Leung Joseph-Hang, Karmakar Riya, Mukundan Arvind, Thongsit Pacharasak, Chen Meei-Maan, Chang Wen-Yen, Wang Hsiang-Chen
Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City 600566, Taiwan.
Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi City 62102, Taiwan.
Bioengineering (Basel). 2024 Oct 24;11(11):1060. doi: 10.3390/bioengineering11111060.
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.
世界上最常见的癌症是乳腺癌，全球病例超过50万例。乳腺癌的检测机制依赖于内镜医师，需要熟练的病理学家。然而，近年来，许多计算机辅助诊断（CAD）已被用于使用仅在三个颜色通道中分析图像的传统RGB图像来诊断和分类乳腺癌。尽管如此，高光谱成像（HSI）是一种开创性的无损检测（NDT）图像处理技术，它可以克服传统图像处理在宽光谱带中分析图像的缺点。根据Quadas-2工具的结果，选择了八项研究进行系统的诊断测试准确性（DTA）分析。这些研究中的每一项技术都根据数据的种族、所采用的方法、使用的波长、诊断的癌症类型和发表年份进行分类。创建了Deeks漏斗图、森林图和准确性图。结果在统计学上不显著，这些研究之间没有异质性。使用HSI技术检测乳腺癌所采用的方法和波段提供了高灵敏度、特异性和准确性。对八项使用HSI方法诊断乳腺癌的研究进行的荟萃分析报告，平均灵敏度、特异性和准确性分别为78%、89%和87%。支持向量机（SVM）实现了最高的灵敏度和准确性（95%），而卷积神经网络（CNN）方法是最常用的，但灵敏度较低（65.43%）。包括荟萃回归和Deeks漏斗图在内的统计分析表明，这些研究之间没有异质性，并突出了HSI技术不断发展的性能，特别是在2019年之后。
