中心型肺癌（CLC）常发生于临近纵膈和肺门的部位，手术通常只能做全肺切除，患者往往难以承受。近年来，CLC的治疗主要以放疗、化疗相结合为主。X线、CT和PET等多模态影像对于肺部病变的诊断、治疗和预后评估都起着重要的作用。本项目首先研究了从多模态影像中提取新的反映肿瘤异质性的特征方法，进而利用支持向量机（SVM）选择定量影像标记物；然后，我们建立了一种基于人工智能判别器的定量治疗与预后评估模型，利用贝叶斯信任网络（BBN）来融合定量影像标记物与其他类型标记物，来提高预后评估的准确性。在此过程中，我们与合作单位共同建立包含肺部多模态图像与随访记录的数据集。此项目的研究成果将对中心型肺癌的定量治疗和预后评估有着非常重要的理论研究意义和实际应用价值。

Central Lung Cancer (CLC) often occurs in the vicinity of the mediastinum and hilus of lung. Generally, it can be treated by surgery of doing pneumonectomy, which patients cannot always bear. In recent years, CLC therapy mainly depends on radiotherapy, hybrid with chemotherapy. Multi-modal imaging technologies, including X-ray, CT and PET, play a significant role on lung disease diagnosis, therapy and prognosis assessment. In this project, we first study the feature extraction methodology from multi-modal images, which presents the tumor heterogeneity. Next, quantitative image biomarkers are then selected from the extracted features by utilizing Support Vector Machine (SVM). Afterwards, we established the quantitative therapy and prognosis assessment model based on artificial intelligence discriminator. Furthermore, the Bayesian Belief Network (BBN) is used to integrate quantitative image biomarkers and other types of biomarkers, in order to improve the accuracy of prognosis assessment. During the whole period of this project, we will collaborate with our partner to establish a dataset containing multi-model images of lung and medical follow-up records. The achievements of this project will be significantly important to CLC quantitive therapy and prognosis assessment in both theoretical research and industrial products.