| Autor: |
Julia Gehrmann, Devina Johanna Soenarto, Kevin Hidayat, Maria Beyer, Lars Quakulinski, Samer Alkarkoukly, Scarlett Berressem, Anna Gundert, Michael Butler, Ana Grönke, Simon Lennartz, Thorsten Persigehl, Thomas Zander, Oya Beyan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Medicine, Vol 11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-858X |
| DOI: |
10.3389/fmed.2024.1396459 |
| Popis: |
The Cancer of Unknown Primary (CUP) syndrome is characterized by identifiable metastases while the primary tumor remains hidden. In recent years, various data-driven approaches have been suggested to predict the location of the primary tumor (LOP) in CUP patients promising improved diagnosis and outcome. These LOP prediction approaches use high-dimensional input data like images or genetic data. However, leveraging such data is challenging, resource-intensive and therefore a potential translational barrier. Instead of using high-dimensional data, we analyzed the LOP prediction performance of low-dimensional data from routine medical care. With our findings, we show that such low-dimensional routine clinical information suffices as input data for tree-based LOP prediction models. The best model reached a mean Accuracy of 94% and a mean Matthews correlation coefficient (MCC) score of 0.92 in 10-fold nested cross-validation (NCV) when distinguishing four types of cancer. When considering eight types of cancer, this model achieved a mean Accuracy of 85% and a mean MCC score of 0.81. This is comparable to the performance achieved by approaches using high-dimensional input data. Additionally, the distribution pattern of metastases appears to be important information in predicting the LOP. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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