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A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods

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Abstract

Objective

This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline 18F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology.

Methods

The initial staging 18F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann–Whitney U test) was used for each feature, in pairwise comparisons of independent variables. A p value of < 0.05 was considered significant. Receiver operating curve (ROC) analysis was performed for features with p < 0.05. Correlation between the significant features was evaluated with Spearman correlation test; features with correlation coefficient < 0.8 were evaluated with several ML algorithms.

Results

In predicting survival in a 1-year follow-up J48 was obtained as the most successful algorithm (AUC: 0.581, PRC: 0.565, MCC: 0.258, acc: 64.29%). 5-year survival results were more promising than 1-year survival results with (AUC: 0.820, PRC: 0.860, MCC: 271, acc: 81.36%) by logistic regression. It is revealed that the most successful algorithm was naive bayes (AUC: 0.680 PRC: 0.776, MCC: 0.298, acc: 82.66%) in the histopathological discrimination.

Conclusion

Texture analysis with ML algorithms could be predictive of overall survival and discriminating histopathological subtypes of EC.

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Acknowledgements

The authors declared that there is no conflict of interest concerning this manuscript. This study did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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The authors received no financial support for the research and/or authorship of this article.

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Authors and Affiliations

  1. Department of Nuclear Medicine, Dokuz Eylul University Faculty of Medicine, İnciraltı mah. Mithatpaşa cad. no:1606 Balçova, Izmir, Turkey

    Nazlı Pınar Karahan Şen & Gamze Çapa Kaya

  2. Başakşehir Çam ve Sakura City Hospital, Department of Nuclear Medicine, Istanbul, Turkey

    Ayşegül Aksu

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  1. Nazlı Pınar Karahan Şen
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  2. Ayşegül Aksu
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  3. Gamze Çapa Kaya
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Correspondence to Nazlı Pınar Karahan Şen.

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Karahan Şen, N.P., Aksu, A. & Çapa Kaya, G. A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods. Ann Nucl Med 35, 1030–1037 (2021). https://doi.org/10.1007/s12149-021-01638-z

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  • DOI: https://doi.org/10.1007/s12149-021-01638-z

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