Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging
The most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used to d...
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2024-10-01
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| author | Joseph-Hang Leung Riya Karmakar Arvind Mukundan Pacharasak Thongsit Meei-Maan Chen Wen-Yen Chang Hsiang-Chen Wang |
| author_facet | Joseph-Hang Leung Riya Karmakar Arvind Mukundan Pacharasak Thongsit Meei-Maan Chen Wen-Yen Chang Hsiang-Chen Wang |
| author_sort | Joseph-Hang Leung |
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| description | The most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used to diagnose and classify breast cancer using traditional RGB images that analyze the images only in three-color channels. Nevertheless, hyperspectral imaging (HSI) is a pioneering non-destructive testing (NDT) image-processing technique that can overcome the disadvantages of traditional image processing which analyzes the images in a wide-spectrum band. Eight studies were selected for systematic diagnostic test accuracy (DTA) analysis based on the results of the Quadas-2 tool. Each of these studies’ techniques is categorized according to the ethnicity of the data, the methodology employed, the wavelength that was used, the type of cancer diagnosed, and the year of publication. A Deeks’ funnel chart, forest charts, and accuracy plots were created. The results were statistically insignificant, and there was no heterogeneity among these studies. The methods and wavelength bands that were used with HSI technology to detect breast cancer provided high sensitivity, specificity, and accuracy. The meta-analysis of eight studies on breast cancer diagnosis using HSI methods reported average sensitivity, specificity, and accuracy of 78%, 89%, and 87%, respectively. The highest sensitivity and accuracy were achieved with SVM (95%), while CNN methods were the most commonly used but had lower sensitivity (65.43%). Statistical analyses, including meta-regression and Deeks’ funnel plots, showed no heterogeneity among the studies and highlighted the evolving performance of HSI techniques, especially after 2019. |
| format | Article |
| id | doaj-art-92bcb63043704a9b83f8972fc32a41d9 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-92bcb63043704a9b83f8972fc32a41d92024-11-26T17:51:47ZengMDPI AGBioengineering2306-53542024-10-011111106010.3390/bioengineering11111060Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral ImagingJoseph-Hang Leung0Riya Karmakar1Arvind Mukundan2Pacharasak Thongsit3Meei-Maan Chen4Wen-Yen Chang5Hsiang-Chen Wang6Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City 600566, TaiwanDepartment of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi City 62102, TaiwanDepartment of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi City 62102, TaiwanFaculty of Mechanical Engineering, King Mongkut’s University of Technology North Bangkok, Pracharat 1 Road, Wongsawang, Bangsue, Bangkok 10800, ThailandCenter for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, TaiwanDepartment of General Surgery, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, TaiwanDepartment of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi City 62102, TaiwanThe most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used to diagnose and classify breast cancer using traditional RGB images that analyze the images only in three-color channels. Nevertheless, hyperspectral imaging (HSI) is a pioneering non-destructive testing (NDT) image-processing technique that can overcome the disadvantages of traditional image processing which analyzes the images in a wide-spectrum band. Eight studies were selected for systematic diagnostic test accuracy (DTA) analysis based on the results of the Quadas-2 tool. Each of these studies’ techniques is categorized according to the ethnicity of the data, the methodology employed, the wavelength that was used, the type of cancer diagnosed, and the year of publication. A Deeks’ funnel chart, forest charts, and accuracy plots were created. The results were statistically insignificant, and there was no heterogeneity among these studies. The methods and wavelength bands that were used with HSI technology to detect breast cancer provided high sensitivity, specificity, and accuracy. The meta-analysis of eight studies on breast cancer diagnosis using HSI methods reported average sensitivity, specificity, and accuracy of 78%, 89%, and 87%, respectively. The highest sensitivity and accuracy were achieved with SVM (95%), while CNN methods were the most commonly used but had lower sensitivity (65.43%). Statistical analyses, including meta-regression and Deeks’ funnel plots, showed no heterogeneity among the studies and highlighted the evolving performance of HSI techniques, especially after 2019.https://www.mdpi.com/2306-5354/11/11/1060hyperspectral imagingbreast cancercomputer-aided detectionsystematic meta-analysisDeeks’ funnel chartdiagnostic test accuracy |
| spellingShingle | Joseph-Hang Leung Riya Karmakar Arvind Mukundan Pacharasak Thongsit Meei-Maan Chen Wen-Yen Chang Hsiang-Chen Wang Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging Bioengineering hyperspectral imaging breast cancer computer-aided detection systematic meta-analysis Deeks’ funnel chart diagnostic test accuracy |
| title | Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging |
| title_full | Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging |
| title_fullStr | Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging |
| title_full_unstemmed | Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging |
| title_short | Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging |
| title_sort | systematic meta analysis of computer aided detection of breast cancer using hyperspectral imaging |
| topic | hyperspectral imaging breast cancer computer-aided detection systematic meta-analysis Deeks’ funnel chart diagnostic test accuracy |
| url | https://www.mdpi.com/2306-5354/11/11/1060 |
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