Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning

By N. Coudray, P. Ocampo, T. Sakellaropoulos, N. Narula, M. Snuderl, D. Fenyö, A. Moreira, Narges Razavian, A. Tsirigos. 2018

2,533 citations recorded by the source corpus.

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. A convolutional neural network model using feature extraction and machine-learning techniques provides a tool for classification of lung cancer histopathology images and predicting mutational status of driver oncogenes

Subfields: Artificial Intelligence, Cancer Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cancer Research

Citations in the corpus

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “Cardiovascular 08/15/2019 Class II(510(k)) 53 K183268 AI-Rad Companion (Cardiovascular) (Siemens Medical Solutions USA) An image processing software that provides quantitative and qualitative analysis from previously acquired CT DICOM images to support radiologists and physicians in the evaluation a”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “Gleason Grade in prostate cancer, lung cancer, breast cancer, melanoma, lymphoma and neuroblastoma) using statistical methods and machine and deep learning techniques [154], [169], [170].”
HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides (2019, background): “As revealed in a number of recent publications, many CAD and artificial intelligence (AI) algorithms appear to not generalize very well when evaluated on a cohort distinct from the set of images on which they were initially trained.(14,29) Consequently, these AI and CAD algorithms must be robustly v”
Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis (2020, background): “Regarding the high-level WSI classification task [33]–[39], due to the lack of detailed annotations of cancer regions, previous studies applied domain-specific handcrafted features to depict morphological character, texture, and statistic property of malignant tumor [33], [34], [40] along with the”