基于治疗前心脏磁共振图像的机器学习模型预测癌症治疗相关心功能障碍
研究纳入3项前瞻性研究中的229例接受蒽环类和曲妥珠单抗治疗的HER2+乳腺癌女性，利用治疗前CMR短轴电影图像构建深度学习模型预测CTRCD。内部验证AUC 0.85，外部验证AUC 0.80，均优于HFA-ICOS风险评分、传统CMR参数及超声参数模型。
要点
- 治疗前CMR短轴电影图像可用于预测后续CTRCD，外部验证AUC达0.80。
- 深度学习模型预测效能优于HFA-ICOS风险评分（AUC 0.66）和手动影像参数。
- CTRCD定义基于CMR，随访包括蒽环类后及每三个月超声和CMR复查。
局限性
- 样本量有限（共229例，外部验证53例）。
- 仅纳入HER2+乳腺癌女性，结论外推需谨慎。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
BACKGROUND: Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging.
OBJECTIVES: The purpose of this study was to assess if deep learning (DL) approaches using cardiac magnetic resonance (CMR) images before cancer therapy can predict subsequent CTRCD and compare them with clinical and conventional imaging models.
METHODS: Women with HER2+ breast cancer receiving anthracyclines and trastuzumab from 3 prospective studies (Toronto, Canada: EMBRACE-MRI, SPARE-HF; and Calgary, Canada: CIROC) were included. Patients were assessed before cancer therapy, after anthracycline, and trimonthly with repeated echocardiography and CMR. CTRCD was defined according to CMR. The authors calculated the HFA-ICOS risk score and measured CMR and echocardiography volumetric and functional parameters. Deep convolutional neural network architectures were used with pre-cancer therapy CMR short-axis cine images to develop image-based DL models to predict CTRCD. The Toronto patients were used for model derivation and internal validation; the Calgary patients were used for external validation.
RESULTS: A total of 229 patients (mean age 50.4 ± 9.7 years) were included: 176 in the internal (52 CTRCD events) development cohort and 53 in the external data set (14 CTRCD events). Our pre-cancer therapy CMR DL model demonstrated an AUC of 0.85 (95% CI: 0.69-0.97) and F1 score 0.69 (95% CI: 0.47-0.86) to predict CTRCD. On external validation, the DL model had an AUC of 0.80 (95% CI: 0.66-0.92) and F1 score of 0.55 (95% CI: 0.24-0.77). In comparison, the best-performing models using HFA-ICOS risk score, CMR, and echocardiographic parameters demonstrated, respectively, AUCs of 0.66 (95% CI: 0.53-0.75), 0.59 (95% CI: 0.41-0.73), 0.62 (95% CI: 0.44-0.78) and F1 scores of 0.56 (95% CI: 0.47-0.61), 0.20 (95% CI: 0.00-0.42) and 0.36 (95% CI: 0.12-0.62) to predict CTRCD.
CONCLUSIONS: A DL model using pre-cancer therapy CMR short-axis cines can predict future CTRCD risk better than clinical or manually quantified imaging models.
引用
- Yu C, Peikari M, Labib D, et al. Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction. JACC Cardiovasc Imaging. 2026. PMID: 42212994. doi:10.1016/j.jcmg.2026.04.007
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。