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Open Access 01-12-2024 | Esophageal Cancer | Original Article

Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy

Authors: Linrui Li, Zhihui Qin, Juan Bo, Jiaru Hu, Yu Zhang, Liting Qian, Jiangning Dong

Published in: Insights into Imaging | Issue 1/2024

Abstract

Objectives

To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses.

Methods

A total of 170 patients with pathologically and endoscopically confirmed proximal esophageal cancer from two centers were enrolled. Radiomics models were established by five machine learning approaches. The optimal radiomics model was selected using receiver operating curve analysis. Bioinformatics methods were applied to explore the potential biological mechanisms. Nomograms based on radiomics and clinical–radiomics features were constructed and assessed by receiver operating characteristics, calibration, and decision curve analyses net reclassification improvement, and integrated discrimination improvement evaluations.

Results

The peritumoral models performed well with the majority of classifiers in the training and validation sets, with the dual-region radiomics model showing the highest integrated area under the curve values of 0.9763 and 0.9471, respectively, and outperforming the single-region models. The clinical–radiomics nomogram showed better predictive performance than the clinical nomogram, with a net reclassification improvement of 34.4% (p = 0.02) and integrated discrimination improvement of 10% (p = 0.007). Gene ontology enrichment analysis revealed that lipid metabolism-related functions are potentially crucial in the process by which the radiomics score could stratify patients.

Conclusions

A combination of peritumoral radiomics features could improve the predictive performance of intratumoral radiomics to estimate overall survival after definitive chemoradiotherapy in patients with proximal esophageal cancer. Radiomics features could provide insights into the lipid metabolism associated with radioresistance and hold great potential to guide personalized care.

Critical relevance statement

This study demonstrates that incorporating peritumoral radiomics features enhances the predictive accuracy of overall survival in proximal esophageal cancer patients after chemoradiotherapy, and suggests a link between radiomics and lipid metabolism in radioresistance, highlighting its potential for personalized treatment strategies.

Key Points

Peritumoral region radiomics features could predict the prognosis of proximal esophageal cancer.
Dual-region radiomics features showed significantly better predictive performance.
Radiomics features can provide insights into the lipid metabolism associated with radioresistance.

Graphical Abstract

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Title: Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy
Authors: Linrui Li
Zhihui Qin
Juan Bo
Jiaru Hu
Yu Zhang
Liting Qian
Jiangning Dong
Publication date: 01-12-2024
Publisher: Springer Vienna
Keywords: Esophageal Cancer
Esophageal Cancer
Published in: Insights into Imaging / Issue 1/2024
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-024-01853-y

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Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy

Abstract

Objectives

Methods

Results

Conclusions

Critical relevance statement

Key Points

Graphical Abstract

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Graphical Abstract

Please log in to get access to this content

Predicting three or more metastatic nodes using contrast-enhanced lymphatic US findings in early breast cancer

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer

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