 2010 to 2019. PNI-low was defined as a pretreatment PNI of < 47.2. Skeletal muscle index (SMI) was measured on pre- and posttreatment computed tomography (CT) at L3. The cut-off for the SMI loss associated with all-cause mortality was calculated using maximally selected rank statistics.

RESULTS

The median follow-up was 4.2 years, and 226 deaths (34.8%) were observed. With a median duration of 176 days (interquartile range: 166-187) between CT scans, patients experienced an average decrease in SMI of 1.7% (P < 0.001). The cut-off for SMI loss as a predictor of mortality was - 4.2%. PNI-low was independently associated with SMI loss (odds ratio: 1.97, P = 0.001). On multivariable analysis of all-cause mortality, PNI-low and SMI loss were independently associated with all-cause mortality (hazard ratio: 1.43, P = 0.017; hazard ratio: 2.27, P < 0.001, respectively). Patients with both SMI loss and PNI-low (vs. neither) had triple the risk of all-cause mortality (hazard ratio: 3.10, P < 0.001).

CONCLUSIONS

PNI is a predictor of muscle loss during treatment for ovarian cancer. PNI and muscle loss are additively associated with poor survival. PNI can help clinicians guide multimodal interventions to preserve muscle and optimize survival outcomes.