scholarly journals A Convolutional Neural Network Application for Predicting the Locating of Squamous Cell Carcinoma in the Lung

2018 ◽  
pp. 62-65 ◽  
Author(s):  
H. Selçuk NOĞAY ◽  
Tahir Cetin AKINCI
Keyword(s):  
Neural Network ◽  
Squamous Cell Carcinoma ◽  
Convolutional Neural Network ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Network Application

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases

2019 ◽  
Vol 11 (509) ◽  
pp. eaaw8513 ◽  
Author(s):  
Philipp Jurmeister ◽  
Michael Bockmayr ◽  
Philipp Seegerer ◽  
Teresa Bockmayr ◽  
Denise Treue ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dna Methylation ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Pulmonary Metastases ◽  
Normal Lung ◽  
Support Vector

Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.

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

2017 ◽  
Author(s):  
Nicolas Coudray ◽  
Andre L. Moreira ◽  
Theodore Sakellaropoulos ◽  
David Fenyö ◽  
Narges Razavian ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Squamous Cell Carcinoma ◽  
Deep Learning ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Visual Analysis ◽  
The Cancer Genome Atlas ◽  
Normal Lung ◽  
Histopathology Images

AbstractVisual analysis of histopathology slides of lung cell tissues is one of the main methods used by pathologists to assess the stage, types and sub-types of lung cancers. Adenocarcinoma and squamous cell carcinoma are two most prevalent sub-types of lung cancer, but their distinction can be challenging and time-consuming even for the expert eye. In this study, we trained a deep learning convolutional neural network (CNN) model (inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately classify whole-slide pathology images into adenocarcinoma, squamous cell carcinoma or normal lung tissue. Our method slightly outperforms a human pathologist, achieving better sensitivity and specificity, with ∼0.97 average Area Under the Curve (AUC) on a held-out population of whole-slide scans. Furthermore, we trained the neural network to predict the ten most commonly mutated genes in lung adenocarcinoma. We found that six of these genes – STK11, EGFR, FAT1, SETBP1, KRAS and TP53 – can be predicted from pathology images with an accuracy ranging from 0.733 to 0.856, as measured by the AUC on the held-out population. These findings suggest that deep learning models can offer both specialists and patients a fast, accurate and inexpensive detection of cancer types or gene mutations, and thus have a significant impact on cancer treatment.

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