Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules

OBJECTIVES: The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). METHODS: CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1-10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of 

• Peters, A. A.
• Solomon, J. B.
• von Stackelberg, O.
• Samei, E.
• Alsaihati, N.
• Valenzuela, W.
• Debic, M.
• Heidt, C.
• Huber, A. T.
• Christe, A.
• Heverhagen, J. T.
• Kauczor, H. U.
• Heussel, C. P.
• Ebner, L.
• Wielpütz, M. O.