The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Open accessPublished version of a preprint
Abstract
Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .
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“By design, these patch-level approaches ignore up to 99% of the total tissue, miss potentially diagnostically relevant areas, and cannot be applied to tasks that require the full context of the entire tissue sample (e.g., tumor thickness prediction).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [11]
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In the citing paper
“which may result from prognostic information also partially explained via genomics as aberrations such as ERBB2 or KRAS mutation and the presence of microsatellite instability can be determined from histology (Coudray et al., 2018;Kather et al.,2019,2020;Gamble et al.,2021).”
From Pan-cancer integrative histology-genomic analysis via multimodal deep learning (2022) · cites this paper as Coudray et al., 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“By design, these patch-level approaches ignore up to 99% of the total tissue, miss potentially diagnostically relevant areas, and cannot be applied to tasks that require the full context of the entire tissue sample (e.g., tumor thickness prediction).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [11]
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In the citing paper
“Coudray et al. proposed that specific gene alterations might influence the morphological appearance of cancer cells on histologic slides; by training neural networks, they were able to predict the ten most frequent mutations in lung adenocarcinoma [36].”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [36]
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In the citing paper
“By design, these patch-level approaches ignore up to 99% of the total tissue, miss potentially diagnostically relevant areas, and cannot be applied to tasks that require the full context of the entire tissue sample (e.g., tumor thickness prediction).”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [11]
Experimental — accuracy not yet measured
In the citing paper
“Studies have even shown that morphological features captured by a hematoxylin and eosin (H&E) stain are predictive of molecular biomarkers utilized in theragnosis 85,89 .”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [85]
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [19]
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Citations
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Cited by
Sorted by citations
Items 101–141 of 141
Benchmarking foundation models as feature extractors for weakly supervised computational pathology
Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01516-3 · Cited by 80
Turner, O. C., Aeffner, F., Bangari, D. S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L. E., Rudmann, D. G., Bawa, B., et al. (2020) · Toxicologic Pathology · doi:10.1177/0192623319881401 · Cited by 64
Biased data, biased AI: deep networks predict the acquisition site of TCGA images
Dehkharghanian, T., Bidgoli, A. A., Riasatian, A., Mazaheri, P., Campbell, C. J. V., Pantanowitz, L., Tizhoosh, H. R., & Rahnamayan, S. (2023) · Diagnostic Pathology · doi:10.1186/s13000-023-01355-3 · Cited by 67
Federated Learning Empowered Real-Time Medical Data Processing Method for Smart Healthcare
Guo, K., Chen, T., Ren, S., Li, N., Hu, M., & Kang, J. (2024) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3185395 · Cited by 63
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Kumar, R., Khan, A. A., Kumar, J., Zakria, Golilarz, N. A., Zhang, S., Ting, Y., Zheng, C., & Wang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076767 · Cited by 57
Technology Can Augment, but Not Replace, Critical Human Skills Needed for Patient Care
Alrassi, J., Katsufrakis, P. J., & Chandran, L. (2021) · Academic Medicine · doi:10.1097/acm.0000000000003733 · Cited by 60
Jiang, Y., Liang, X., Han, Z., Wang, W., Xi, S., Li, T., Chen, C., Yuan, Q., Li, N., Yu, J., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00065-0 · Cited by 58
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout
Cortés-Ciriano, I., & Bender, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00297 · Cited by 51
Shao, X., Zhang, H., Wang, Y., Qian, H., Zhu, Y., Dong, B., Xu, F., Chen, N., Liu, S., Pan, J., et al. (2020) · Nanomedicine: Nanotechnology, Biology and Medicine · doi:10.1016/j.nano.2020.102245 · Cited by 53
Transcriptomics-Guided Slide Representation Learning in Computational Pathology
Jaume, G., Oldenburg, L., Vaidya, A., Chen, R. J., Williamson, D. F. K., Peeters, T., Song, A. H., & Mahmood, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00920 · Cited by 51
Kriegsmann, K., Lobers, F., Zgorzelski, C., Kriegsmann, J., Janßen, C., Meliß, R. R., Muley, T., Sack, U., Steinbuss, G., & Kriegsmann, M. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1022967 · Cited by 50
Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology
You, S., Sun, Y., Yang, L., Park, J., Tu, H., Marjanovic, M., Sinha, S., & Boppart, S. A. (2019) · npj Precision Oncology · doi:10.1038/s41698-019-0104-3 · Cited by 46
Khosravi, P., Fuchs, T. J., & Ho, D. J. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-3630 · Cited by 41
Carrillo-Perez, F., Pizurica, M., Zheng, Y., Nandi, T. N., Madduri, R., Shen, J., & Gevaert, O. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01193-8 · Cited by 37
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Chen, S. B., & Novoa, R. A. (2022) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2022.01.003 · Cited by 37
Generating dermatopathology reports from gigapixel whole slide images with HistoGPT
Tran, M., Schmidle, P., Guo, R. R., Wagner, S. J., Koch, V., Lupperger, V., Novotny, B., Murphree, D. H., Hardway, H. D., D’Amato, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60014-x · Cited by 36
Deep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34
Kiyokawa, H., Abe, M., Matsui, T., Kurashige, M., Ohshima, K., Tahara, S., Nojima, S., Ogino, T., Sekido, Y., Mizushima, T., et al. (2022) · The American Journal of Pathology · doi:10.1016/j.ajpath.2022.03.006 · Cited by 32
Chen, Z., Zhu, P., Qiu, W., Guo, J., & Li, Y. (2023) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12783 · Cited by 31
Computational estimation of quality and clinical relevance of cancer cell lines
Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211017 · Cited by 31
Goel, I., Bhaskar, Y., Kumar, N., Singh, S., Amanullah, M., Dhar, R., & Karmakar, S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1550407 · Cited by 31
Giga-SSL: Self-Supervised Learning for Gigapixel Images
Lazard, T., Lerousseau, M., Decencière, E., & Walter, T. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00453 · Cited by 30
Differences between human and machine perception in medical diagnosis
Makino, T., Jastrzębski, S., Oleszkiewicz, W., Chacko, C., Ehrenpreis, R., Samreen, N., Chhor, C., Kim, E., Lee, J., Pysarenko, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-10526-z · Cited by 30
Deep Learning Based Multimodal Progression Modeling for Alzheimer’s Disease
Yang, L., Wang, X., Guo, Q., Gladstein, S., Wooten, D., Li, T., Robieson, W. Z., Sun, Y., Huang, X., & Initiative, F. T. A. D. N. (2021) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2021.1884129 · Cited by 28
One label is all you need: Interpretable AI-enhanced histopathology for oncology
Tavolara, T. E., Su, Z., Gurcan, M. N., & Niazi, M. K. K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.006 · Cited by 28
Sassis, L., Kefala-Karli, P., Sassi, M., & Zervides, C. (2021) · Journal of Artificial Intelligence for Medical Sciences · doi:10.2991/jaims.d.210617.002 · Cited by 26
Liu, B., Polack, M., Coudray, N., Claudio Quiros, A., Sakellaropoulos, T., Le, H., Karimkhan, A., Crobach, A. S. L. P., van Krieken, J. H. J. M., Yuan, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57541-y · Cited by 25
Self-citationThe impact of AI on modern oncology from early detection to personalized cancer treatment
Li, J., Zhang, L., Yu, Z., Bao, Z., Li, D., & Wang, L. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01276-6 · Cited by 24
Carmichael, I., Song, A. H., Chen, R. J., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-16434-7_38 · Cited by 20
SSLP: Spatial Guided Self-supervised Learning on Pathological Images
Li, J., Lin, T., & Xu, Y. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87196-3_1 · Cited by 26
Developing an image-based deep learning framework for automatic scoring of the pentagon drawing test
Li, Y., Guo, J., & Yang, P. (2022) · Journal of Alzheimer's Disease · doi:10.3233/jad-210714 · Cited by 16
Benchmarking artificial intelligence methods for end-to-end computational pathology
Laleh, N. G., Muti, H. S., Lavinia Loeffler, C. M., Echle, A., Saldanha, O. L., Mahmood, F., Lu, M. Y., Trautwein, C., Langer, R., Dislich, B., et al. (2021) · doi:10.1101/2021.08.09.455633 · Cited by 13
Deep learning transforms colorectal cancer biomarker prediction from histopathology images
Ruusuvuori, P., Valkonen, M., & Latonen, L. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.08.006 · Cited by 13
Bellahsen-Harrar, Y., Lubrano, M., Lépine, C., Beaufrère, A., Bocciarelli, C., Brunet, A., Decroix, E., El-Sissy, F. N., Fabiani, B., Morini, A., et al. (2025) · PLOS One · doi:10.1371/journal.pone.0323270 · Cited by 11
Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images
Liu, Y., He, Q., Duan, H., Shi, H., Han, A., & He, Y. (2022) · Sensors · doi:10.3390/s22166053 · Cited by 10
Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10
Integrating context for superior cancer prognosis
Jaume, G., Song, A. H., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00924-z · Cited by 8
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7
Klingelhöfer, D., Braun, M., Dröge, J., Groneberg, D. A., & Brüggmann, D. (2025) · Globalization and Health · doi:10.1186/s12992-025-01128-1 · Cited by 6
Ezzeddine, H., Awad, M., Ghanem, A. S. A., & Mourani, B. (2021) · 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET) · doi:10.1109/imcet53404.2021.9665574 · Cited by 2
Türkmen Dedeoğlu, A., Yanık, H., Uysal, E., Fikir, G., Değirmenci, E., Türsen, Ü., & Yuyucu Karabulut, Y. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02057-0
Metadata
- Authors
- Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A.
- Affiliations
- New York University (US)
- National Technical University of Athens (GR)
- NYU Langone Health (US)
- Corresponding author
- Razavian, Narges
- Tsirigos, Aristotelis
- ORCID
- Venue
- Nature Medicine
- Published
- 2018-09-17
- Volume
- 24
- Issue
- 10
- Pages
- 1559-1567
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC9847512
- OpenAlex
- W2760946358
- Cited by
- 2,930
- References
- 71
- Citations by year
- 2026: 349 · 2025: 380 · 2024: 380 · 2023: 436 · 2022: 452 · 2021: 415 · 2020: 319 · 2019: 183 · 2018: 10
- FWCI
- 130.74
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Medicine (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NYU Langone Medical Center
- School of Medicine, New York University
- National Cancer Institute (P30CA016087)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- non-small cell lung cancer, lung adenocarcinoma, squamous cell carcinoma, histopathological image classification, whole slide images, gene mutation prediction, STK11, EGFR, FAT1, SETBP1, KRAS, TP53, computer-aided diagnosis, frozen tissue, formalin-fixed paraffin-embedded tissue, biopsy
- MeSH terms
- Deep Learning, Adenocarcinoma, Carcinoma, Non-Small-Cell Lung, Carcinoma, Squamous Cell, Humans, Mutation, Neoplasm Proteins, Gene Expression Regulation, Neoplastic, Neural Networks, Computer
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