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

Abstract

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.

Keywords: SHapley Additive exPlanations; machine learning; muscle loss; ovarian cancer.

Figure 1

Receiver operating characteristic curves showing the muscle loss predictive performance of the machine learning models in (A, B) the training and test datasets and (C) external validation dataset. K‐NN, k‐nearest neighbour; LR, logistic regression; RF, random forest; SVM, support vector machine; XGBoost, extreme gradient boosting.

Figure 2

(A) SHapley Additive exPlanations (SHAP) feature importance shown according to the mean absolute SHAP value of each feature. (B) SHAP summary plot showing the distribution of the SHAP values of each feature. Each dot represents a SHAP value for a feature per patient. The x axis represents the SHAP value, and the colour varying from red to blue represents the feature value from high to low, respectively. BMI, body mass index; ECOG, Eastern Cooperative Oncology Group; FIGO, International Federation of Gynecology and Obstetrics; NLR, neutrophil‐to‐lymphocyte ratio; PLR, platelet‐to‐lymphocyte ratio.

Figure 3

SHapley Additive exPlanations (SHAP) dependence plots of model features: (A) albumin change, (B) body mass index (BMI) change, (C) neutrophil‐to‐lymphocyte ratio (NLR) change, and (D) platelet‐to‐lymphocyte ratio (PLR) change. The y axis represents the SHAP values of features, and the values of certain features are shown in the x axis. Each dot represents a SHAP value for a feature per patient, and colour from red to blue represents the feature's value from high to low. SHAP values for specific features exceeding zero represent an increased risk of muscle loss development.

Figure 4

SHapley Additive exPlanations (SHAP) force plot of (A) Patient A (true positive) and (B) Patient B (true negative). The colour represents the contributions of each feature, with red being positive and blue being negative. The length of the colour bar represents the contribution strength. BMI, body mass index; ECOG, Eastern Cooperative Oncology Group; NLR, neutrophil‐to‐lymphocyte ratio; PLR, platelet‐to‐lymphocyte ratio.