Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer
BackgroundMedical imaging is critical in clinical practice, and high value radiological reports can positively assist clinicians. However, there is a lack of methods for determining the value of reports.ObjectiveThe purpose of this study was to establish an ensemble learning classification model usi...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.913806/full |
| _version_ | 1797984058736640000 |
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| author | Wenjuan Liu Xi Zhang Han Lv Jia Li Yawen Liu Zhenghan Yang Xutao Weng Yucong Lin Hong Song Zhenchang Wang |
| author_facet | Wenjuan Liu Xi Zhang Han Lv Jia Li Yawen Liu Zhenghan Yang Xutao Weng Yucong Lin Hong Song Zhenchang Wang |
| author_sort | Wenjuan Liu |
| collection | DOAJ |
| description | BackgroundMedical imaging is critical in clinical practice, and high value radiological reports can positively assist clinicians. However, there is a lack of methods for determining the value of reports.ObjectiveThe purpose of this study was to establish an ensemble learning classification model using natural language processing (NLP) applied to the Chinese free text of radiological reports to determine their value for liver lesion detection in patients with colorectal cancer (CRC).MethodsRadiological reports of upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) were divided into five categories according to the results of liver lesion detection in patients with CRC. The NLP methods including word segmentation, stop word removal, and n-gram language model establishment were applied for each dataset. Then, a word-bag model was built, high-frequency words were selected as features, and an ensemble learning classification model was constructed. Several machine learning methods were applied, including logistic regression (LR), random forest (RF), and so on. We compared the accuracy between priori choosing pertinent word strings and our machine language methodologies.ResultsThe dataset of 2790 patients included CT without contrast (10.2%), CT with/without contrast (73.3%), MRI without contrast (1.8%), and MRI with/without contrast (14.6%). The ensemble learning classification model determined the value of reports effectively, reaching 95.91% in the CT with/without contrast dataset using XGBoost. The logistic regression, random forest, and support vector machine also achieved good classification accuracy, reaching 95.89%, 95.04%, and 95.00% respectively. The results of XGBoost were visualized using a confusion matrix. The numbers of errors in categories I, II and V were very small. ELI5 was used to select important words for each category. Words such as “no abnormality”, “suggest”, “fatty liver”, and “transfer” showed a relatively large degree of positive correlation with classification accuracy. The accuracy based on string pattern search method model was lower than that of machine learning.ConclusionsThe learning classification model based on NLP was an effective tool for determining the value of radiological reports focused on liver lesions. The study made it possible to analyze the value of medical imaging examinations on a large scale. |
| first_indexed | 2024-04-11T06:55:39Z |
| format | Article |
| id | doaj.art-4ef5b71420aa438c813aabedec4e7e47 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T06:55:39Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-4ef5b71420aa438c813aabedec4e7e472022-12-22T04:39:02ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-11-011210.3389/fonc.2022.913806913806Using a classification model for determining the value of liver radiological reports of patients with colorectal cancerWenjuan Liu0Xi Zhang1Han Lv2Jia Li3Yawen Liu4Zhenghan Yang5Xutao Weng6Yucong Lin7Hong Song8Zhenchang Wang9Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaSchool of Biological Science and Medical Engineering, Beihang University, Beijing, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, ChinaBackgroundMedical imaging is critical in clinical practice, and high value radiological reports can positively assist clinicians. However, there is a lack of methods for determining the value of reports.ObjectiveThe purpose of this study was to establish an ensemble learning classification model using natural language processing (NLP) applied to the Chinese free text of radiological reports to determine their value for liver lesion detection in patients with colorectal cancer (CRC).MethodsRadiological reports of upper abdominal computed tomography (CT) and magnetic resonance imaging (MRI) were divided into five categories according to the results of liver lesion detection in patients with CRC. The NLP methods including word segmentation, stop word removal, and n-gram language model establishment were applied for each dataset. Then, a word-bag model was built, high-frequency words were selected as features, and an ensemble learning classification model was constructed. Several machine learning methods were applied, including logistic regression (LR), random forest (RF), and so on. We compared the accuracy between priori choosing pertinent word strings and our machine language methodologies.ResultsThe dataset of 2790 patients included CT without contrast (10.2%), CT with/without contrast (73.3%), MRI without contrast (1.8%), and MRI with/without contrast (14.6%). The ensemble learning classification model determined the value of reports effectively, reaching 95.91% in the CT with/without contrast dataset using XGBoost. The logistic regression, random forest, and support vector machine also achieved good classification accuracy, reaching 95.89%, 95.04%, and 95.00% respectively. The results of XGBoost were visualized using a confusion matrix. The numbers of errors in categories I, II and V were very small. ELI5 was used to select important words for each category. Words such as “no abnormality”, “suggest”, “fatty liver”, and “transfer” showed a relatively large degree of positive correlation with classification accuracy. The accuracy based on string pattern search method model was lower than that of machine learning.ConclusionsThe learning classification model based on NLP was an effective tool for determining the value of radiological reports focused on liver lesions. The study made it possible to analyze the value of medical imaging examinations on a large scale.https://www.frontiersin.org/articles/10.3389/fonc.2022.913806/fullnatural language processingcolorectal cancerliver lesionmedical imaging reportclassification model |
| spellingShingle | Wenjuan Liu Xi Zhang Han Lv Jia Li Yawen Liu Zhenghan Yang Xutao Weng Yucong Lin Hong Song Zhenchang Wang Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer Frontiers in Oncology natural language processing colorectal cancer liver lesion medical imaging report classification model |
| title | Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer |
| title_full | Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer |
| title_fullStr | Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer |
| title_full_unstemmed | Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer |
| title_short | Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer |
| title_sort | using a classification model for determining the value of liver radiological reports of patients with colorectal cancer |
| topic | natural language processing colorectal cancer liver lesion medical imaging report classification model |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.913806/full |
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