Jang Bum-Sup, Chun Seok-Joo, Choi Hyeon Seok, Chang Ji Hyun, Shin Kyung Hwan
Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea; Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea.
Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea.
Comput Methods Programs Biomed. 2024 Mar;245:108049. doi: 10.1016/j.cmpb.2024.108049. Epub 2024 Jan 24.
We aimed to evaluate the risk and benefit of (y)pN1 breast cancer patients in a Bayesian network model.
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.
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.
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.
我们旨在通过贝叶斯网络模型评估（y）pN1期乳腺癌患者的风险和获益。
我们开发了一个包含三个部分的贝叶斯网络（BN）模型：预处理、干预以及风险/获益。预处理部分包含来自一家三级医疗中心的临床信息。干预部分涉及放射治疗领域。风险/获益部分涵盖放疗（RT）相关的副作用和疗效，包括复发、心脏毒性、淋巴水肿和放射性肺炎等因素。这些因素通过全国性专家调查的残疾权重和概率进行评估。总体疾病负担（ODB）通过概率乘以残疾权重的总和来计算。ODB值越高表明患者的疾病负担越大。
在58名参与者中，一个利用离散化和聚类技术的BN模型揭示了五个不同的聚类。总体而言，与乳房重建和放疗相关的因素在专家之间显示出较高的差异（24 - 34%），而放疗相关的副作用在专家之间显示出较低的差异（3 - 11%）。当纳入复发和放疗相关的副作用时，（y）pN1患者的平均ODB为0.258（范围为0.244 - 0.337），在三阴性乳腺癌（TNBC）或乳房切除术病例中观察到更高的趋势。未进行乳房切除术后放疗的TNBC患者接受乳房切除术的ODB为0.327，而接受保乳手术加放疗的非TNBC患者的疾病负担为0.251。随着放疗范围的增加，ODB呈上升趋势。
我们基于专家调查开发了一个贝叶斯网络模型，该模型有助于理解治疗模式，并能够精确估计（y）pN1患者放疗相关的风险和获益。
