Key result
The TPNET model predicted the incidence of cancer therapy-related cardiac dysfunction over 24 months with an AUC of 0.83 and sensitivity of 0.88, demonstrating greater robustness than existing models.
Why the study?
Cancer therapy-related cardiac dysfunction is a potential complication of cancer treatment requiring cardiac monitoring, motivating the evaluation of tissue Doppler imaging combined with deep learning to predict its occurrence.
Does a temporal-multimodal pattern network (TPNET) using TDI and clinical data accurately predict the incidence of CTRCD in cancer patients?
Observational (n=270)
Does a temporal-multimodal pattern network (TPNET) using TDI and clinical data accurately predict the incidence of CTRCD in cancer patients?
A novel deep learning model combining Tissue Doppler imaging and clinical data demonstrated good predictive performance for cancer therapy-related cardiac dysfunction.
Cancer therapy-related cardiac dysfunction (CTRCD) is a potential complication associated with cancer treatment, particularly in patients with breast cancer, requiring monitoring of cardiac health during the treatment process. Tissue Doppler imaging (TDI) is a remarkable technique that can provide a comprehensive reflection of the left ventricle's physiological status. We hypothesized that the combination of TDI features with deep learning techniques could be utilized to predict CTRCD. To evaluate the hypothesis, we developed a temporal-multimodal pattern network for efficient training (TPNET) model to predict the incidence of CTRCD over a 24-month period based on TDI, function, and clinical data from 270 patients. Our model achieved an area under curve (AUC) of 0.83 and sensitivity of 0.88, demonstrating greater robustness compared to other existing visual models. To further translate our model's findings into practical applications, we utilized the integrated gradients (IG) attribution to perform a detailed evaluation of all the features. This analysis has identified key pathogenic signs that may have remained unnoticed, providing a viable option for implementing our model in preoperative breast cancer patients. Additionally, our findings demonstrate the potential of TPNET in discovering new causative agents for CTRCD.
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Zhang et al. (2025) conducted an observational in Cancer therapy-related cardiac dysfunction (CTRCD) (n=270). TPNET model using TDI, function, and clinical data vs. other existing visual models was evaluated on Prediction of CTRCD incidence (AUC 0.83, sensitivity 0.88). The TPNET model predicted the incidence of cancer therapy-related cardiac dysfunction over 24 months with an AUC of 0.83 and sensitivity of 0.88, demonstrating greater robustness than existing models.
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