Background: In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs.Methods: We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009–2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed.Results:The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient’s risk.Conclusions:In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors.
背景：近几十年来，青少年与年轻成人（AYA）乳腺癌（BC）幸存者数量的增加凸显了癌症疗法的心脏毒性作用，使得心血管疾病（CVDs）成为最常见但相对罕见的长期后遗症之一。借助创新的人工智能（AI）工具和真实世界数据（RWD），我们旨在开发一个因果可解释模型，以识别有心血管疾病风险的年轻乳腺癌幸存者。 方法：我们设计并训练了一个贝叶斯网络（BN）——一种人工智能模型，该模型结合了专家知识以及基于人群（1036名患者）和临床（339名患者）队列的数据。这些数据来自2009年至2019年间确诊的、年龄在18至39岁的女性AYA乳腺癌一年幸存者。BN模型的性能通过标准分类指标进行了验证，并提出了两种临床应用方案。 结果：该模型展现出优异的分类性能和清晰的因果语义。根据模型的预测，重点关注心血管疾病风险最高的25% AYA乳腺癌幸存者，我们可以识别出81%实际会发展为心血管疾病的患者。此外，我们还开发了一款桌面应用程序，用于计算个体患者的风险。 结论：在本研究中，我们开发了首个用于预测AYA乳腺癌幸存者心血管疾病风险的因果模型，并提出了一种创新的AI方法，该方法可能对所有处理真实世界数据的研究人员都有帮助。该模型对于旨在为AYA乳腺癌幸存者规划个性化随访策略的临床医生至关重要。
肿瘤(癌症)患者之家