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Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study

  • Rhinology
  • Published:
European Archives of Oto-Rhino-Laryngology Aims and scope Submit manuscript

Abstract

Purpose

Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC.

Methods

One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed.

Results

The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05).

Conclusion

ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.

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Availability of data and materials

The authors declare that the data are available.

Code availability

The authors declare that the custom code can be available.

Abbreviations

SNSCC:

Sinonasal squamous cell carcinoma

HNSCC:

Head and neck squamous cell carcinoma

DWI:

Diffusion-weighted imaging

ADC:

Apparent diffusion coefficient

RESOLVE:

Readout-segmented echo-planar imaging sequence

SMOTE:

Synthetic minority oversampling technique

PCC:

Pearson correlation coefficient

ANOVA:

Analysis of variance

KW:

Kruskal–Wallis

RFE:

Recursive feature elimination

SVM:

Support vector machine

LDA:

Linear discriminant analysis

LR:

Logistic regression

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Authors and Affiliations

  1. Department of Radiology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China

    Yue Geng, Rujian Hong, Yushu Cheng, Fang Zhang & Yan Sha

  2. Scientific Marketing, Siemens Healthineers, Shanghai, 200336, China

    Yang Song

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Correspondence to Yan Sha.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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This study was approved by the institutional review board of our university hospital and written consent was obtained from each patient.

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Geng, Y., Hong, R., Cheng, Y. et al. Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study. Eur Arch Otorhinolaryngol 280, 4131–4140 (2023). https://doi.org/10.1007/s00405-023-07989-9

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