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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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“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
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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 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
“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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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