Automated detection and segmentation is a prerequisite for the deployment of image-based secondary analyses, especially for lung tumors. However, currently only applications for lung nodules ≤3 cm exist. Therefore, we tested the performance of a fully automated AI-based lung nodule algorithm for detection and 3D segmentation of primary lung tumors in the context of tumor staging using the CT component of FDG-PET/CT and including all T-categories (T1-T4). FDG-PET/CTs of 320 patients with histologically confirmed lung cancer performed between 01/2010 and 06/2016 were selected. First, the main primary lung tumor within each scan was manually segmented using the CT component of the PET/CTs as reference. Second, the CT series were transferred to a platform with AI-based algorithms trained on chest CTs for detection and segmentation of lung nodules. Detection and segmentation performance were analyzed. Factors influencing detection rates were explored with binominal logistic regression and radiomic analysis. We also processed 94 PET/CTs negative for pulmonary nodules to investigate frequency and reasons of false-positive findings. The ratio of detected tumors was best in the T1-category (90.4%) and decreased continuously: T2 (70.8%), T3 (29.4%), and T4 (8.8%). Tumor contact with the pleura was a strong predictor of misdetection. Segmentation performance was excellent for T1 tumors (r = 0.908, p < 0.001) and tumors without pleural contact (r = 0.971, p < 0.001). Volumes of larger tumors were systematically underestimated. There were 0.41 false-positive findings per exam. The algorithm tested facilitates a reliable detection and 3D segmentation of T1/T2 lung tumors on FDG-PET/CTs. The detection and segmentation of more advanced lung tumors is currently imprecise due to the conception of the algorithm for lung nodules <3 cm. Future efforts should therefore focus on this collective to facilitate segmentation of all tumor types and sizes to bridge the gap between CAD applications for screening and staging of lung cancer.

中文翻译：

---

AI驱动的肺结节算法对原发性肺肿瘤的检测和3D分割的评估。

自动化的检测和分割是部署基于图像的二次分析的前提，特别是对于肺部肿瘤。但是，目前仅存在肺结节≤3 cm的应用。因此，我们使用FDG-PET / CT的CT组件并包括所有T类（T1-T1），在肿瘤分期的背景下测试了基于AI的全自动肺结节算法对原发性肺肿瘤的检测和3D分割的性能T4）。选择在01/2010年至06/2016年进行的320例经组织学证实为肺癌的患者的FDG-PET / CT。首先，将每次扫描中的主要原发性肺肿瘤以PET / CT的CT成分为参考手动进行分割。其次，将CT系列转移到一个平台，该平台具有基于AI的经过胸部CT训练的算法，用于肺结节的检测和分割。分析了检测和分割性能。通过二项式逻辑回归和放射学分析探索了影响检出率的因素。我们还处理了94例肺结节阴性的PET / CT，以调查假阳性结果的频率和原因。在T1类中检出的肿瘤比例最高（90.4％），并持续下降：T2（70.8％），T3（29.4％）和T4（8.8％）。肿瘤与胸膜接触是错误检测的有力预测指标。对于T1肿瘤（r = 0.908，p <0.001）和没有胸膜接触的肿瘤（r = 0.971，p <0.001），分割性能非常好。系统地低估了较大肿瘤的体积。每次检查有0.41例假阳性结果。测试的算法有助于在FDG-PET / CT上对T1 / T2肺肿瘤进行可靠的检测和3D分割。由于<3 cm的肺结节的算法概念，目前尚不精确的检测和分割更晚期的肺肿瘤。因此，未来的工作应集中在这一集合上，以促进所有肿瘤类型和大小的分割，以弥合CAD用于肺癌筛查和分期的应用之间的差距。