Establishment of a machine learning model for the risk assessment of perineural invasion in head and neck squamous cell carcinoma
Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can a...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
18 May 2023
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| In: |
International journal of molecular sciences
Year: 2023, Volume: 24, Issue: 10, Pages: 1-19 |
| ISSN: | 1422-0067 |
| DOI: | 10.3390/ijms24108938 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/ijms24108938 Verlag, kostenfrei, Volltext: https://www.mdpi.com/1422-0067/24/10/8938 
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| Author Notes: | Christopher Weusthof, Sebastian Burkart, Karl Semmelmayer, Fabian Stögbauer, Bohai Feng, Karam Khorani, Sebastian Bode, Peter Plinkert, Karim Plath and Jochen Hess |
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| 520 | |a Perineural invasion is a prevalent pathological finding in head and neck squamous cell carcinoma and a risk factor for unfavorable survival. An adequate diagnosis of perineural invasion by pathologic examination is limited due to the availability of tumor samples from surgical resection, which can arise in cases of definitive nonsurgical treatment. To address this medical need, we established a random forest prediction model for the risk assessment of perineural invasion, including occult perineural invasion, and characterized distinct cellular and molecular features based on our new and extended classification. RNA sequencing data of head and neck squamous cell carcinoma from The Cancer Genome Atlas were used as a training cohort to identify differentially expressed genes that are associated with perineural invasion. ... | ||
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