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.

Purpose: This study investigates the association between body composition change during concurrent chemoradiotherapy (CCRT) and outcome in patients with locally advanced cervical cancer (LACC). Experimental Design: Pre- and post-treatment computed tomography (CT) images of 245 patients with LACC who were treated between 2004 and 2015 were analysed. Skeletal muscle index (SMI) and density (SMD), subcutaneous adipose tissue index (SATI), and visceral adipose tissue index (VATI) were measured from two sets of CT images at the level of the L3 vertebra. Sarcopenia and a low SMD were defined using published cut-points. Predictors of overall survival (OS) and cancer-specific survival (CSS) were analysed using Cox regression models. Results: The median follow-up was 62.7 (range, 7.3-152.3) months. Among the 245 patients, 127 (51.8%) had pre-treatment sarcopenia and 154 (62.9%) had a low SMD. SMI did not decrease significantly during CCRT, 0.6%/150 days (95% CI: -1.8 to 0.6; p = 0.35). However, a SMI loss during CCRT of >10.0%/150 days was independently associated with poorer OS (hazard ratio: 6.02, 95% confidence interval: 3.04-11.93; p < 0.001) and CSS (hazard ratio: 3.49, 95% confidence interval: 1.44-8.42; p = 0.006) when adjusted for FIGO stage, pathology, and treatment. Pre-treatment sarcopenia and change of SMD, SATI, and VATI during CCRT were not associated with survival. Conclusions: Skeletal muscle measurements could be imaging biomarkers to predict outcomes for patients with LACC in clinical practice. Further studies are needed to determine whether multimodal interventions can preserve skeletal muscle mass and thereby improve survival.

Background: Sarcopenia is commonly observed in patients with advanced‐stage epithelial ovarian cancer (EOC). However, the effect of body composition changes—during primary debulking surgery (PDS) and adjuvant platinum‐based chemotherapy—on outcomes of patients with advanced‐stage EOC is unknown. This study aimed to evaluate the association between body composition changes and outcomes of patients with stage III EOC treated with PDS and adjuvant platinum‐based chemotherapy.Methods: Pre‐treatment and post‐treatment computed tomography (CT) images of 139 patients with stage III EOC were analysed. All CT images were contrast‐enhanced scans and were acquired according to a standardized protocol. The skeletal muscle index (SMI), skeletal muscle radiodensity (SMD), and total adipose tissue index were measured using CT images obtained at the L3 vertebral level. Predictors of overall survival were identified using Cox regression models.Results: The median follow‐up was 37.9 months. The median duration between pre‐treatment and post‐treatment CT was 182 days (interquartile range: 161–225 days). Patients experienced an average SMI loss of 1.8%/180 days (95% confidence interval: −3.1 to −0.4; P = 0.01) and SMD loss of 1.7%/180 days (95% confidence interval: −3.3 to −0.03; P = 0.046). SMI and SMD changes were weakly correlated with body mass index changes (Spearman ρ for SMI, 0.15, P = 0.07; ρ for SMD, 0.02, P = 0.82). The modified Glasgow prognostic score was associated with SMI loss (odds ratio: 2.42, 95% confidence interval: 1.03–5.69; P = 0.04). The median time to disease recurrence was significantly shorter in patients with SMI loss ≥5% after treatment than in those with SMI loss <5% or gain (5.4 vs. 11.2 months, P = 0.01). Pre‐treatment SMI (1 cm²/m² decrease; hazard ratio: 1.08, 95% confidence interval: 1.03–1.11; P = 0.002) and SMI change (1%/180 days decrease; hazard ratio: 1.04, 95% confidence interval: 1.01–1.08; P = 0.002) were independently associated with poorer overall survival. SMD, body mass index, and total adipose tissue index at baseline and changes were not associated with overall survival.Conclusions: Skeletal muscle index decreased significantly during treatment and was independently associated with poor overall survival in patients with stage III EOC treated with PDS and adjuvant platinum‐based chemotherapy. The modified Glasgow prognostic score might be a predictor of SMI loss during treatment.

Background: Treatment‐related toxicities and decreased levels of patient performance during cancer therapy might contribute to body composition changes and thereby impact outcomes. However, the effect of longitudinal body composition changes on outcomes in patients with advanced endometrial cancer is unknown. This study investigated the association between body composition changes during staging surgery and adjuvant chemoradiotherapy and outcomes in patients with stage III endometrial cancer.Methods: Pretreatment and post‐treatment computed tomography (CT) images of 131 patients with stage III endometrial cancer who were treated between 2008 and 2016 were analysed. All CT images were contrast enhanced and acquired according to the standardized protocol. The skeletal muscle index (SMI), skeletal muscle radiodensity (SMD), and total adipose tissue index were measured from two sets of CT images obtained at the level of the third lumbar vertebra. The skeletal muscle gauge was calculated by multiplying SMI by SMD (SMI × SMD). Predictors of overall survival and progression‐free survival were identified using Cox regression models.Results: The median follow‐up was 50.6 (range 12.1–117.0) months. Overall, body mass index (BMI) changes during treatment were 0.4% per 210 days (95% confidence interval: −0.6 to 1.4; P = 0.41), and patients experienced an average SMD loss of 2.1% per 210 days (95% confidence interval: −4.0 to −0.2; P = 0.03). Weight loss and SMD loss ≥5% were observed in 23 (17.6%) and 54 (41.2%) patients, respectively. The changes in SMD did not correlate with those in BMI (Spearman's ρ for SMD, −0.13; P = 0.13). SMD change (per 1 Hounsfield unit/210 days decrease) was independently associated with poorer overall survival (hazard ratio: 1.32, 95% confidence interval: 1.14–1.52; P < 0.001) and progression‐free survival (hazard ratio: 1.28, 95% confidence interval: 1.12–1.43; P < 0.001). Our results did not show association between survival and pretreatment myosteatosis and sarcopenia or changes in SMI and total adipose tissue index during treatment. The pretreatment skeletal muscle gauge was associated with treatment modifications such as delays, dose reductions, and discontinuation of chemotherapy.Conclusions: Skeletal muscle radiodensity decreased significantly during treatment and was independently associated with poorer survival in patients with stage III endometrial cancer who underwent staging surgery and adjuvant chemoradiotherapy. SMD loss was occult and occurred independently of BMI change.