Machine learning–based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma



To develop and compare several machine learning models to predict occult cervical lymph node (LN) metastasis in early-stage oral tongue squamous cell cancer (OTSCC) from preoperative MRI texture features.

Materials and methods

We retrospectively enrolled 116 patients with early-stage OTSCC (cT1-2N0) who had been surgically treated by tumor excision and elective neck dissection (END). For each patient, we extracted 86 texture features from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (ceT1WI), respectively. Dimension reduction was performed in three consecutive steps: reproducibility analysis, collinearity analysis, and information gain algorithm. Models were created using six machine learning methods, including logistic regression (LR), random forest (RF), naïve Bayes (NB), support vector machine (SVM), AdaBoost, and neural network (NN). Their performance was assessed using tenfold cross-validation.


Occult LN metastasis was pathologically detected in 42.2% (49/116) of the patients. No significant association was identified between node status and patients’ gender, age, or clinical T stage. Dimension reduction steps selected 6 texture features. The NB model gave the best overall performance, which correctly classified the nodal status in 74.1% (86/116) of the carcinomas, with an AUC of 0.802.


Machine learning–based MRI texture analysis offers a feasible tool for preoperative prediction of occult cervical node metastasis in early-stage OTSCC.

Key Points

• A machine learningbased MRI texture analysis approach was adopted to predict occult cervical node metastasis in early-stage OTSCC with no evidence of node involvement on conventional images.

• Six texture features from T2WI and ceT1WI of preoperative MRI were selected to construct the predictive model.

• After comparing six machine learning methods, naïve Bayes (NB) achieved the best performance by correctly identifying the node status in 74.1% of the patients, using tenfold cross-validation.

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Area under the curve


Contrast-enhanced T1-weighted imaging


Depth of invasion


Elective neck dissection


Gray-level co-occurrence matrix


Gray-level dependence matrix


Gray-level run-length matrix


Gray-level size zone matrix


Head and neck squamous cell carcinoma


Intraclass correlation coefficients


Lymph node


Logistic regression


Magnetic resonance imaging


Naïve Bayes


Neural network


Oral tongue squamous cell carcinoma


Random forest


Receiver operator characteristic


Region of interests


Support vector machine


T2-Weighted imaging


Echo time


Repetition time


Volume of interest


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This study has received funding by National Scientific Foundation of China (Grant number: 91859202, 81771901, to Xiaofeng Tao). Youth Medical Talents-Medical Imaging Practitioner Program (to Ying Yuan), Shanghai Municipal Health Commission (Grant number: 20194Y0104 to Jiliang Ren).

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Correspondence to Xiaofeng Tao.

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The scientific guarantor of this publication is Xiaofeng Tao.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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Yuan, Y., Ren, J. & Tao, X. Machine learning–based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma. Eur Radiol (2021).

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  • Squamous cell carcinoma of head and neck
  • Lymphatic metastasis
  • Magnetic resonance imaging
  • Machine learning
  • Computer-assisted diagnosis