Estimating the risk and benefit of radiation therapy in (y)pN1 stage breast cancer patients: A Bayesian network model incorporating expert knowledge (KROG 22-13)

Bum-Sup Jang; Seok-Joo Chun; Hyeon Seok Choi; Ji Hyun Chang; Kyung Hwan Shin; Division for Breast Cancer, Korean Radiation Oncology Group

doi:10.1016/j.cmpb.2024.108049

Estimating the risk and benefit of radiation therapy in (y)pN1 stage breast cancer patients: A Bayesian network model incorporating expert knowledge (KROG 22-13)

Comput Methods Programs Biomed. 2024 Mar:245:108049. doi: 10.1016/j.cmpb.2024.108049. Epub 2024 Jan 24.

Authors

Bum-Sup Jang¹, Seok-Joo Chun², Hyeon Seok Choi², Ji Hyun Chang¹, Kyung Hwan Shin³; Division for Breast Cancer, Korean Radiation Oncology Group

Affiliations

¹ Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea.
² Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea.
³ Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, South Korea. Electronic address: radiat@snu.ac.kr.

PMID: 38295597
DOI: 10.1016/j.cmpb.2024.108049

Abstract

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.

Keywords: Bayesian network; Breast Cancer; Disability weights; Disease burden; Expert knowledge; Radiotherapy; Survey.

MeSH terms

Bayes Theorem
Breast Neoplasms* / pathology
Breast Neoplasms* / radiotherapy
Female
Humans
Mastectomy / methods
Neoplasm Staging
Radiotherapy, Adjuvant / methods
Triple Negative Breast Neoplasms* / pathology
Triple Negative Breast Neoplasms* / radiotherapy
Triple Negative Breast Neoplasms* / surgery