Artificial Intelligence in Lung Cancer Pathology Image Analysis
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-a...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-10-01
|
Series: | Cancers |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-6694/11/11/1673 |
id |
doaj-689c1ae99af04b0d8837fabedb1c2e77 |
---|---|
record_format |
Article |
spelling |
doaj-689c1ae99af04b0d8837fabedb1c2e772020-11-24T21:50:44ZengMDPI AGCancers2072-66942019-10-011111167310.3390/cancers11111673cancers11111673Artificial Intelligence in Lung Cancer Pathology Image AnalysisShidan Wang0Donghan M. Yang1Ruichen Rong2Xiaowei Zhan3Junya Fujimoto4Hongyu Liu5John Minna6Ignacio Ivan Wistuba7Yang Xie8Guanghua Xiao9Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USAQuantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USAQuantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USAQuantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USADepartment of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USAQuantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USAHarold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USADepartment of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USAQuantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USAQuantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USAObjective: 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.https://www.mdpi.com/2072-6694/11/11/1673lung cancerdeep learningpathology imagecomputer-aided diagnosisdigital pathologywhole-slide imaging |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shidan Wang Donghan M. Yang Ruichen Rong Xiaowei Zhan Junya Fujimoto Hongyu Liu John Minna Ignacio Ivan Wistuba Yang Xie Guanghua Xiao |
spellingShingle |
Shidan Wang Donghan M. Yang Ruichen Rong Xiaowei Zhan Junya Fujimoto Hongyu Liu John Minna Ignacio Ivan Wistuba Yang Xie Guanghua Xiao Artificial Intelligence in Lung Cancer Pathology Image Analysis Cancers lung cancer deep learning pathology image computer-aided diagnosis digital pathology whole-slide imaging |
author_facet |
Shidan Wang Donghan M. Yang Ruichen Rong Xiaowei Zhan Junya Fujimoto Hongyu Liu John Minna Ignacio Ivan Wistuba Yang Xie Guanghua Xiao |
author_sort |
Shidan Wang |
title |
Artificial Intelligence in Lung Cancer Pathology Image Analysis |
title_short |
Artificial Intelligence in Lung Cancer Pathology Image Analysis |
title_full |
Artificial Intelligence in Lung Cancer Pathology Image Analysis |
title_fullStr |
Artificial Intelligence in Lung Cancer Pathology Image Analysis |
title_full_unstemmed |
Artificial Intelligence in Lung Cancer Pathology Image Analysis |
title_sort |
artificial intelligence in lung cancer pathology image analysis |
publisher |
MDPI AG |
series |
Cancers |
issn |
2072-6694 |
publishDate |
2019-10-01 |
description |
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. |
topic |
lung cancer deep learning pathology image computer-aided diagnosis digital pathology whole-slide imaging |
url |
https://www.mdpi.com/2072-6694/11/11/1673 |
work_keys_str_mv |
AT shidanwang artificialintelligenceinlungcancerpathologyimageanalysis AT donghanmyang artificialintelligenceinlungcancerpathologyimageanalysis AT ruichenrong artificialintelligenceinlungcancerpathologyimageanalysis AT xiaoweizhan artificialintelligenceinlungcancerpathologyimageanalysis AT junyafujimoto artificialintelligenceinlungcancerpathologyimageanalysis AT hongyuliu artificialintelligenceinlungcancerpathologyimageanalysis AT johnminna artificialintelligenceinlungcancerpathologyimageanalysis AT ignacioivanwistuba artificialintelligenceinlungcancerpathologyimageanalysis AT yangxie artificialintelligenceinlungcancerpathologyimageanalysis AT guanghuaxiao artificialintelligenceinlungcancerpathologyimageanalysis |
_version_ |
1725881874875678720 |