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 cm3and < 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.
目的：本单中心回顾性研究旨在评估基于计算机断层扫描（CT）的全身成分指标与接受聚腺苷二磷酸核糖聚合酶（PARP）抑制剂治疗的卵巢上皮癌患者剂量调整之间的关联。 方法：研究纳入48例卵巢上皮癌患者（中位年龄61岁，四分位距53-68.5岁），所有患者在开始PARP抑制剂治疗前均接受胸腹部CT扫描。记录的临床数据包括年龄、体重、身高、分期、PARP抑制剂起始与终止日期、剂量调整情况、提前终止治疗情况、末次随访日期、疾病进展及死亡信息。身体成分指标通过专用软件自动提取。鉴于研究的探索性质，统计分析结合了单变量评估（单变量逻辑回归）与分类树方法——后者是同时将所有变量作为协变量的数据驱动机器学习方法。这种整合策略旨在确定与毒性风险增加相关的身体成分特征的实证临界值。 结果：单变量逻辑回归显示身体成分变量对剂量调整概率无统计学显著影响。由于变量关系的复杂性，采用分类树的机器学习方法显示骨骼肌是唯一与剂量调整显著相关的身体成分变量。具体而言，骨骼肌值在≥7506 cm³且<8650 cm³范围内时，剂量调整风险显著增高（p=0.0118）。 结论：本探索性研究发现，在骨骼肌值处于7506 cm³≤SKM<8650 cm³区间的患者中，全身成分参数（骨骼肌）与剂量调整存在显著关联。若在更大规模队列中得到验证，这些发现可帮助临床医生识别可能从初始减量PARP抑制剂治疗中获益的患者群体。
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