Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350014, China.
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong 999077, SAR, China.
Comput Methods Programs Biomed. 2024 Jun;249:108135. doi: 10.1016/j.cmpb.2024.108135. Epub 2024 Mar 18.
Lung cancer continues to be a leading cause of cancer-related mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for advanced non-small cell lung cancer (NSCLC). Despite its potential, not all patients experience benefits from immunotherapy, and the current biomarkers used for treatment selection possess inherent limitations. As a result, the implementation of imaging-based biomarkers to predict the efficacy of lung cancer treatments offers a promising avenue for improving therapeutic outcomes.
This study presents an automatic system for immunotherapy efficacy prediction on the subjects with lung cancer, facilitating significant clinical implications. Our model employs an advanced 2.5D neural network that incorporates 2D intra-slice feature extraction and 3D inter-slice feature aggregation. We further present a lesion-focused prior to guide the re-calibration for intra-slice features, and a attention-based re-calibration for the inter-slice features. Finally, we design an accumulated back-propagation strategy to optimize network parameters in a memory-efficient fashion.
We demonstrate that the proposed method achieves impressive performance on an in-house clinical dataset, surpassing existing state-of-the-art models. Furthermore, the proposed model exhibits increased efficiency in inference for each subject on average. To further validate the effectiveness of our model and its components, we conducted comprehensive and in-depth ablation experiments and discussions.
The proposed model showcases the potential to enhance physicians' diagnostic performance due to its impressive performance in predicting immunotherapy efficacy, thereby offering significant clinical application value. Moreover, we conduct adequate comparison experiments of the proposed methods and existing advanced models. These findings contribute to our understanding of the proposed model's effectiveness and serve as motivation for future work in immunotherapy efficacy prediction.
肺癌仍是全球癌症相关死亡的主要原因，免疫疗法作为一种治疗晚期非小细胞肺癌（NSCLC）的有前途的治疗策略已经出现。尽管有潜力，但并非所有患者都能从免疫疗法中获益，目前用于治疗选择的生物标志物存在固有局限性。因此，实施基于影像学的生物标志物来预测肺癌治疗的疗效为改善治疗结果提供了一个有前途的途径。
本研究提出了一种用于预测肺癌患者免疫治疗疗效的自动系统，具有重要的临床意义。我们的模型采用了先进的 2.5D 神经网络，该网络结合了 2D 切片内特征提取和 3D 切片间特征聚合。我们进一步提出了一个病灶为中心的先验知识，以指导切片内特征的重新校准，并对切片间特征进行基于注意力的重新校准。最后，我们设计了一种累积反向传播策略，以在节省内存的方式优化网络参数。
我们证明了所提出的方法在内部临床数据集上取得了令人印象深刻的性能，超过了现有的最先进的模型。此外，所提出的模型在每个患者的推断方面平均表现出更高的效率。为了进一步验证我们的模型及其组件的有效性，我们进行了全面而深入的消融实验和讨论。
由于其在预测免疫治疗疗效方面的出色表现，所提出的模型有可能增强医生的诊断性能，从而具有重要的临床应用价值。此外，我们对所提出的方法和现有的先进模型进行了充分的比较实验。这些发现有助于我们理解所提出的模型的有效性，并为未来的免疫治疗疗效预测工作提供动力。
