A simulation framework for modeling tumor control probability in breast conserving therapy

The effect of age in breast conserving therapy: A retrospective analysis on pathology and clinical outcome data

“…In previous work [10], the total MSD quantity was considered as a function of age at diagnosis, tumor grade and tumor diameter, and the function parameters were estimated using the pathology dataset. In the current study, the effect of age on treatment outcome was the major focus; therefore, we only chose age to establish a link with the patient's disease quantity.…”

“…In order to study the effect of patient age on the outcome of breast conserving therapy (BCT), we deployed a retrospective analysis of patients in two different age groups: patients at age 50 years or below, and patients older than 50 years [3] on the treatment local control was analyzed by tracking the change in microscopic disease (MSD) quantity around the primary tumor after each treatment component through an earlier developed simulation framework [10]. In short, this framework contains three sequential stages: disease load prediction, surgery simulation and radiotherapy (RT) modeling.…”

“…To model disease quantity as a function of age, a Zero-Inflated Model was employed [10]. To estimate disease cell density, we selected 36 pathology slides from the five youngest patients and the seven oldest patients and analyzed the difference between these two groups.…”

“…We compared the distribution of the negative surgical margins in the two age groups in the EORTC dataset using the Kolmogorov-Smirnov test and incorporated this difference in excision volumes between the two age groups within our surgery simulation module. As reported previously [10], the removed tissue around the primary tumor in the MARGINS dataset was estimated using the surgical margins in six directions. The sum of surgical margins minus minimal surgical resection margin was modeled with a Poisson distribution.…”

“…For this purpose we employed a previously developed treatment simulation framework [10] to model the treatment outcome. We aimed specifically at answering three questions:…”