Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

[1] Lancaster HL(2022)Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification Lung Cancer 165 133-140

[2] Zheng S(2020)Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives Nat Rev Clin Oncol 191 1-14

[3] Aleshina OO(2021)(2021) Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value BMC Med 146 110068-626.e32

[4] Oudkerk M(2022)Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program Eur J Radiol 325 962-152

[5] Liu S(2021)Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement JAMA 76 626.e23-1061

[6] Heuvelmans MA(2021)Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system Clin Radiol 29 144-488

[7] Chamberlin J(2019)A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies Eur Radiol 9 100435-288

[8] Kocher MR(2022)Higher agreement between readers with deep learning CAD software for reporting pulmonary nodules on CT Eur J Radiol Open 38 1052-2900

[9] Waltz J(2020)Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography Jpn J Radiol 210 480-undefined

[10] Cui X(2018)JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function AJR Am J Roentgenol 281 279-undefined

[1] Lancaster HL(2022)Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification Lung Cancer 165 133-140

[2] Zheng S(2020)Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives Nat Rev Clin Oncol 191 1-14

[3] Aleshina OO(2021)(2021) Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value BMC Med 146 110068-626.e32

[4] Oudkerk M(2022)Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program Eur J Radiol 325 962-152

[5] Liu S(2021)Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement JAMA 76 626.e23-1061

[6] Heuvelmans MA(2021)Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system Clin Radiol 29 144-488

[7] Chamberlin J(2019)A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies Eur Radiol 9 100435-288

[8] Kocher MR(2022)Higher agreement between readers with deep learning CAD software for reporting pulmonary nodules on CT Eur J Radiol Open 38 1052-2900

[9] Waltz J(2020)Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography Jpn J Radiol 210 480-undefined

[10] Cui X(2018)JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function AJR Am J Roentgenol 281 279-undefined