Low L3 skeletal muscle index associated with the clinicopathological characteristics and prognosis of ovarian cancer: a meta-analysis

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.

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.

Relationship between paraspinal muscle properties and bone mineral density based on QCT in patients with lumbar disc herniation

OBJECTIVE

Increasing research suggests that paraspinal muscle fat infiltration may be a potential biological marker for the assessment of osteoporosis. Our aim was to investigate the relationship between lumbar paraspinal muscle properties on MRI and volumetric bone mineral density (vBMD) based on QCT in patients with lumbar disc herniation (LDH).

METHODS

A total of 383 patients (aged 24-76 years, 193 females) with clinically and radiologically diagnosed LDH were enrolled in this retrospective study. The muscle cross-sectional area (CSA) and the proton density fat fraction (PDFF) were measured for the multifidus (MF), erector spinae (ES) and psoas major (PS) at the central level of L3/4, L4/5 and L5/S1 on lumbar MRI. QCT was used to measure the vBMD of two vertebral bodies at L1 and L2 levels. Patients were divided into three groups based on their vBMD values: normal bone density group (> 120 mg/cm3), osteopenia group (80 to 120 mg/cm3) and osteoporosis group (< 80 mg/cm3). The differences in paraspinal muscle properties among three vBMD groups were tested by one-way ANOVA with post hoc analysis. The relationships between paraspinal muscle properties and vBMD were analyzed using Pearson correlation coefficients. Furthermore, the association between vBMD and paraspinal muscle properties was further evaluated using multiple linear regression analysis, with age and sex also included as predictors.

RESULTS

Among the 383 LDH patients, 191 had normal bone density, 129 had osteopenia and 63 had osteoporosis. In LDH patients, compared to normal and osteopenia group, paraspinal muscle PDFF was significantly greater in osteoporosis group, while paraspinal muscle CSA was lower (p < 0.001). After adjusting for age and sex, it was found that MF PDFF and PS CSA were found to be independent factors influencing vBMD (p < 0.05).

CONCLUSION

In patients with LDH, paraspinal muscle properties measured by IDEAL-IQ sequence and lumbar MR scan were found to be related to vBMD. There was a correlation between the degree of paraspinal muscle PDFF and decreasing vBMD, as well as a decrease paraspinal muscle CSA with decreasing vBMD. These findings suggest that clinical management should consider offering tailored treatment options for patients with LDH based on these associations.

Impact of magnetic resonance imaging-derived skeletal muscle index in locoregionally advanced nasopharyngeal carcinoma

PURPOSE

To evaluate the clinical implication of magnetic resonance imaging (MRI)-derived skeletal muscle index (SMI) in locoregionally advanced nasopharyngeal carcinoma (LANPC) patients undergoing induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CCRT) and further to develop a nomogram for predicting survival prognosis.

METHODS

SMI was determined through baseline MRI at the third cervical level. The nomogram was based on a training cohort involving 409 LANPC patients. We validated the prognostic accuracy of this prognostic model in an internal validation cohort (n = 204) and an external independent cohort (n = 272).

RESULTS

SMI was an independent risk factor for OS. A prognostic model comprising age, TNM stage and SMI for individual survival prediction was developed and graphically represented as a nomogram. The model showed favorable discrimination (C-index: 0.686), predictive accuracy [time dependent area under the curve (tAUC) at 5 years: 0.70], and calibration, and was further validated in the internal and external validation datasets. A risk stratification derived from the model stratified these patients into three prognostic subgroups with significantly different survival.

CONCLUSIONS

Low SMI accessed by MRI was significantly associated with poor overall survival in LANPC patients undergoing IC + CCRT. Moreover, we established and validated a novel nomogram involving age, TNM stage and SMI that could provide accurate prognostic stratification among this population.

Machine learning models and nomogram based on clinical, laboratory profiles and skeletal muscle index to predict pancreatic fistula after pancreatoduodenectomy

Background

Postoperative pancreatic fistula (POPF) is a perilous complication that may arise subsequent to pancreaticoduodenectomy (PD). In recent times, there has been an escalating interest in employing machine learning (ML) techniques to aid in treatment decision-making. The purpose of this research is to assess the effectiveness of ML in comparison to conventional models, while also conducting an initial evaluation of the predictive capability of skeletal muscle index (SMI) concerning POPF.

Methods

This retrospective observational study was carried out at The First Affiliated Hospital of Wenzhou Medical University from January 2012 to January 2021, encompassing data from 269 patients who underwent PD. After identifying independent factors associated with the condition, a logistic regression model was employed to construct a nomogram, alongside the establishment of five ML models. To assess their effectiveness, the best-performing ML model and nomogram were evaluated on a separate test group comprising 77 additional patients. The evaluation involved comparing the area under the curve (AUC) and Brier score.

Results

Among the 269 patients studied, the incidence of POPF was found to be 56.9%, with 106 patients (69.3%) experiencing clinically-relevant POPF. We identified six independent factors associated with POPF, including body mass index (BMI), SMI, pancreatic duct dilatation, tumor size, triglyceride levels, and the ratio of aspartate aminotransferase to alanine aminotransferase (AST/ALT) on the first postoperative day. When evaluated on the test set, the Gaussian Naive Bayes (GNB) model, which was the best-performing ML model, achieved an AUC of 0.824 and a Brier score of 0.175. The corresponding performance indicators for the nomogram were 0.844 for AUC and 0.165 for the Brier score.

Conclusions

This study found that there is minimal difference between ML and the nomogram based on logistic regression in predicting POPF. Additionally, SMI shows promise as a potential and practical tool for assessing the risk of POPF.

Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study

BACKGROUND

Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis.

AIM

To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics.

METHODS

This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was calculated for each patient using these important predictors.

RESULTS

Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05).

CONCLUSION

A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.

Pre-Operative Malnutrition in Patients with Ovarian Cancer: What Are the Clinical Implications? Results of a Prospective Study

BACKGROUND

Malnutrition was associated with worse survival outcomes, impaired quality of life, and deteriorated performance status across various cancer types. We aimed to identify risk factors for malnutrition in patients with epithelial ovarian cancer (EOC) and impact on survival.

METHODS

In our prospective observational monocentric study, we included the patients with primary and recurrent EOC, tubal or peritoneal cancer conducted. We assessed serum laboratory parameters, body mass index, nutritional risk index, nutritional risk screening score (NRS-2002), and bio-electrical impedance analysis.

RESULTS

We recruited a total of 152 patients. Patients > 65 years-old, with ascites of >500 mL, or with platinum-resistant EOC showed statistically significant increased risk of malnutrition when evaluated using NRS-2002 (p-values= 0.014, 0.001, and 0.007, respectively). NRS-2002 < 3 was an independent predictive factor for complete tumor resectability (p = 0.009). The patients with NRS-2002 ≥ 3 had a median overall survival (OS) of seven months (95% CI = 0-24 months), as compared to the patients with NRS-2002 < 3, where median OS was forty-six months (p = 0.001). A phase angle (PhAα) ≤ 4.5 was the strongest predictor of OS.

CONCLUSIONS

In our study, we found malnutrition to be an independent predictor of incomplete cytoreduction and independent prognostic factor for poor OS. Preoperative nutritional assessment is an effective tool in the identification of high-risk EOC groups characterized by poor clinical outcome.

Establishment and validation of novel nomograms to predict muscle quality in colorectal cancer patients

OBJECTIVES

The skeletal muscle mass index and skeletal muscle radiodensity have promise as specific diagnostic indicators for muscle quality. However, the difficulties in measuring low skeletal muscle mass index and low skeletal muscle radiodensity limit their use in routine clinical practice, impeding early screening and diagnosis. The objective of this study is to develop a nomogram that incorporates preoperative factors for predicting low skeletal muscle mass index and low skeletal muscle radiodensity.

METHODS

A total of 1692 colorectal cancer patients between 2015 and 2021 were included. The patients were randomly divided into a training cohort (n = 1353) and a validation cohort (n = 339). Nomogram models were calibrated using the area under the curve, calibration curves, and the Hosmer-Lemeshow test to assess their predictive ability. Finally, a decision curve was applied to assess the clinical usefulness.

RESULTS

In a prediction model for low skeletal muscle mass index, age, body mass index, and grip strength were incorporated as variables. For low skeletal muscle radiodensity, age, sex, body mass index, serum hemoglobin level, and grip strength were included as predictors. In the training cohort, the area under the curve value for low skeletal muscle mass index was 0.750 (95% CI, 0.726-0.773), whereas for low skeletal muscle radiodensity, it was 0.763 (95% CI, 0.739-0.785). The Hosmer-Lemeshow test confirmed that both models fit well in both cohorts. Decision curve analysis was applied to assess the clinical usefulness of the model.

CONCLUSIONS

The incorporation of preoperative factors into the nomogram-based prediction model represents a significant advancement in the muscle quality assessment. Its implementation has the potential to early screen patients at risk of low skeletal muscle mass index and low skeletal muscle radiodensity.

High intramuscular adipose tissue content associated with prognosis and postoperative complications of cancers

Sarcopenia has been considered an adverse prognostic factor in cancer patients. Intramuscular adipose tissue content, as a new marker of sarcopenia, can effectively reflect skeletal muscle quality. The aim of this study was performed to evaluate the association between high intramuscular adipose tissue content (IMAC) and survival outcomes and postoperative complications in cancer patients. Specific databases, including the Web of Science, Embase and Web of Science, were systematically searched to identify relevant articles evaluating the prognostic value of IMAC in cancer patients. Hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) were utilized for comprehensive analysis. All data analyses were performed using STATA 12.0 software. A total of 25 studies from 24 articles including 5663 patients were enrolled in the study. Meta-analysis showed that high IMAC was associated with unfavourable overall survival (OS) (HR: 2.21, 95% CI: 1.70-2.86, P < 0.001), relapse-free survival (RFS) (HR: 1.51, 95% CI: 1.30-1.75, P < 0.001) and disease-specific survival (DSS) (HR: 1.64, 95% CI: 1.19-2.28, P = 0.003). Subgroup analysis revealed that high IMAC remained an adverse prognostic factor when stratified by different country, treatment methods, cancer type or analysis type. High IMAC had better predictive value for gallbladder carcinoma (GBC) (HR: 3.50, 95% CI: 1.98-6.17, P < 0.001), hepatocellular carcinoma (HCC) (HR: 1.84, 95% CI: 1.45-2.33, P < 0.001), pancreatic cancer (PC) (HR: 2.11, 95% CI: 1.67-2.66, P < 0.001) and colorectal cancer (CRC) (HR: 2.54, 95% CI: 1.27-5.10, P = 0.009). High IMAC was also identified as a significant risk factor for postoperative complications (OR: 2.05, 95% CI: 1.22-3.46, P = 0.007). High IMAC was associated with an adverse prognosis and an increased risk of postoperative complications in cancer patients. IMAC may be a good indicator of sarcopenia.

Mechanisms of Ovarian Cancer-Associated Cachexia

Cancer-associated cachexia occurs in 50% to 80% of cancer patients and is responsible for 20% to 30% of cancer-related deaths. Cachexia limits survival and treatment outcomes, and is a major contributor to morbidity and mortality during cancer. Ovarian cancer is one of the leading causes of cancer-related deaths in women, and recent studies have begun to highlight the prevalence and clinical impact of cachexia in this population. Here, we review the existing understanding of cachexia pathophysiology and summarize relevant studies assessing ovarian cancer-associated cachexia in clinical and preclinical studies. In clinical studies, there is increased evidence that reduced skeletal muscle mass and quality associate with worse outcomes in subjects with ovarian cancer. Mouse models of ovarian cancer display cachexia, often characterized by muscle and fat wasting alongside inflammation, although they remain underexplored relative to other cachexia-associated cancer types. Certain soluble factors have been identified and successfully targeted in these models, providing novel therapeutic targets for mitigating cachexia during ovarian cancer. However, given the relatively low number of studies, the translational relevance of these findings is yet to be determined and requires more research. Overall, our current understanding of ovarian cancer-associated cachexia is insufficient and this review highlights the need for future research specifically aimed at exploring mechanisms of ovarian cancer-associated cachexia by using unbiased approaches and animal models representative of the clinical landscape of ovarian cancer.

Whole-Body Composition Features by Computed Tomography in Ovarian Cancer: Pilot Data on Survival Correlations

BACKGROUND

The primary objective of this study was to assess the associations of computed tomography (CT)-based whole-body composition values with overall survival (OS) and progression-free survival (PFS) in epithelial ovarian cancer (EOC) patients. The secondary objective was the association of body composition with chemotherapy-related toxicity.

METHODS

Thirty-four patients (median age 64.9 years; interquartile range 55.4-75.4) with EOC and thorax and abdomen CT scans were included. Clinical data recorded: age; weight; height; stage; chemotherapy-related toxicity; and date of last contact, progression and death. Automatic extraction of body composition values was performed by dedicated software. Sarcopenia was defined according to predefined cutoffs. Statistical analysis included univariate tests to investigate associations of sarcopenia and body composition with chemotoxicity. Association of body composition parameters and OS/PFS was evaluated by log-rank test and Cox proportional hazard model. Multivariate models were adjusted for FIGO stage and/or age at diagnosis.

RESULTS

We found significant associations of skeletal muscle volume with OS (p = 0.04) and PFS (p = 0.04); intramuscular fat volume with PFS (p = 0.03); and visceral adipose tissue, epicardial and paracardial fat with PFS (p = 0.04, 0.01 and 0.02, respectively). We found no significant associations between body composition parameters and chemotherapy-related toxicity.

CONCLUSIONS

In this exploratory study, we found significant associations of whole-body composition parameters with OS and PFS. These results open a window to the possibility to perform body composition profiling without approximate estimations.