IntroductionPartial breast irradiation (PBI) has increased in utilization, with the postoperative lumpectomy cavity and clips used to guide target volumes. The ideal timing to perform computed tomography (CT)–based treatment planning for this technique is unclear. Prior studies have examined change in volume over time from surgery but not the effect of patient characteristics on lumpectomy cavity volume. We sought to investigate patient and clinical factors that may contribute to larger postsurgical lumpectomy cavities and therefore predict for larger PBI volumes.MethodsA total of 351 consecutive women with invasive orin situbreast cancer underwent planning CT after breast-conserving surgery at a single institution during 2019 and 2020. Lumpectomy cavities were contoured, and volume was retrospectively computed using the treatment planning system. Univariate and multivariate analyses were performed to evaluate the associations between lumpectomy cavity volume and patient and clinical factors.ResultsMedian age was 61.0 years (range, 30–91), 23.9% of patients were Black people, 52.1% had hypertension, the median body mass index (BMI) was 30.4 kg/m², 11.4% received neoadjuvant chemotherapy, 32.5% were treated prone, mean interval from surgery to CT simulation was 54.1 days ± 45.9, and mean lumpectomy cavity volume was 42.2 cm3± 52.0. Longer interval from surgery was significantly associated with smaller lumpectomy cavity volume on univariate analysis, p = 0.048. Race, hypertension, BMI, the receipt of neoadjuvant chemotherapy, and prone position remained significant on multivariate analysis (p < 0.05 for all). Prone position vs. supine, higher BMI, the receipt of neoadjuvant chemotherapy, the presence of hypertension, and race (Black people vs. White people) were associated with larger mean lumpectomy cavity volume.DiscussionThese data may be used to select patients for which longer time to simulation may result in smaller lumpectomy cavity volumes and therefore smaller PBI target volumes. Racial disparity in cavity size is not explained by known confounders and may reflect unmeasured systemic determinants of health. Larger datasets and prospective evaluation would be ideal to confirm these hypotheses.