Among cancers, lung cancer has the highest morbidity, and mortality rate. The survival probability of lung cancer patients depends largely on an early diagnosis. For predicting lung cancer from low-dose Computed Tomography (LDCT) scans, computer-aided diagnosis (CAD) system needs to detect all pulmonary nodules, and combine their morphological features to assess the risk of cancer. An automatic lung cancer prognosis system is proposed. The existing CAD system is only for nodule detection. Actually, presence of a nodule does not mean cancer. Depending on its morphological features, the risk that it eventually would develop into cancer, is different. The motivation of the work is to propose a complete lung cancer prognosis system. It consists of 2 cascaded modules: nodule detection module and cancer risk evaluation module. In nodule detection module, two object detection algorithms are ensembled to minimize missing detection, i.e., maximize recall performance. They are based on 3D convolutional neural network (3D-CNN), and our recently proposed model of recurrent neural network (RNN). As they extract features in completely different ways, we call them heterogeneous deep learning models. By ensembing them, we could achieve much better recall performance compared to individual detectors. In cancer risk evaluation module, 3D-CNN based models are trained to evaluate the grade of malady of morphological features of pulmonary nodules. It will also provide medically interpretable intermediate information. Finally, a regression model is trained to match the ground truth labels describing morbidity grade of the CT-Scan. In this work, 13 features from the highest risk nodule is used to evaluate the risk of lung cancer. We also identify the subset of structural and morphological features which are strongly related to grading decision, labelled by oncologist. The final system could obtain a low logloss of 0.408.

在癌症中，肺癌的发病率和死亡率最高。肺癌患者的存活概率在很大程度上取决于早期诊断。为了通过低剂量计算机断层扫描（LDCT）扫描预测肺癌，计算机辅助诊断（CAD）系统需要检测所有肺结节，并结合其形态学特征来评估癌症风险。提出了一种自动肺癌预后系统。现有的CAD系统仅用于结节检测。实际上，结节的存在并不意味着癌症。根据其形态特征，其最终发展为癌症的风险有所不同。这项工作的动机是提出一个完整的肺癌预后系统。它由2个级联模块组成：结节检测模块和癌症风险评估模块。在结节检测模块中，集成了两种对象检测算法，以最大程度地减少漏检，即最大化召回性能。它们基于3D卷积神经网络（3D-CNN）和我们最近提出的递归神经网络（RNN）模型。当他们以完全不同的方式提取特征时，我们称它们为异构深度学习模型。通过结合它们，与单个探测器相比，我们可以获得更好的召回性能。在癌症风险评估模块中，训练了基于3D-CNN的模型来评估肺结节形态特征的恶性程度。它还将提供医学上可解释的中间信息。最后，训练回归模型以匹配描述CT扫描发病率等级的地面真相标签。在这项工作中 最高风险结节的13个特征用于评估肺癌的风险。我们还确定了与肿瘤分级决定密切相关的结构和形态特征的子集，并由肿瘤学家进行了标记。最终系统可以获得0.408的低logloss。