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Cancers
Volume 11
Issue 11
10.3390/cancers11111673
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Review

Artificial Intelligence in Lung Cancer Pathology Image Analysis

by
Shidan Wang
1,
Donghan M. Yang
1,
Ruichen Rong
1,
Xiaowei Zhan
1,
Junya Fujimoto
2,
Hongyu Liu
1,
John Minna
3,4,5,
Ignacio Ivan Wistuba
2,
Yang Xie
1,3,6 and
Guanghua Xiao
1,3,6,*
1
Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
2
Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
3
Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
4
Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX 75390, USA
5
Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
6
Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
*
Author to whom correspondence should be addressed.
Cancers 2019, 11(11), 1673; https://doi.org/10.3390/cancers11111673
Received: 23 September 2019 / Revised: 17 October 2019 / Accepted: 21 October 2019 / Published: 28 October 2019
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Abstract

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation. View Full-Text
Keywords: lung cancer; deep learning; pathology image; computer-aided diagnosis; digital pathology; whole-slide imaging lung cancer; deep learning; pathology image; computer-aided diagnosis; digital pathology; whole-slide imaging
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MDPI and ACS Style

Wang, S.; Yang, D.M.; Rong, R.; Zhan, X.; Fujimoto, J.; Liu, H.; Minna, J.; Wistuba, I.I.; Xie, Y.; Xiao, G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers 2019, 11, 1673. https://doi.org/10.3390/cancers11111673

AMA Style

Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers. 2019; 11(11):1673. https://doi.org/10.3390/cancers11111673

Chicago/Turabian Style

Wang, Shidan; Yang, Donghan M.; Rong, Ruichen; Zhan, Xiaowei; Fujimoto, Junya; Liu, Hongyu; Minna, John; Wistuba, Ignacio I.; Xie, Yang; Xiao, Guanghua. 2019. "Artificial Intelligence in Lung Cancer Pathology Image Analysis" Cancers 11, no. 11: 1673. https://doi.org/10.3390/cancers11111673

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