Development and validation of a multivariable prediction model for the identification of occult lymph node metastasis in oral squamous cell carcinoma.
Details
Serval ID
serval:BIB_F50D846E2457
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Development and validation of a multivariable prediction model for the identification of occult lymph node metastasis in oral squamous cell carcinoma.
Journal
Head & neck
ISSN
1097-0347 (Electronic)
ISSN-L
1043-3074
Publication state
Published
Issued date
08/2020
Peer-reviewed
Oui
Volume
42
Number
8
Pages
1811-1820
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Abstract
There have been few recent advances in the identification of occult lymph node metastases (OLNM) in oral squamous cell carcinoma (OSCC). This study aimed to develop, compare, and validate several machine learning models to predict OLNM in clinically N0 (cN0) OSCC.
The biomarkers CD31 and PROX1 were combined with relevant histological parameters and evaluated on a training cohort (n = 56) using four different state-of-the-art machine learning models. Next, the optimized models were tested on an external validation cohort (n = 112) of early-stage (T1-2 N0) OSCC.
The random forest (RF) model gave the best overall performance (area under the curve = 0.89 [95% CI = 0.8, 0.98]) and accuracy (0.88 [95% CI = 0.8, 0.93]) while maintaining a negative predictive value >95%.
We provide a new clinical decision algorithm incorporating risk stratification by an RF model that could significantly improve the management of patients with early-stage OSCC.
The biomarkers CD31 and PROX1 were combined with relevant histological parameters and evaluated on a training cohort (n = 56) using four different state-of-the-art machine learning models. Next, the optimized models were tested on an external validation cohort (n = 112) of early-stage (T1-2 N0) OSCC.
The random forest (RF) model gave the best overall performance (area under the curve = 0.89 [95% CI = 0.8, 0.98]) and accuracy (0.88 [95% CI = 0.8, 0.93]) while maintaining a negative predictive value >95%.
We provide a new clinical decision algorithm incorporating risk stratification by an RF model that could significantly improve the management of patients with early-stage OSCC.
Keywords
biomarkers, clinical decision model, machine learning, occult lymph node metastasis, oral squamous cell carcinoma, prognosis
Pubmed
Web of science
Create date
17/02/2020 17:48
Last modification date
13/02/2021 7:26
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