The impact of body composition on treatment in ovarian cancer: a current insight

McSharry, Veronica; Glennon, Kate; Mullee, Amy; Brennan, Donal J.

doi:10.1080/17512433.2021.1937125

Cited by 15 publications

(14 citation statements)

References 109 publications

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“…This study investigated the effects of resistance exercise in advanced-stage OC survivors. We found that participation in a resistance exercise program shortly after the completion of first-line treatment can effectively improve muscle morphology and muscle function, both considered potentially prognostic for treatment and survival outcomes [ 7 – 11 ]. Despite homogeneity in terms of disease and treatment stage, our sample of women was diverse in terms of age, BMI, comorbidities, and physical functioning.…”

Section: Discussionmentioning

confidence: 99%

“…Both suboptimal muscle morphology and muscle function (i.e., low muscle strength and physical function) are associated with poor treatment and survival outcomes [ 7 – 9 ]. In OC, suboptimal muscle morphology is highly prevalent and correlates with more post-operative complications and chemotherapy toxicity [ 10 ] and increased mortality [ 11 ]. In a cross-sectional study conducted by our group, we observed significantly lower physical function and upper body strength in post-treatment OC survivors compared with age-matched controls [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Supervised resistance exercise for women with ovarian cancer who have completed first-line treatment: a pragmatic study

Schofield

Newton

Taaffe

et al. 2023

Support Care Cancer

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Objectives In ovarian cancer (OC), suboptimal muscle morphology (i.e., low muscle mass and density) is associated with poor clinical outcomes, yet little is known about the effect of interventions aimed at improving these measures. We investigated the effect of resistance exercise after first-line treatment on muscle mass and density, muscle strength and physical function, health-related quality of life (QoL), and pelvic-floor function in advanced-stage OC survivors. Methods Fifteen OC survivors participated in supervised resistance exercise twice weekly for 12 weeks (in-clinic or by telehealth). Assessments included muscle mass and density (dual-energy X-ray absorptiometry, peripheral quantitative computed tomography), muscle strength (1-repetition maximum [1RM] chest press, 5RM leg press, handgrip strength), physical function (400-m walk, timed up-and-go [TUG]), QoL (QLQ-C30 questionnaire), and self-reported pelvic floor function (Australian Pelvic Floor Questionnaire). Results The median age was 64 (range 33–72) years, 10 women underwent neoadjuvant chemotherapy and five underwent adjuvant chemotherapy. All participants completed the intervention (median attendance = 92%; range 79–100%). Post-intervention improvements were observed for whole-body lean mass (1.0 ± 1.4 kg, p = 0.015), appendicular lean mass (0.6 ± 0.9 kg, p = 0.013), muscle density (p = 0.011), upper and lower body strength (p ≤ 0.001), 400-m walk (p = 0.001), TUG (p = 0.005), and social and cognitive QoL domains (p = 0.002 and 0.007), with no change to pelvic floor symptoms (p > 0.05). Conclusion In this study, supervised resistance exercise effectively improved muscle mass and density, muscle strength, and physical functioning without deleterious effects on the pelvic floor. Considering the prognostic value of these outcomes, larger studies are needed to confirm the benefits of resistance exercise in OC supportive care.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised resistance exercise for women with ovarian cancer who have completed first-line treatment: a pragmatic study

Schofield

Newton

Taaffe

et al. 2023

Support Care Cancer

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“…However, skeletal muscle loss occurs during treatment and compromises the survival outcomes. [2][3][4][5][6][7] Muscle loss can be overlooked and may not be identified by changes in body weight. 5 Therefore, muscles in these patients should be assessed to identify loss and target interventions to preserve and recover muscle.…”

Section: Introductionmentioning

confidence: 99%

“…8 Previous studies have revealed the usefulness of CT-based muscle measurements in patients with ovarian cancer. [3][4][5][6] However, the utility of such measurements may be limited in clinical practice because the required process is labour-intensive and time-consuming. 9 To assess changes in muscle, pre-and post-treatment CT scans should be processed by specific software, and the muscle is manually or semi-automatically segmented on a cross-sectional image at L3 to calculate the area of skeletal muscle.…”

Section: Introductionmentioning

confidence: 99%

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

Hsu

Weng

et al. 2023

J cachexia sarcopenia muscle

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BackgroundSkeletal 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.MethodsThis 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.ResultsThe 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.ConclusionsExplainable 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.

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“…Because it is defined by a decrease in skeletal muscle mass, the skeletal muscle index (SMI) is useful for the evaluation of sarcopenia ( 13 ). The methods of measuring the SMI include measuring the cross-sectional area of the muscle by examining images, such as computed tomography (CT) and magnetic resonance imaging (MRI); bioelectrical impedance analysis (BIA); and dual-energy X-ray absorptiometry (DEXA) ( 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

Prognostic significance of sarcopenia as determined by bioelectrical impedance analysis in patients with advanced pancreatic cancer receiving gemcitabine plus nab‑paclitaxel: A retrospective study

et al. 2022

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Sarcopenia often affects patients with various types of cancer, and has been reported to affect patient prognosis and therapeutic effects. However, to the best of our knowledge, there are no reports on the relationship between gemcitabine plus nab-paclitaxel combination therapy (GnP) and sarcopenia in patients with unresectable pancreatic cancer. The present study analyzed the relationship between overall survival (OS), progression-free survival (PFS), response rate, disease control rate, adverse events (AEs) and sarcopenia in patients with pancreatic cancer treated with GnP. A total of 121 consecutive patients with advanced pancreatic cancer who received GnP as first-line chemotherapy between January 2015 and December 2017 were retrospectively analyzed. GnP consisted of 1,000 mg/m 2 gemcitabine and 125 mg/m 2 nab-paclitaxel, which were administered on days 1, 8 and 15 every 4 weeks. The skeletal muscle index (SMI) was calculated using bioimpedance analysis (BIA) as an index of sarcopenia prior to GnP. The patients were divided into sarcopenia (n=41) and non-sarcopenia (n=80) groups using cutoff values of 8.87 and 6.42 kg/m 2 for male and female patients, respectively. The sarcopenia and non-sarcopenia groups had a median OS of 8.1 and 13.9 months, respectively [hazard ratio (HR) 0.79; 95% confidence interval (CI) 0.53-1.20], and a median PFS of 4.3 and 6.3 months, respectively (HR 0.63; 95% CI 0.42-0.95). The response and disease controls rate were not statistically different between the groups (20 vs. 32%, P=0.20; 81 vs. 80%, P=1.0). In addition, comparison of common grade 3 and 4 AEs between the two groups revealed no statistically significant differences. In conclusion, the results of the present study indicated that SMI obtained by BIA may be a predictor of treatment response and prognosis in patients with advanced pancreatic cancer who undergo GnP.

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The impact of body composition on treatment in ovarian cancer: a current insight

Cited by 15 publications

References 109 publications

Supervised resistance exercise for women with ovarian cancer who have completed first-line treatment: a pragmatic study

Supervised resistance exercise for women with ovarian cancer who have completed first-line treatment: a pragmatic study

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

Prognostic significance of sarcopenia as determined by bioelectrical impedance analysis in patients with advanced pancreatic cancer receiving gemcitabine plus nab‑paclitaxel: A retrospective study

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