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Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature

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Abstract

Objectives

To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC)

Methods

A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis.

Results

Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843–0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.

Conclusions

The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC.

Clinical relevance statement

Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making.

Key Points

• Combining analysis of variance with logistic regression yielded the optimal radiomic model.

• A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma.

• It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.

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Abbreviations

AE:

Auto-encoder

AJCC:

American Joint Committee on Cancer

ANOVA:

Analysis of variance

AUC:

Area under the receiver operating characteristic curve

CI:

Confidence interval

CT:

Computed tomography

DCA:

Decision curve analysis

FAE:

FeAture Explorer software

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

GP:

Gaussian process

HU:

Hounsfield units

IARC:

International Agency for Research on Cancer

ICCs:

Intra- /interclass correlation coefficients

KW:

Kruskal-Wallis

Lasso-LR:

Logistic regression via lasso

LDA:

Linear discriminant analysis

LHSCC:

Laryngeal and hypopharyngeal squamous cell carcinoma

LR:

Logistic regression

NB:

Naive Bayes

NGTDM:

Neighboring gray tone difference matrix

NNs:

Neural networks

NPV:

Negative predictive value

PACS:

Picture archiving and communication system

PCC:

Pearson’s correlation coefficient

PPV:

Positive predictive value

RFE:

Recursive feature elimination

ROI:

Region of interest

SMOTE:

Synthetic minority over-sampling technique

SVM:

Support vector machine

TNM:

Tumor-node-metastasis

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Funding

This study was supported by the Sichuan Science and Technology Program of China (Grant No. 2022YFS0616), the Sichuan University & Luzhou Collaborative Foundation (Grant No. 2017CDLZ-G27, 2018CDLZ-11), the Luzhou Science & Technology Department (Grant No. 2022-SYF-60), and the Project for Doctors of Affiliated Hospital of Southwest Medical University (Grant No. 2018-17129).

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Author notes

  1. Qianhan Liu and Shengdan Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, The Affiliated Hospital of Southwest Medical University, No. 23 Tai Ping Street, Luzhou, 646000, Sichuan, China

    Qianhan Liu, Shengdan Liu, Yu Mao, Xuefeng Kang, Mingling Yu & Guangxiang Chen

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  1. Qianhan Liu
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  2. Shengdan Liu
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  3. Yu Mao
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Corresponding author

Correspondence to Guangxiang Chen.

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The scientific guarantor of this publication is Guangxiang Chen.

Conflict of interest

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

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• diagnostic study

• performed at one institution

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Liu, Q., Liu, S., Mao, Y. et al. Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature. Eur Radiol (2024). https://doi.org/10.1007/s00330-023-10557-8

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  • DOI: https://doi.org/10.1007/s00330-023-10557-8

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