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

IF 4.1 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Linrui Li, Zhihui Qin, Juan Bo, Jiaru Hu, Yu Zhang, Liting Qian, Jiangning Dong
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引用次数: 0

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.

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来源期刊
Insights into Imaging
Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
7.30
自引率
4.30%
发文量
182
审稿时长
13 weeks
期刊介绍: Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.
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