多模态 CT 深度学习标志物无创预测新辅助免疫化疗后非小细胞肺癌的病理完全缓解：一项多中心研究。

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study.

机构信息

Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

Department of Thoracic Surgery, Huazhong University of Science and Technology Tongji Medical College Union Hospital, Wuhan, Hubei, China.

出版信息

J Immunother Cancer. 2024 Sep 3;12(9):e009348. doi: 10.1136/jitc-2024-009348.

OBJECTIVES

Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers.

METHODS

This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps.

RESULTS

The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively.

CONCLUSIONS

By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

目的

尽管新辅助免疫化疗已广泛应用于非小细胞肺癌（NSCLC），但预测治疗反应仍然是一个挑战。我们使用预处理多模态 CT 来探索基于深度学习的免疫化疗反应图像生物标志物。

方法

本研究回顾性地获取了 2019 年 8 月至 2023 年 2 月期间在多个中心接受新辅助免疫化疗后接受手术的 NSCLC 患者的非增强和增强 CT 扫描。从非增强和增强 CT 扫描中提取深度学习特征，分别构建预测模型（LUNAI-uCT 模型和 LUNAI-eCT 模型）。对这两种类型的特征进行特征融合后，构建融合模型（LUNAI-fCT 模型）。使用受试者工作特征曲线下面积（AUC）、准确率、敏感度、特异度、阳性预测值和阴性预测值评估模型性能。使用 Shapley Additive exPlanations 分析量化 CT 成像特征对模型预测的影响。为了深入了解我们的模型如何进行预测，我们采用了 Gradient-weighted Class Activation Mapping 生成显著热图。

结果

训练和验证数据集包括来自中心 A 的 113 名患者，比例为 8:2，测试数据集包括来自中心 B 的 112 名患者（n=73）、中心 C 的 20 名患者（n=20）和中心 D 的 19 名患者（n=19）。在测试数据集中，LUNAI-uCT、LUNAI-eCT 和 LUNAI-fCT 模型的 AUC 分别为 0.762（95%CI 0.654 至 0.791）、0.797（95%CI 0.724 至 0.844）和 0.866（95%CI 0.821 至 0.883）。