Label-free differentiation of human pancreatic cancer, pancreatitis, and normal pancreatic tissue by molecular spectroscopy
Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining...
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| Published in: | Journal of biomedical optics Vol. 27; no. 7; p. 075001 |
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| Format: | Journal Article |
| Language: | English |
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| Abstract | Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding.
Aim: We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner.
Approach: Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using a MATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leave-one-out method. Validation of the algorithm was accomplished with 13 independent test samples.
Results: Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm.
Conclusion: IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications. |
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| AbstractList | Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding.
Aim: We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner.
Approach: Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using a MATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leave-one-out method. Validation of the algorithm was accomplished with 13 independent test samples.
Results: Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm.
Conclusion: IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications. Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding. We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner. Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using aMATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leaveone- out method. Validation of the algorithm was accomplished with 13 independent test samples. Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm. IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications. SignificancePancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding.AimWe aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner.ApproachTwenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using aMATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leaveone- out method. Validation of the algorithm was accomplished with 13 independent test samples.ResultsReclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm.ConclusionIR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications. Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding. Aim: We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner. Approach: Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using a MATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leave-one-out method. Validation of the algorithm was accomplished with 13 independent test samples. Results: Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm. Conclusion: IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications. Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding.Aim: We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner.Approach: Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using a MATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leave-one-out method. Validation of the algorithm was accomplished with 13 independent test samples.Results: Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm.Conclusion: IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications. |
| Author | Weitz, Jürgen Uckermann, Ortrud Liedel, Katja Kahlert, Christoph Teske, Christian Welsch, Thilo Steiner, Gerald |
| Author_xml | – sequence: 1 givenname: Christian orcidid: 0000-0002-6049-4765 surname: Teske fullname: Teske, Christian email: christian.teske@ukdd.de organization: National Center for Tumor Diseases (NCT/UCC), Dresden, Germany – sequence: 2 givenname: Christoph surname: Kahlert fullname: Kahlert, Christoph email: Christoph.kahlert@uniklinikum-dresden.de organization: National Center for Tumor Diseases (NCT/UCC), Dresden, Germany – sequence: 3 givenname: Thilo surname: Welsch fullname: Welsch, Thilo email: Thilo.welsch@mailbox.org organization: National Center for Tumor Diseases (NCT/UCC), Dresden, Germany – sequence: 4 givenname: Katja surname: Liedel fullname: Liedel, Katja email: Katja.Liedel@ukdd.de organization: National Center for Tumor Diseases (NCT/UCC), Dresden, Germany – sequence: 5 givenname: Jürgen surname: Weitz fullname: Weitz, Jürgen email: juergen.weitz@ukdd.de organization: National Center for Tumor Diseases (NCT/UCC), Dresden, Germany – sequence: 6 givenname: Ortrud surname: Uckermann fullname: Uckermann, Ortrud email: ortrud.uckermann@ukdd.de organization: University Hospital Carl Gustav Carus, Department of Neurosurgery, Dresden, Germany – sequence: 7 givenname: Gerald surname: Steiner fullname: Steiner, Gerald email: gerald.steiner@tu-dresden.de organization: Technische Universität Dresden, Department of Anaesthesiology and Critical Care Medicine, Clinical Sensoring and Monitoring, Faculty of Medicine, Dresden, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36399853$$D View this record in MEDLINE/PubMed |
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| Snippet | Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for... Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease... SignificancePancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for... Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for... |
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| SubjectTerms | Adenocarcinoma Algorithms Brain cancer Cancer Cancer therapies Carcinoma, Pancreatic Ductal - diagnostic imaging Carcinoma, Pancreatic Ductal - pathology Classification Cluster analysis Cryopreservation Data processing Datasets Differentiation (biology) Discriminant analysis Humans Inflammation Infrared spectroscopy Labels Malignancy Mercury cadmium telluride Molecular spectroscopy Neuroendocrine tumors Pancreas - pathology Pancreatic cancer Pancreatic Neoplasms Pancreatic Neoplasms - diagnostic imaging Pancreatic Neoplasms - pathology Pancreatitis Pancreatitis - diagnosis Pancreatitis - pathology Principal components analysis Reclassification Signal to noise ratio Spectra Spectrum analysis Spectrum Analysis - methods Surgery Tissues Training |
| Title | Label-free differentiation of human pancreatic cancer, pancreatitis, and normal pancreatic tissue by molecular spectroscopy |
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