Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections
Abstract The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
|
| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-07111-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850179063129309184 |
|---|---|
| author | Charlotte Delrue Mattias Hofmans Jo Van Dorpe Malaïka Van der Linden Zen Van Gaever Tessa Kerre Marijn M. Speeckaert Sander De Bruyne |
| author_facet | Charlotte Delrue Mattias Hofmans Jo Van Dorpe Malaïka Van der Linden Zen Van Gaever Tessa Kerre Marijn M. Speeckaert Sander De Bruyne |
| author_sort | Charlotte Delrue |
| collection | DOAJ |
| description | Abstract The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800–900 cm–1 region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881–0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories. |
| format | Article |
| id | doaj-art-5fd99ef4e90f467dbe35577c4c98a145 |
| institution | OA Journals |
| issn | 2399-3642 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-5fd99ef4e90f467dbe35577c4c98a1452025-08-20T02:18:35ZengNature PortfolioCommunications Biology2399-36422024-10-017111110.1038/s42003-024-07111-7Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sectionsCharlotte Delrue0Mattias Hofmans1Jo Van Dorpe2Malaïka Van der Linden3Zen Van Gaever4Tessa Kerre5Marijn M. Speeckaert6Sander De Bruyne7Department of Nephrology, Department of Internal Medicine and Pediatrics, Ghent University HospitalDepartment of Diagnostic Sciences, Ghent UniversityDepartment of Pathology, Ghent University HospitalDepartment of Pathology, Ghent University HospitalData & AI, DelawareDepartment of Hematology, Department of Internal Medicine and Pediatrics, Ghent University HospitalDepartment of Nephrology, Department of Internal Medicine and Pediatrics, Ghent University HospitalDepartment of Diagnostic Sciences, Ghent UniversityAbstract The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800–900 cm–1 region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881–0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories.https://doi.org/10.1038/s42003-024-07111-7 |
| spellingShingle | Charlotte Delrue Mattias Hofmans Jo Van Dorpe Malaïka Van der Linden Zen Van Gaever Tessa Kerre Marijn M. Speeckaert Sander De Bruyne Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections Communications Biology |
| title | Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections |
| title_full | Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections |
| title_fullStr | Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections |
| title_full_unstemmed | Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections |
| title_short | Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections |
| title_sort | innovative label free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections |
| url | https://doi.org/10.1038/s42003-024-07111-7 |
| work_keys_str_mv | AT charlottedelrue innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections AT mattiashofmans innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections AT jovandorpe innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections AT malaikavanderlinden innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections AT zenvangaever innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections AT tessakerre innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections AT marijnmspeeckaert innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections AT sanderdebruyne innovativelabelfreelymphomadiagnosisusinginfraredspectroscopyandmachinelearningontissuesections |