A Retrospective Assessment of Computed Tomography-Based Body Composition and Toxicity in Ovarian Cancer Patients Treated with PARP Inhibitors

TL;DR

This study explores how body composition, measured via CT scans, relates to toxicity in ovarian cancer patients taking PARP inhibitors, finding a link between skeletal muscle volume and dose reductions.

Contribution

The study is the first to explore the association between CT-based body composition and PARP inhibitor toxicity in ovarian cancer patients.

Findings

Skeletal muscle volume (SKM) was the only body composition variable significantly associated with dose reduction.

Patients with SKM values between 7506 cm³ and 8650 cm³ had a higher risk of dose reduction.

No other body composition variables showed a statistically significant effect on dose reduction.

Abstract

Poly ADP-ribose polymerase (PARP) inhibitors (PARPi) are a well-established maintenance therapy in stage III and IV ovarian cancer, with evidence of efficacy, especially in patients with germline and/or somatic pathogenic variants (PVs) in the BRCA1/2 genes and in patients with homologous recombination deficiency. While the toxicity profile of PARPi—often leading to dose reductions—is well characterized in both clinical trials and real-world settings, the potential link between drug toxicity and body composition parameters remains unexplored. This exploratory study aims to investigate that potential association, with the goal of identifying a specific patient profile more susceptible to treatment-related toxicity.

Objectives: The objective of this single-site retrospective study was to assess the association between Computed Tomography (CT)-based whole-body composition values with dose reduction in patients with a diagnosis of epithelial ovarian cancer (EOC) treated with poly ADP-ribose polymerase (PARP) inhibitors (PARPi). Methods: Forty-eight patients (median age 61 years; interquartile range 53–68.5) with EOC who had a thorax and abdomen CT scan (performed before starting PARPi) were enrolled. Recorded clinical data included age, weight, height, stage, start and end date of PARPi, dose reduction, premature discontinuation of therapy, date of last contact, progression, and death. Body composition values were automatically extracted by dedicated software. Given the exploratory nature of the study, the statistical analysis combined univariate assessments (univariate logistic regression) used to evaluate the individual effect of each variable on the probability of dose reduction, with a classification tree approach—a data-driven machine learning method considering all variables simultaneously as covariates. This integrated strategy was designed to identify empirical cut-offs defining body composition profiles associated with increased risk of toxicity. Results: Univariate logistic regression showed no statistically significant effect of body composition variables on the probability of dose reduction. Due to the complexity of variable relations, a machine-learning approach with a classification tree showed that SKM (skeletal muscle) was the sole body composition variable significantly associated with dose reduction. Specifically, there was a higher risk of dose reduction with SKM values ≥ 7506 cm3 and < 8650 cm3 (p = 0.0118). Conclusions: In this exploratory study, a significant association of whole-body composition parameters (SKM) with dose reduction was observed in patients with a 7506 cm3 ≤ SKM < 8650 cm3. If confirmed in larger cohorts, these findings could help clinicians identify patients who might benefit from an upfront reduced PARPi dose.

Figures