Zhuang Luoting, Yadav Anil, Kim Grace H, Mohammad Hossein Tabatabaei Seyed, Prosper Ashley, Hsu William
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-5. doi: 10.1109/EMBC53108.2024.10781833.
Image-based risk models have the potential to aid in the identification of individuals who would benefit from cancer screening. However, models need to be robust against variations in image acquisition and reconstruction parameters, which alter the appearance of images and may lead to different downstream predictions. We evaluated Sybil, an imaging-based lung cancer model that predicts up to six-year risk, on a lung cancer screening dataset that was acquired and reconstructed using a range of parameters. Using raw projection data from 169 retrospectively acquired low-dose computed tomography (LDCT) scans, we generated six image conditions for each case, varying reconstruction kernels (smooth, medium, sharp) and slice thicknesses (1.0mm, 2.0mm). Each image condition was processed and run through the pre-trained Sybil model in the same way. Variations in predicted risk scores were observed across various kernels and slice thicknesses, suggesting that deep features derived from LDCT scans can be sensitive to nuanced variations in acquisition and reconstruction parameters. Our study underscores the importance of enhancing understanding of how technical parameters impact predictive models like Sybil to enhance the reliability of model outputs, facilitating more accurate and robust clinical decision support.
基于图像的风险模型有潜力帮助识别那些将从癌症筛查中获益的个体。然而，模型需要对图像采集和重建参数的变化具有鲁棒性，这些参数会改变图像的外观，并可能导致不同的下游预测。我们在一个使用一系列参数采集和重建的肺癌筛查数据集上评估了Sybil，这是一个基于成像的肺癌模型，可预测长达六年的风险。利用169例回顾性采集的低剂量计算机断层扫描（LDCT）原始投影数据，我们为每个病例生成了六种图像条件，改变重建核（平滑、中等、锐利）和切片厚度（1.0毫米、2.0毫米）。每个图像条件都以相同的方式进行处理并通过预训练的Sybil模型运行。在各种核和切片厚度中观察到预测风险分数的变化，这表明从LDCT扫描中提取的深度特征可能对采集和重建参数的细微变化敏感。我们的研究强调了加强理解技术参数如何影响像Sybil这样的预测模型的重要性，以提高模型输出的可靠性，促进更准确和稳健的临床决策支持。
