Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma
Abstract Pancreatic ductal adenocarcinoma is currently the 12th most frequent form of cancer worldwide, characterized by a very low 5-year survival rate. Although several therapeutic approaches have been proposed to treat this form of pancreatic cancer, surgical resection is still commonly recognize...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-98122-9 |
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| author | Gianmarco Lazzini Raffele Gaeta Luca Emanuele Pollina Annalisa Comandatore Niccolò Furbetta Luca Morelli Mario D’Acunto |
| author_facet | Gianmarco Lazzini Raffele Gaeta Luca Emanuele Pollina Annalisa Comandatore Niccolò Furbetta Luca Morelli Mario D’Acunto |
| author_sort | Gianmarco Lazzini |
| collection | DOAJ |
| description | Abstract Pancreatic ductal adenocarcinoma is currently the 12th most frequent form of cancer worldwide, characterized by a very low 5-year survival rate. Although several therapeutic approaches have been proposed to treat this form of pancreatic cancer, surgical resection is still commonly recognized as the most effective technique to slow down the disease progression and maximize the 5-year survival rate. Analogously, one critical issue is the ability of current diagnostic methodologies to distinguish between irregular growth of the tumor mass and surrounding inflammatory tissues. In this pilot study, we apply Raman spectroscopy, supported by a series of machine learning techniques, to distinguish among healthy, pancreatitis and ductal adenocarcinoma tissues, respectively, for a total of 15 cases. Raman spectroscopy is a label-free, non-destructive spectral technique exploiting Raman scattering. In turn, by applying a combination of principal component analysis and random forest classifier on the Raman spectral dataset, we achieved a maximum accuracy of up to $$\sim$$ ∼ 96%. Our findings clearly indicate that Raman spectroscopy could become a powerful spectral technique to support pathologists in improving pancreatic cancer diagnosis. |
| format | Article |
| id | doaj-art-0e428bafd47e4505b7d104ee74a34eec |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0e428bafd47e4505b7d104ee74a34eec2025-08-20T03:18:32ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-98122-9Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinomaGianmarco Lazzini0Raffele Gaeta1Luca Emanuele Pollina2Annalisa Comandatore3Niccolò Furbetta4Luca Morelli5Mario D’Acunto6CNR-IBF, Istituto di Biofisica Consiglio Nazionale delle RicercheSecond Division of Surgical Pathology, University Hospital of PisaSecond Division of Surgical Pathology, University Hospital of PisaGeneral Surgery Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of PisaDepartment of Surgery, Amsterdam UMC, Location Vrije UniversiteitDepartment of Surgery, Amsterdam UMC, Location Vrije UniversiteitCNR-IBF, Istituto di Biofisica Consiglio Nazionale delle RicercheAbstract Pancreatic ductal adenocarcinoma is currently the 12th most frequent form of cancer worldwide, characterized by a very low 5-year survival rate. Although several therapeutic approaches have been proposed to treat this form of pancreatic cancer, surgical resection is still commonly recognized as the most effective technique to slow down the disease progression and maximize the 5-year survival rate. Analogously, one critical issue is the ability of current diagnostic methodologies to distinguish between irregular growth of the tumor mass and surrounding inflammatory tissues. In this pilot study, we apply Raman spectroscopy, supported by a series of machine learning techniques, to distinguish among healthy, pancreatitis and ductal adenocarcinoma tissues, respectively, for a total of 15 cases. Raman spectroscopy is a label-free, non-destructive spectral technique exploiting Raman scattering. In turn, by applying a combination of principal component analysis and random forest classifier on the Raman spectral dataset, we achieved a maximum accuracy of up to $$\sim$$ ∼ 96%. Our findings clearly indicate that Raman spectroscopy could become a powerful spectral technique to support pathologists in improving pancreatic cancer diagnosis.https://doi.org/10.1038/s41598-025-98122-9Raman spectroscopyPancreatic ductal adenocarcinomaGaussian Naive-BayesRandom forest classifierSPectral SELection |
| spellingShingle | Gianmarco Lazzini Raffele Gaeta Luca Emanuele Pollina Annalisa Comandatore Niccolò Furbetta Luca Morelli Mario D’Acunto Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma Scientific Reports Raman spectroscopy Pancreatic ductal adenocarcinoma Gaussian Naive-Bayes Random forest classifier SPectral SELection |
| title | Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma |
| title_full | Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma |
| title_fullStr | Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma |
| title_full_unstemmed | Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma |
| title_short | Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma |
| title_sort | raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma |
| topic | Raman spectroscopy Pancreatic ductal adenocarcinoma Gaussian Naive-Bayes Random forest classifier SPectral SELection |
| url | https://doi.org/10.1038/s41598-025-98122-9 |
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