Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images
Abstract EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently refl...
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Wiley
2024-11-01
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| Series: | The Journal of Pathology: Clinical Research |
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| Online Access: | https://doi.org/10.1002/2056-4538.70004 |
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| author | Jun Hyeong Park June Hyuck Lim Seonhwa Kim Chul‐Ho Kim Jeong‐Seok Choi Jun Hyeok Lim Lucia Kim Jae Won Chang Dongil Park Myung‐won Lee Sup Kim Il‐Seok Park Seung Hoon Han Eun Shin Jin Roh Jaesung Heo |
| author_facet | Jun Hyeong Park June Hyuck Lim Seonhwa Kim Chul‐Ho Kim Jeong‐Seok Choi Jun Hyeok Lim Lucia Kim Jae Won Chang Dongil Park Myung‐won Lee Sup Kim Il‐Seok Park Seung Hoon Han Eun Shin Jin Roh Jaesung Heo |
| author_sort | Jun Hyeong Park |
| collection | DOAJ |
| description | Abstract EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch‐level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple‐instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch‐masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607–0.7720) and an area under the precision‐recall curve of 0.8391 (0.8326–0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next‐generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans. |
| format | Article |
| id | doaj-art-6a9c3887e8b440d1b1ea3a8bc4bed933 |
| institution | OA Journals |
| issn | 2056-4538 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
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| series | The Journal of Pathology: Clinical Research |
| spelling | doaj-art-6a9c3887e8b440d1b1ea3a8bc4bed9332025-08-20T01:52:45ZengWileyThe Journal of Pathology: Clinical Research2056-45382024-11-01106n/an/a10.1002/2056-4538.70004Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide imagesJun Hyeong Park0June Hyuck Lim1Seonhwa Kim2Chul‐Ho Kim3Jeong‐Seok Choi4Jun Hyeok Lim5Lucia Kim6Jae Won Chang7Dongil Park8Myung‐won Lee9Sup Kim10Il‐Seok Park11Seung Hoon Han12Eun Shin13Jin Roh14Jaesung Heo15Department of Radiation Oncology Ajou University School of Medicine Suwon Republic of KoreaDepartment of Radiation Oncology Ajou University School of Medicine Suwon Republic of KoreaDepartment of Radiation Oncology Ajou University School of Medicine Suwon Republic of KoreaDepartment of Otolaryngology Ajou University School of Medicine Suwon Republic of KoreaDepartment of Otorhinolaryngology‐Head and Neck Surgery Inha University College of Medicine Incheon Republic of KoreaDivision of Pulmonology, Department of Internal Medicine Inha University College of Medicine Incheon Republic of KoreaDepartment of Pathology Inha University College of Medicine Incheon Republic of KoreaDepartment of Otolaryngology‐Head and Neck Surgery Chungnam National University Hospital Daejeon Republic of KoreaDivision of Pulmonary, Allergy and Critical Care Medicine, Critical Care Medicine, Department of Internal Medicine Chungnam National University Hospital Daejeon Republic of KoreaDivision of Hematology and Oncology, Department of Internal Medicine Chungnam National University Hospital Daejeon Republic of KoreaDepartment of Radiation Oncology Chungnam National University Hospital Daejeon Republic of KoreaDepartment of Otorhinolaryngology‐Head and Neck Surgery Hallym University Dontan Sacred Heart Hospital, Hallym University College of Medicine Hwaseong Republic of KoreaDepartment of Otorhinolaryngology‐Head and Neck Surgery Hallym University Dontan Sacred Heart Hospital, Hallym University College of Medicine Hwaseong Republic of KoreaDepartment of Pathology, Dongtan Sacred Heart Hospital Hallym University College of Medicine Hwaseong Republic of KoreaDepartment of Pathology Ajou University School of Medicine Suwon Republic of KoreaDepartment of Radiation Oncology Ajou University School of Medicine Suwon Republic of KoreaAbstract EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch‐level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple‐instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch‐masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607–0.7720) and an area under the precision‐recall curve of 0.8391 (0.8326–0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next‐generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.https://doi.org/10.1002/2056-4538.70004EGFRwhole‐slide image analysisdeep learning in histopathologymultiple‐instance learning |
| spellingShingle | Jun Hyeong Park June Hyuck Lim Seonhwa Kim Chul‐Ho Kim Jeong‐Seok Choi Jun Hyeok Lim Lucia Kim Jae Won Chang Dongil Park Myung‐won Lee Sup Kim Il‐Seok Park Seung Hoon Han Eun Shin Jin Roh Jaesung Heo Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images The Journal of Pathology: Clinical Research EGFR whole‐slide image analysis deep learning in histopathology multiple‐instance learning |
| title | Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images |
| title_full | Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images |
| title_fullStr | Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images |
| title_full_unstemmed | Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images |
| title_short | Deep learning‐based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images |
| title_sort | deep learning based analysis of egfr mutation prevalence in lung adenocarcinoma h e whole slide images |
| topic | EGFR whole‐slide image analysis deep learning in histopathology multiple‐instance learning |
| url | https://doi.org/10.1002/2056-4538.70004 |
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