Tan Peixin, Huang Wei, Wang Lingling, Deng Guanhua, Yuan Ye, Qiu Shili, Ni Dong, Du Shasha, Cheng Jun
Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Front Physiol. 2022 Jul 25;13:978222. doi: 10.3389/fphys.2022.978222. eCollection 2022.
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.
免疫检查点抑制剂（ICI）彻底改变了肺癌的治疗方式，包括非小细胞肺癌和小细胞肺癌。尽管免疫疗法取得了令人鼓舞的成果，但ICI相关肺炎（ICIP）是一种潜在致命的不良事件。因此，在免疫治疗开始前早期检测有发生ICIP风险的患者，对于通过早期干预减轻未来并发症并改善治疗结果至关重要。在本研究中，我们展示了首个探索深度学习预测发生ICIP风险患者潜力的报告工作。为此，我们收集了24例免疫治疗后发生ICIP的患者和24例未发生ICIP的对照患者的治疗前基线CT图像及临床信息。基于3D CT图像和临床数据构建了一个多模态深度学习模型。为提高性能，我们通过在一个大型自然图像数据集和一个大型CT图像数据集上依次预训练模型以及采用迁移学习，运用了两阶段迁移学习。进行了大量实验以验证我们方法中使用的关键组件的有效性。使用五折交叉验证，我们的方法能够准确区分ICIP患者和非ICIP患者，受试者操作特征曲线下面积为0.918，准确率为0.920。本研究证明了深度学习在识别有发生ICIP风险患者方面具有广阔的潜力。所提出的深度学习模型能够对ICIP进行有效的风险分层、密切监测和及时管理，最终带来更好的治疗结果。
