融合多模态MRI提升前列腺癌严重性预测可信度
Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalities
- 用多模态MRI训练深度学习模型，模拟临床诊断流程
- 模型在验证集上表现优于单模态方法，提升预测可信度
- 适合医学AI可信性研究者及放射科医生参考
在美国，前列腺癌是男性第二大死亡原因，预计2024年导致35,250人死亡。然而，多数诊断为非致命性，临床意义不大，患者一生中可能不受影响。因此，大量研究探索基于磁共振成像（MRI）模态和深度神经网络预测前列腺癌临床显著性的准确性。尽管性能优异，这些模型仍难以获得临床科学家信任，因它们仅基于单一模态训练，而临床实践中常结合多种MRI模态。本文研究通过整合多模态MRI训练深度学习模型，以增强对临床显著性前列腺癌预测的信任。其优秀性能与提出的训练流程展示了多模态融合在提升可信度与准确率方面的优势。
原文摘要 · Abstract (English)
In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024. However, most diagnoses are non-lethal and deemed clinically insignificant which means that the patient will likely not be impacted by the cancer over their lifetime. As a result, numerous research studies have explored the accuracy of predicting clinical significance of prostate cancer based on magnetic resonance imaging (MRI) modalities and deep neural networks. Despite their high performance, these models are not trusted by most clinical scientists as they are trained solely on a single modality whereas clinical scientists often use multiple magnetic resonance imaging modalities during their diagnosis. In this paper, we investigate combining multiple MRI modalities to train a deep learning model to enhance trust in the models for clinically significant prostate cancer prediction. The promising performance and proposed training pipeline showcase the benefits of incorporating multiple MRI modalities for enhanced trust and accuracy.
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