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European Archives of Oto-Rhino-Laryngology

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09-05-2023 | Rhinology

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

Authors: Yue Geng, Rujian Hong, Yushu Cheng, Fang Zhang, Yan Sha, Yang Song

Published in: European Archives of Oto-Rhino-Laryngology | Issue 9/2023

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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Title: 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
Authors: Yue Geng
Rujian Hong
Yushu Cheng
Fang Zhang
Yan Sha
Yang Song
Publication date: 09-05-2023
Publisher: Springer Berlin Heidelberg
Published in: European Archives of Oto-Rhino-Laryngology / Issue 9/2023
Print ISSN: 0937-4477
Electronic ISSN: 1434-4726
DOI: https://doi.org/10.1007/s00405-023-07989-9

2024 ASCO Annual Meeting

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusion

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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