Chenyang Liu , Caifeng Xiu , Yongfang Zou , Weina Wu , Yizhi Huang , Lili Wan , Shuping Xu , Bing Han , Haipeng Zhang
{"title":"利用自发拉曼光谱和相干反斯托克斯拉曼光谱与人工智能的宫颈癌诊断模型","authors":"Chenyang Liu , Caifeng Xiu , Yongfang Zou , Weina Wu , Yizhi Huang , Lili Wan , Shuping Xu , Bing Han , Haipeng Zhang","doi":"10.1016/j.saa.2024.125353","DOIUrl":null,"url":null,"abstract":"<div><div>Cervical cancer is the fourth most common cancer worldwide. Histopathology, which is currently considered the gold standard for cervical cancer diagnosis, can be time-consuming and subjective. Therefore, there is an urgent need for a rapid, objective, and non-destructive cervical cancer detection technique. In this study, high-wavenumber spontaneous Raman spectroscopy was used to detect cervical squamous cell carcinoma and normal tissues. The levels of lipids, fatty acids, and proteins in cervical cancerous tissues were found to be higher than those in normal tissues. Raman difference spectroscopy revealed the most significant difference at 2928 cm<sup>−1</sup>. Additionally, a Coherent anti-Stokes Raman spectroscopy (CARS) instrument was employed to enhance the wavenumber signal intensity and sensitivity. The intrinsic relationship between CARS imaging and cervical lesions was established. The CARS images indicated that the intensity of normal cervical squamous cells was zero, whereas the intensities of keratinized and non-keratinized cervical squamous cell carcinoma tissues were significantly higher. Consequently, diagnostic outcomes could be obtained by observing CARS images with the naked eye. Furthermore, the characteristic structure of keratin pearls in keratinized cervical cancer could serve as a marker for subdividing cervical cancer types. Finally, a ConvNeXt network, a machine-learning model built from CARS images, was utilized to classify different types of tissue images. The results indicated a verification accuracy of 100 %, with a loss function of 0.0927. These findings suggest that the diagnostic model established using CARS images could efficiently diagnose cervical cancer, providing novel insights into the pathological diagnosis of this disease.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cervical cancer diagnosis model using spontaneous Raman and Coherent anti-Stokes Raman spectroscopy with artificial intelligence\",\"authors\":\"Chenyang Liu , Caifeng Xiu , Yongfang Zou , Weina Wu , Yizhi Huang , Lili Wan , Shuping Xu , Bing Han , Haipeng Zhang\",\"doi\":\"10.1016/j.saa.2024.125353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Cervical cancer is the fourth most common cancer worldwide. Histopathology, which is currently considered the gold standard for cervical cancer diagnosis, can be time-consuming and subjective. Therefore, there is an urgent need for a rapid, objective, and non-destructive cervical cancer detection technique. In this study, high-wavenumber spontaneous Raman spectroscopy was used to detect cervical squamous cell carcinoma and normal tissues. The levels of lipids, fatty acids, and proteins in cervical cancerous tissues were found to be higher than those in normal tissues. Raman difference spectroscopy revealed the most significant difference at 2928 cm<sup>−1</sup>. Additionally, a Coherent anti-Stokes Raman spectroscopy (CARS) instrument was employed to enhance the wavenumber signal intensity and sensitivity. The intrinsic relationship between CARS imaging and cervical lesions was established. The CARS images indicated that the intensity of normal cervical squamous cells was zero, whereas the intensities of keratinized and non-keratinized cervical squamous cell carcinoma tissues were significantly higher. Consequently, diagnostic outcomes could be obtained by observing CARS images with the naked eye. Furthermore, the characteristic structure of keratin pearls in keratinized cervical cancer could serve as a marker for subdividing cervical cancer types. Finally, a ConvNeXt network, a machine-learning model built from CARS images, was utilized to classify different types of tissue images. The results indicated a verification accuracy of 100 %, with a loss function of 0.0927. These findings suggest that the diagnostic model established using CARS images could efficiently diagnose cervical cancer, providing novel insights into the pathological diagnosis of this disease.</div></div>\",\"PeriodicalId\":433,\"journal\":{\"name\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386142524015191\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SPECTROSCOPY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386142524015191","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SPECTROSCOPY","Score":null,"Total":0}
Cervical cancer diagnosis model using spontaneous Raman and Coherent anti-Stokes Raman spectroscopy with artificial intelligence
Cervical cancer is the fourth most common cancer worldwide. Histopathology, which is currently considered the gold standard for cervical cancer diagnosis, can be time-consuming and subjective. Therefore, there is an urgent need for a rapid, objective, and non-destructive cervical cancer detection technique. In this study, high-wavenumber spontaneous Raman spectroscopy was used to detect cervical squamous cell carcinoma and normal tissues. The levels of lipids, fatty acids, and proteins in cervical cancerous tissues were found to be higher than those in normal tissues. Raman difference spectroscopy revealed the most significant difference at 2928 cm−1. Additionally, a Coherent anti-Stokes Raman spectroscopy (CARS) instrument was employed to enhance the wavenumber signal intensity and sensitivity. The intrinsic relationship between CARS imaging and cervical lesions was established. The CARS images indicated that the intensity of normal cervical squamous cells was zero, whereas the intensities of keratinized and non-keratinized cervical squamous cell carcinoma tissues were significantly higher. Consequently, diagnostic outcomes could be obtained by observing CARS images with the naked eye. Furthermore, the characteristic structure of keratin pearls in keratinized cervical cancer could serve as a marker for subdividing cervical cancer types. Finally, a ConvNeXt network, a machine-learning model built from CARS images, was utilized to classify different types of tissue images. The results indicated a verification accuracy of 100 %, with a loss function of 0.0927. These findings suggest that the diagnostic model established using CARS images could efficiently diagnose cervical cancer, providing novel insights into the pathological diagnosis of this disease.
期刊介绍:
Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (SAA) is an interdisciplinary journal which spans from basic to applied aspects of optical spectroscopy in chemistry, medicine, biology, and materials science.
The journal publishes original scientific papers that feature high-quality spectroscopic data and analysis. From the broad range of optical spectroscopies, the emphasis is on electronic, vibrational or rotational spectra of molecules, rather than on spectroscopy based on magnetic moments.
Criteria for publication in SAA are novelty, uniqueness, and outstanding quality. Routine applications of spectroscopic techniques and computational methods are not appropriate.
Topics of particular interest of Spectrochimica Acta Part A include, but are not limited to:
Spectroscopy and dynamics of bioanalytical, biomedical, environmental, and atmospheric sciences,
Novel experimental techniques or instrumentation for molecular spectroscopy,
Novel theoretical and computational methods,
Novel applications in photochemistry and photobiology,
Novel interpretational approaches as well as advances in data analysis based on electronic or vibrational spectroscopy.