Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5 Άγιος Νικόλαος
Κρήτη 72100
00302841026182
00306932607174
alsfakia@gmail.com

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Κυριακή 18 Σεπτεμβρίου 2022

Radiomics based on magnetic resonance imaging for preoperative prediction of lymph node metastasis in head and neck cancer: Machine learning study

alexandrossfakianakis shared this article with you from Inoreader

Abstract

Background

In this study, we use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics approach for evaluation of cervical lymph node (CLN) status.

Methods

After collecting all patients' MRI images, we used CLN radiomic features, the apparent diffusion coefficients (ADC) from diffusion-weighted imaging (DWI), and lymph node short diameter of the CLN to build MRI model to predict the status of the CLN.

Results

One hundred and twenty cases met inclusion criteria. The MRI model including the radiomic features, ADC, and lymph node size of the CLN achieved better performance for CLN status prediction with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.83.

Conclusions

The multiomic signature of MRI radiomics, ADC, and lymph node size of CLNs has high predictive value for the status of CLNs. This model has provided scientific value to the surgeon regarding cervical lymph nodes before surgery.

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