Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.

Journal: Journal of translational medicine

PMID: 38978066

Abstract

BACKGROUND: The tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferation, invasion, and metastasis. Artificial intelligence (AI) methods could potentially accelerate TME analysis. The aims of this study were to (1) assess the feasibility of using hematoxylin and eosin (H&E)-stained whole slide images (WSI) to develop an AI model for evaluating the TME and (2) to characterize the TME of adenocarcinoma (ADCA) and squamous cell carcinoma (SCCA) in fibrotic and non-fibrotic lung.

Authors

Anjali Saqi

Regeneron Pharmaceuticals, Tarrytown, New York.
Yucheng Liu

Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China.
Michelle Garlin Politis

Department of Pathology and Cell Biology, Columbia University Irving Medical Center, 630 West 168th Street, New York, NY, VC14-215, 10032, USA.
Mary Salvatore

Department of Radiology and.
Sachin Jambawalikar

Department of Radiology, Columbia University Medical Center, New York, NY.

Keywords

Aged Artificial Intelligence Carcinoma, Non-Small-Cell Lung Female Fibrosis Humans Image Processing, Computer-Assisted Lung Neoplasms Male Middle Aged Neural Networks, Computer Random Forest Tumor Microenvironment

External Resources

View on PubMed Access via DOI PubMed (38978066)

Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.

Abstract

Authors

Keywords

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