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

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Abstract

Objectives

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.

Results

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.

Conclusion

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 learning–based 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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Abbreviations

AUC:

Area under the curve

ceT1WI:

Contrast-enhanced T1-weighted imaging

DOI:

Depth of invasion

END:

Elective neck dissection

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run-length matrix

GLSZM:

Gray-level size zone matrix

HNSCC:

Head and neck squamous cell carcinoma

ICCs:

Intraclass correlation coefficients

LN:

Lymph node

LR:

Logistic regression

MRI:

Magnetic resonance imaging

NB:

Naïve Bayes

NN:

Neural network

OTSCC:

Oral tongue squamous cell carcinoma

RF:

Random forest

ROC:

Receiver operator characteristic

ROI:

Region of interests

SVM:

Support vector machine

T2WI:

T2-Weighted imaging

TE:

Echo time

TR:

Repetition time

VOI:

Volume of interest

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Funding

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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Author notes

  1. Ying Yuan and Jiliang Ren contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China

    Ying Yuan, Jiliang Ren & Xiaofeng Tao

Authors
  1. Ying Yuan
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  2. Jiliang Ren
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  3. Xiaofeng Tao
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Corresponding author

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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• retrospective

• case-control study

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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 31, 6429–6437 (2021). https://doi.org/10.1007/s00330-021-07731-1

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  • DOI: https://doi.org/10.1007/s00330-021-07731-1

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