Application of Artificial Intelligence in Lung Cancer

Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening

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The cancer genome atlas pan-cancer analysis project

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ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

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The 2015 World Health Organization Classification of Lung Tumors

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Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial

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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

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Machine Learning Applications in Cancer Prognosis and Prediction

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Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

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Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

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The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

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Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017.

MacMahon, H., Naidich, D. P., Goo, J. M., Lee, K. S., Leung, A. N. C., Mayo, J. R., Mehta, A. C., Ohno, Y., Powell, C. A., Prokop, M., et al. (2017) · Radiology · doi:10.1148/radiol.2017161659 · Cited by 2,491 · see citing works

MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0322-0 · Cited by 1,816 · see citing works

The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015

Nicholson, A. G., Tsao, M., Beasley, M. B., Borczuk, A., Brambilla, E. M., Cooper, W. A., Đačić, S., Jain, D., Kerr, K. M., Lantuéjoul, S., et al. (2021) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2021.11.003 · Cited by 1,606 · see citing works

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

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Digital pathology and artificial intelligence

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Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule

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Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study

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Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.

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Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

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PadChest: A large chest x-ray image dataset with multi-label annotated reports

Bustos, A., Pertusa, A., Salinas, J.-M., & de la Iglesia-Vayá, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101797 · Cited by 691 · see citing works

Integrating genomic features for non-invasive early lung cancer detection

Chabon, J. J., Hamilton, E. G., Kurtz, D. M., Esfahani, M. S., Moding, E. J., Stehr, H., Schroers‐Martin, J. G., Nabet, B. Y., Chen, B., Chaudhuri, A. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2140-0 · Cited by 686 · see citing works

Deep learning in cancer pathology: a new generation of clinical biomarkers

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Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579 · see citing works

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

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The future of computing beyond Moore’s Law

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Screening for Early Lung Cancer

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There’s plenty of room at the Top: What will drive computer performance after Moore’s law?

Leiserson, C. E., Thompson, N., Emer, J., Kuszmaul, B. C., Lampson, B., Sánchez, D., & Schardl, T. B. (2020) · Science · doi:10.1126/science.aam9744 · Cited by 473 · see citing works

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning

Wang, S., Shi, J., Ye, Z., Dong, D., Yu, D., Zhou, M., Liu, Y., Gevaert, O., Wang, K., Zhu, Y., et al. (2019) · European Respiratory Journal · doi:10.1183/13993003.00986-2018 · Cited by 437 · see citing works

The Danish Randomized Lung Cancer CT Screening Trial—Overall Design and Results of the Prevalence Round

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Cancer Diagnosis Using Deep Learning: A Bibliographic Review

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Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

Wang, X., Chen, H., Gan, C., Lin, H., Dou, Q., Tsougenis, E., Huang, Q., Cai, M., & Heng, P. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2935141 · Cited by 417 · see citing works

Annual or biennial CT screening versus observation in heavy smokers

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Towards automatic pulmonary nodule management in lung cancer screening with deep learning

Ciompi, F., Chung, K., van Riel, S. J., Setio, A. A. A., Gerke, P. K., Jacobs, C., Scholten, E. T., Schaefer‐Prokop, C. M., Wille, M. M. W., Marchianò, A., et al. (2017) · Scientific Reports · doi:10.1038/srep46479 · Cited by 407 · see citing works

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

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Genome-wide allelotyping of lung cancer identifies new regions of allelic loss, differences between small cell lung cancer and non-small cell lung cancer, and loci clustering.

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A review of machine learning techniques using decision tree and support vector machine

Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. V. (2016) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2016.7860040 · Cited by 333 · see citing works

Gut microbiome, big data and machine learning to promote precision medicine for cancer

Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., Gasbarrini, A., & Tortora, G. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0327-3 · Cited by 324 · see citing works

Machine learning for metabolic engineering: A review

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Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

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Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

van Ginneken, B., Armato, S. G., de Hoop, B., de Vorst, S. V. A.-V., Duindam, T., Niemeijer, M., Murphy, K., Schilham, A. M. R., Retico, A., Fantacci, M. E., et al. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.05.005 · Cited by 298 · see citing works

Predicting Malignant Nodules from Screening CT Scans

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Untitled work

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miR-Test: A Blood Test for Lung Cancer Early Detection

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Deep learning-based survival prediction for multiple cancer types using histopathology images

Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C. H., Chen, P.-H. C., Liu, Y., & Stumpe, M. C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0233678 · Cited by 266 · see citing works

Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

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Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer

Zhu, X., Dong, D., Chen, Z., Fang, M., Zhang, L., Song, J., Yu, D., Zang, Y., Liu, Z., Shi, J., et al. (2018) · European Radiology · doi:10.1007/s00330-017-5221-1 · Cited by 211 · see citing works

Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides

Gertych, A., Świderska-Chadaj, Ż., Ma, Z., Ing, N., Markiewicz, T., Cierniak, S., Salemi, H., Guzman, S. J., Walts, A. E., & Knudsen, B. S. (2019) · Scientific Reports · doi:10.1038/s41598-018-37638-9 · Cited by 189 · see citing works

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Lewis, J. E., & Kemp, M. L. (2021) · Nature Communications · doi:10.1038/s41467-021-22989-1 · Cited by 187 · see citing works

Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept

Jochems, A., Deist, T. M., van Soest, J., Eble, M. J., Bulens, P., Coucke, P. A., Dries, W., Lambin, P., & Dekker, A. L. A. J. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.10.002 · Cited by 180 · see citing works

Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics

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US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised

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Bayesian Networks for Clinical Decision Support in Lung Cancer Care

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Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers

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Systematic review of multidisciplinary teams in the management of lung cancer

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Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries

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The Coding of Roentgen Images for Computer Analysis as Applied to Lung Cancer

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Structural and functional radiomics for lung cancer

Wu, G., Jochems, A., Refaee, T., Ibrahim, A., Yan, C., Sanduleanu, S., Woodruff, H. C., & Lambin, P. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05242-1 · Cited by 138 · see citing works

CNN-based Method for Lung Cancer Detection in Whole Slide Histopathology Images

Šarić, M., Russo, M., Stella, M., & Sikora, M. (2019) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.23919/splitech.2019.8783041 · Cited by 131 · see citing works

Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images

Sha, L., Osinski, B. L., Ho, I. Y., Tan, T. L., Willis, C., Weiss, H., Beaubier, N., Mahon, B. M., Taxter, T. J., & Yip, S. S. F. (2019) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_24_19 · Cited by 130 · see citing works

Bone Cancer

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The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis

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Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors

Zhang, C., Leng, W., Sun, C., Lu, T., Chen, Z., Men, X.-B., Wang, Y., Wang, G., Zhen, B., & Qin, J. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.03.009 · Cited by 126 · see citing works

Lung Nodule Detection via Deep Reinforcement Learning

Ali, I. M., Hart, G. R., Gunabushanam, G., Liang, Y., Muhammad, W., Nartowt, B. J., Kane, M. J., Ma, X., & Deng, J. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00108 · Cited by 121 · see citing works

Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result

Jiang, M., Sun, D., Guo, Y., Guo, Y., Xiao, J., Wang, L., & Yao, X. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.04.016 · Cited by 120 · see citing works

A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma

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Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer

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Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction

Sun, Y., Li, C., Jin, L., Gao, P., Zhao, W., Ma, W., Tan, M., Wu, W., Duan, S., Shan, Y., et al. (2020) · European Radiology · doi:10.1007/s00330-020-06776-y · Cited by 111 · see citing works

Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies

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Deep learning and medical imaging

Klang, E. (2018) · Journal of Thoracic Disease · doi:10.21037/jtd.2018.02.76 · Cited by 107 · see citing works

Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA

Abels, E., & Pantanowitz, L. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_11_17 · Cited by 106 · see citing works

A narrative review of digital pathology and artificial intelligence: focusing on lung cancer

Sakamoto, T., Furukawa, T., Lami, K., Pham, H. H. N., Uegami, W., Kuroda, K., Kawai, M., Sakanashi, H., Cooper, L. A. D., Bychkov, A., et al. (2020) · Translational Lung Cancer Research · doi:10.21037/tlcr-20-591 · Cited by 106 · see citing works

CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk

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Lung Cancer Screening in the Randomized Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial

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Improving outcomes in lung cancer: the value of the multidisciplinary health care team

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Lung Cancer in Republic of China

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Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning

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Serum and blood based biomarkers for lung cancer screening: a systematic review

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