Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning

计算机断层成像（CT）是肺腺癌诊断与评估的重要工具，利用CT图像预测肺腺癌患者手术后的无复发生存期（RFS）对于术后治疗方案的制定具有重要意义。针对CT图像的肺腺癌RFS精准预测难题，本文提出了一种基于自监督预训练和多任务学习的肺腺癌RFS预测方法。采用“图像变换—图像恢复”的自监督学习策略，在公开肺部CT数据集上对3D-UNet网络进行自监督预训练解析肺部图像的通用视觉特征，通过分割与分类的多任务学习策略进一步优化网络特征提取能力，引导网络提取与RFS相关的图像特征，同时设计多尺度特征聚合模块以充分聚合多尺度的图像特征，最后借助前馈神经网络预测肺腺癌RFS风险评分。通过十折交叉验证评估所提方法的预测性能。结果显示，所提方法预测RFS的一致性指数（C-index）与预测三年内是否复发的曲线下面积（AUC）分别达到0.691 ± 0.076与0.707 ± 0.082，预测性能优于现有方法。综上，本研究所提方法在肺腺癌患者RFS预测方面表现出潜在的优越性，有望为个体化治疗方案的制定提供可靠依据。.; Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tailoring postoperative treatment plans. Addressing the challenging task of accurate RFS prediction using CT images, this paper introduces an innovative approach based on self-supervised pre-training and multi-task learning. We employed a self-supervised learning strategy known as "image transformation to image restoration" to pretrain a 3D-UNet network on publicly available lung CT datasets to extract generic visual features from lung images. Subsequently, we enhanced the network's feature extraction capability through multi-task learning involving segmentation and classification tasks, guiding the network to extract image features relevant to RFS. Additionally, we designed a multi-scale feature aggregation module to comprehensively amalgamate multi-scale image features, and ultimately predicted the RFS risk score for lung adenocarcinoma with the aid of a feed-forward neural network. The predictive performance of the proposed method was assessed by ten-fold cross-validation. The results showed that the consistency index (C-index) of the proposed method for predicting RFS and the area under curve (AUC) for predicting whether recurrence occurs within three years reached 0.691 ± 0.076 and 0.707 ± 0.082, respectively, and the predictive performance was superior to that of existing methods. This study confirms that the proposed method has the potential of RFS prediction in lung adenocarcinoma patients, which is expected to provide a reliable basis for the development of individualized treatment plans.