Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors

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.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

“…Table 1 shows the studies done in both non-ARDS and ARDS frameworks. The non-ARDS AI techniques were also applied to nodule, cancer, and tumor segmentation [ 129 ] [ 130 ] [ 131 ] [ 132 ] [ 133 ] [ 134 ] [ 135 ] [ 136 ]. Note that we excluded (a) lung cellular images and (b) animal studies by considering only human lungs.…”

Térdporc szegmentálása MR-felvételekből mesterséges intelligencia segítségével

“…Szakmai jelentősége lehet bármely anatómiai képlet, illetve patológiai elváltozás kijelölésének, szegmentálásának. Például tüdődaganatok esetén az onkológus nemcsak a tüdőrákszűréshez szükséges diagnosztikai célból [7], hanem sugárterápiás tervezésnél is használja a szegmentálást [8].…”

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

“…Fully automated segmentation methods in PET have been proposed, using fuzzy random walk (Soufi et al 2017) or mutual information of CT and PET to identify NSCLC (Weikert et al 2019;Bug et al 2019). U-Net, one of the mostly used CNN architectures for image contouring, has shown to be able to segment pulmonary parenchyma (Ait Skourt et al 2018), and relatively small tumors (1.83 cm 2 ) resulting reproducible across different scanners (dice scores of 74%), relatively uninfluenced by the partial volume effect, and effectively trained with limited data (30 patients yielded a dices score of 70%) (Leung et al 2020).…”

“…Conversely, the algorithm systematically underestimated volumes of sizable tumors. Accordingly, efforts should focus on facilitating segmentation of all tumor types and sizes to bridge the gap between CAD applications for lung cancer screening and staging (Weikert et al 2019 ).…”