American Cancer Society. Cancer, Facts. and Figs. 2024. Published online 2024. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2024/2024-cancer-facts-and-figures-acs.pdf
Noone AM, Howlader N, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA, editors. SEER Cancer Statistics Review, 1975–2018, National Cancer Institute. Published online 2018 1975. https://seer.cancer.gov/csr/1975_2018/
Ding M, Pan Syu, Huang J et al. Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm. Qiu Y, ed. PLOS ONE. 2021;16(12):e0260600. https://doi.org/10.1371/journal.pone.0260600
Dehkharghanian T, Rahnamayan S, Riasatian A, et al. Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma. Am J Pathol. 2021;191(12):2172–83. https://doi.org/10.1016/j.ajpath.2021.08.013.
Goswami C, Chawla S, Thakral D, et al. Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC. BMC Genomics. 2020;21(1):744. https://doi.org/10.1186/s12864-020-07147-z.
Article CAS PubMed PubMed Central Google Scholar
Chabon JJ, Hamilton EG, Kurtz DM, et al. Integrating genomic features for non-invasive early lung cancer detection. Nature. 2020;580(7802):245–51. https://doi.org/10.1038/s41586-020-2140-0.
Article CAS PubMed PubMed Central Google Scholar
Bruhm DC, Mathios D, Foda ZH, et al. Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer. Nat Genet. 2023;55(8):1301–10. https://doi.org/10.1038/s41588-023-01446-3.
Article CAS PubMed PubMed Central Google Scholar
Fahrmann JF, Marsh T, Irajizad E, et al. Blood-Based Biomarker Panel for Personalized Lung Cancer Risk Assessment. J Clin Oncol. 2022;40(8):876–83. https://doi.org/10.1200/JCO.21.01460.
Article CAS PubMed PubMed Central Google Scholar
Li J, Liu K, Ji Z, et al. Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection. Cancer Sci. 2023;114(2):680–9. https://doi.org/10.1111/cas.15629.
Article CAS PubMed Google Scholar
Cameron JM, Sala A, Antoniou G, et al. A spectroscopic liquid biopsy for the earlier detection of multiple cancer types. Br J Cancer. 2023;129(10):1658–66. https://doi.org/10.1038/s41416-023-02423-7.
Article CAS PubMed PubMed Central Google Scholar
Meng S, Li Q, Zhou Z, et al. Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer. JAMA Netw Open. 2021;4(3):e213486. https://doi.org/10.1001/jamanetworkopen.2021.3486.
Article PubMed PubMed Central Google Scholar
Wang L, Zhang M, Pan X, et al. Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification. Adv Sci. 2022;9(34):2203786. https://doi.org/10.1002/advs.202203786.
Wang C, Shao J, He Y, et al. Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography. Nat Med. 2024;30(11):3184–95. https://doi.org/10.1038/s41591-024-03211-3.
Article CAS PubMed PubMed Central Google Scholar
Rao VM, Hla M, Moor M, et al. Multimodal generative AI for medical image interpretation. Nature. 2025;639(8056):888–96. https://doi.org/10.1038/s41586-025-08675-y.
Article CAS PubMed Google Scholar
Joel MZ, Avesta A, Yang DX, et al. Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging. Cancers. 2023;15(5):1548. https://doi.org/10.3390/cancers15051548.
Article PubMed PubMed Central Google Scholar
Joel MZ, Umrao S, Chang E, et al. Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology. JCO Clin Cancer Inf. 2022;6e2100170. https://doi.org/10.1200/CCI.21.00170.
Yoo H, Lee SH, Arru CD, et al. AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset. Eur Radiol. 2021;31(12):9664–74. https://doi.org/10.1007/s00330-021-08074-7.
Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs. JAMA Netw Open. 2020;3(9):e2017135. https://doi.org/10.1001/jamanetworkopen.2020.17135.
Article PubMed PubMed Central Google Scholar
Mikhael PG, Wohlwend J, Yala A, et al. Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. J Clin Oncol. 2023;41(12):2191–200. https://doi.org/10.1200/JCO.22.01345.
Article PubMed PubMed Central Google Scholar
Rajpurkar P, Irvin J, Ball RL et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. Sheikh A, ed. PLOS Med. 2018;15(11):e1002686. https://doi.org/10.1371/journal.pmed.1002686
Pesce E, Joseph Withey S, Ypsilantis PP, Bakewell R, Goh V, Montana G. Learning to detect chest radiographs containing pulmonary lesions using visual attention networks. Med Image Anal. 2019;53:26–38. https://doi.org/10.1016/j.media.2018.12.007.
Horry M, Chakraborty S, Pradhan B et al. Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection. Published online 2022. https://doi.org/10.48550/ARXIV.2201.09563
Kaviani P, Digumarthy SR, Bizzo BC, et al. Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study. Diagnostics. 2022;12(9):2086. https://doi.org/10.3390/diagnostics12092086.
Article PubMed PubMed Central Google Scholar
Lee JH, Lee D, Lu MT, et al. Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations. Radiology. 2022;305(1):209–18. https://doi.org/10.1148/radiol.212877.
Raghu VK, Walia AS, Zinzuwadia AN, et al. Validation of a Deep Learning–Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data. JAMA Netw Open. 2022;5(12):e2248793–2248793. https://doi.org/10.1001/jamanetworkopen.2022.48793.
Article PubMed PubMed Central Google Scholar
Hwang EJ, Park S, Jin KN, et al. Development and Validation of a Deep Learning–Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs. JAMA Netw Open. 2019;2(3):e191095. https://doi.org/10.1001/jamanetworkopen.2019.1095.
Article PubMed PubMed Central Google Scholar
Homayounieh F, Digumarthy S, Ebrahimian S, et al. An Artificial Intelligence–Based Chest X-ray Model on Human Nodule Detection Accuracy From a Multicenter Study. JAMA Netw Open. 2021;4(12):e2141096. https://doi.org/10.1001/jamanetworkopen.2021.41096.
Article PubMed PubMed Central Google Scholar
Seah JCY, Tang CHM, Buchlak QD, et al. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. Lancet Digit Health. 2021;3(8):e496–506. https://doi.org/10.1016/S2589-7500(21)00106-0.
Article CAS PubMed Google Scholar
Nam JG, Park S, Hwang EJ, et al. Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology. 2019;290(1):218–28. https://doi.org/10.1148/radiol.2018180237.
Nam JG, Hwang EJ, Kim J, et al. AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial. Radiology. 2023;307(2):e221894. https://doi.org/10.1148/radiol.221894.
Jang S, Song H, Shin YJ, et al. Deep Learning–based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs. Radiology. 2020;296(3):652–61. https://doi.org/10.1148/radiol.2020200165.
López-García G, Jerez JM, Franco L, Veredas FJ. Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. Stoean R, ed. PLOS ONE. 2020;15(3):e0230536. https://doi.org/10.1371/journal.pone.0230536
Cai Q, He B, Zhang P, et al. Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods. J Transl Med. 2020;18(1):463. https://doi.org/10.1186/s12967-020-02635-y.
Comments (0)