Algorithm-based intraoperative diagnosis of liver tumors using infrared spectroscopy Article Swipe
Rimantė Bandzevičiūtė
,
Grit Preuße
,
Sascha Brückmann
,
Alexander Hirle
,
Anne Wedemann
,
Franziska Baenke
,
Marius Distler
,
Carina Riediger
,
Jürgen Weitz
,
Valdas Šablinskas
,
Justinas Čeponkus
,
Gerald Steiner
,
Christian Teske
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-06250-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-06250-z
Liver cancer, including hepatocellular carcinoma (HCC), cholangiocellular carcinoma (CCC), and metastases, presents diagnostic challenges during surgery due to its infiltrative nature. Accurate intraoperative classification and margin assessment are crucial for improving outcomes. Current methods, like frozen section analysis, are time-consuming and subjective, necessitating rapid, objective alternatives. This study assessed fiber-based attenuated total reflection infrared (ATR IR) spectroscopy combined with supervised machine learning for intraoperative liver tumor classification based on a holistic biochemical signature approach. Fresh liver tissue from 69 surgical patients was analyzed using a probe consisting of Ge ATR crystal and silver halide fibers. Supervised algorithms reliably classified normal tissue and tumor subtypes (HCC, CCC, metastases) using cross-validation and independent test sets. Normal liver tissue was distinguished primarily by differences in glycogen content and structural compactness of tumor tissue. Normal and tumor tissues were differentiated with a sensitivity of 0.89 and a specificity of 0.92. The accuracy of spectroscopic classification is 0.90. The three-group classification of tumor subtypes also yielded an average accuracy of 0.90. HCC is characterized by a higher glycogen content compared to CCC and metastases and can be identified spectroscopically with high reliability. CCC showed distinct protein-associated spectral signatures, while metastases exhibited unique profiles reflecting their different origins. In a minority of cases, misclassifications occurred, indicating potential for further refinement. Fiber-based ATR IR spectroscopy in combination with machine learning provides a rapid, objective, and highly accurate intraoperative tool for liver tumor classification. This label-free biochemical approach may enhance surgical precision and reduce recurrence risks across the full range of solid tumor entities.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-06250-z
- https://www.nature.com/articles/s41598-025-06250-z.pdf
- OA Status
- gold
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411494803
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411494803Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-06250-zDigital Object Identifier
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Algorithm-based intraoperative diagnosis of liver tumors using infrared spectroscopyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-20Full publication date if available
- Authors
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Rimantė Bandzevičiūtė, Grit Preuße, Sascha Brückmann, Alexander Hirle, Anne Wedemann, Franziska Baenke, Marius Distler, Carina Riediger, Jürgen Weitz, Valdas Šablinskas, Justinas Čeponkus, Gerald Steiner, Christian TeskeList of authors in order
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https://doi.org/10.1038/s41598-025-06250-zPublisher landing page
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https://www.nature.com/articles/s41598-025-06250-z.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-025-06250-z.pdfDirect OA link when available
- Concepts
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Hepatocellular carcinoma, Glycogen, Pathology, Medicine, Cancer, Cancer research, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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29Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.HCC | 167 |
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| abstract_inverted_index.0.92. | 146 |
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| abstract_inverted_index.using | 84, 108 |
| abstract_inverted_index.while | 194 |
| abstract_inverted_index.(CCC), | 9 |
| abstract_inverted_index.(HCC), | 6 |
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| abstract_inverted_index.cases, | 207 |
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| abstract_inverted_index.machine | 61, 222 |
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| abstract_inverted_index.tissues | 134 |
| abstract_inverted_index.yielded | 161 |
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| abstract_inverted_index.accurate | 230 |
| abstract_inverted_index.analyzed | 83 |
| abstract_inverted_index.approach | 240 |
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| abstract_inverted_index.compared | 175 |
| abstract_inverted_index.distinct | 190 |
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| abstract_inverted_index.spectral | 192 |
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| abstract_inverted_index.potential | 211 |
| abstract_inverted_index.precision | 244 |
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| abstract_inverted_index.signature | 73 |
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| abstract_inverted_index.algorithms | 97 |
| abstract_inverted_index.assessment | 27 |
| abstract_inverted_index.attenuated | 51 |
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| abstract_inverted_index.spectroscopically | 184 |
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| abstract_inverted_index.protein-associated | 191 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 13 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.16644232 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |