Abstract
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.
Highlights
Failure to detect lung cancer on imaging studies is a very common reason for malpractice suits [1]. e reasons for misdiagnosis are multilayered and include recognition error and satisfaction of search [2]
Data Analysis. e output of the AI algorithm pipeline was the transversal chest CT component of the PET/CT with overlays for lung lobe boundaries and tumor boundaries of detected tumors. is output series contained specifications of volume (VolumeAI), 2D diameter, and location for every detected tumor and served as the index test. e reference standard was the CT component of the PET/CT for detection and the volumes that were calculated from the 3D tumor masks that resulted from the manual image segmentation process
Detection rates differed significantly across T-categories and declined towards advanced tumors: 90.4% for T1 (75 of 83), 70.8% for T2 (75 of 106), 29.4% for T3 (15 of 51), and 8.8% for T4 (7 of 80). is detection decline is reflected in Figure 2(a) that shows the number of detected and missed tumors by T-category and Figure 2(b) that displays detection of tumors depending on the ground truth volume
Summary
Failure to detect lung cancer on imaging studies is a very common reason for malpractice suits [1]. e reasons for misdiagnosis are multilayered and include recognition error and satisfaction of search [2]. Erefore, 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).
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