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

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.