TY - JOUR
T1 - Estimating the risk and benefit of radiation therapy in (y)pN1 stage breast cancer patients
T2 - A Bayesian network model incorporating expert knowledge (KROG 22–13)
AU - Division for Breast Cancer, Korean Radiation Oncology Group
AU - Jang, Bum Sup
AU - Chun, Seok Joo
AU - Choi, Hyeon Seok
AU - Chang, Ji Hyun
AU - Shin, Kyung Hwan
N1 - Publisher Copyright:
© 2024
PY - 2024/3
Y1 - 2024/3
N2 - Background: We aimed to evaluate the risk and benefit of (y)pN1 breast cancer patients in a Bayesian network model. Method: We developed a Bayesian network (BN) model comprising three parts: pretreatment, intervention, and risk/benefit. The pretreatment part consisted of clinical information from a tertiary medical center. The intervention part regarded the field of radiotherapy. The risk/benefit component encompasses radiotherapy (RT)-related side effects and effectiveness, including factors such as recurrence, cardiac toxicity, lymphedema, and radiation pneumonitis. These factors were evaluated in terms of disability weights and probabilities from a nationwide expert survey. The overall disease burden (ODB) was calculated as the sum of the probability multiplied by the disability weight. A higher value of ODB indicates a greater disease burden for the patient. Results: Among the 58 participants, a BN model utilizing discretization and clustering techniques revealed five distinct clusters. Overall, factors associated with breast reconstruction and RT exhibited high discrepancies (24–34 %), while RT-related side effects demonstrated low discrepancies (3–11 %) among the experts. When incorporating recurrence and RT-related side effects, the mean ODB of (y)pN1 patients was 0.258 (range, 0.244–0.337), with a higher tendency observed in triple-negative breast cancer (TNBC) or mastectomy cases. The ODB for TNBC patients undergoing mastectomy without postmastectomy radiotherapy was 0.327, whereas for non-TNBC patients undergoing breast conserving surgery with RT, the disease burden was 0.251. There was an increasing trend in ODB as the field of RT increased. Conclusion: We developed a Bayesian network model based on an expert survey, which helps to understand treatment patterns and enables precise estimations of RT-related risk and benefit in (y)pN1 patients.
AB - Background: We aimed to evaluate the risk and benefit of (y)pN1 breast cancer patients in a Bayesian network model. Method: We developed a Bayesian network (BN) model comprising three parts: pretreatment, intervention, and risk/benefit. The pretreatment part consisted of clinical information from a tertiary medical center. The intervention part regarded the field of radiotherapy. The risk/benefit component encompasses radiotherapy (RT)-related side effects and effectiveness, including factors such as recurrence, cardiac toxicity, lymphedema, and radiation pneumonitis. These factors were evaluated in terms of disability weights and probabilities from a nationwide expert survey. The overall disease burden (ODB) was calculated as the sum of the probability multiplied by the disability weight. A higher value of ODB indicates a greater disease burden for the patient. Results: Among the 58 participants, a BN model utilizing discretization and clustering techniques revealed five distinct clusters. Overall, factors associated with breast reconstruction and RT exhibited high discrepancies (24–34 %), while RT-related side effects demonstrated low discrepancies (3–11 %) among the experts. When incorporating recurrence and RT-related side effects, the mean ODB of (y)pN1 patients was 0.258 (range, 0.244–0.337), with a higher tendency observed in triple-negative breast cancer (TNBC) or mastectomy cases. The ODB for TNBC patients undergoing mastectomy without postmastectomy radiotherapy was 0.327, whereas for non-TNBC patients undergoing breast conserving surgery with RT, the disease burden was 0.251. There was an increasing trend in ODB as the field of RT increased. Conclusion: We developed a Bayesian network model based on an expert survey, which helps to understand treatment patterns and enables precise estimations of RT-related risk and benefit in (y)pN1 patients.
KW - Bayesian network
KW - Breast Cancer
KW - Disability weights
KW - Disease burden
KW - Expert knowledge
KW - Radiotherapy
KW - Survey
UR - http://www.scopus.com/inward/record.url?scp=85185004062&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2024.108049
DO - 10.1016/j.cmpb.2024.108049
M3 - Article
C2 - 38295597
AN - SCOPUS:85185004062
SN - 0169-2607
VL - 245
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 108049
ER -