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Computed tomography-based radiomic analysis for predicting pathological response and prognosis after neoadjuvant chemotherapy in patients with locally advanced esophageal cancer

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A Letter to the Editor to this article was published on 09 June 2023

A Letter to the Editor to this article was published on 09 June 2023

Abstract

Purpose

Accurate prediction of prognosis and pathological response to neoadjuvant chemotherapy (NAC) is crucial for optimizing treatment strategies for patients with locally advanced esophageal cancer (LA-EC). This study aimed to investigate the use of radiomics for pretreatment CT in predicting the pathological response of patients with LA-EC to NAC.

Methods

Overall, 144 patients (145 lesions) with LA-EC who underwent pretreatment contrast-enhanced CT and then received NAC followed by surgery with pathological tumor regression grade (TRG) analysis were enrolled. The obtained dataset was randomly divided into training and validation cohorts using fivefold cross-validation. CT-based radiomic features were extracted followed by the feature selection process using the variance threshold, SelectKBest, and least absolute shrinkage and selection operator methods. The radiomic model was constructed using six machine learning classifiers, and predictive performance was evaluated using ROC curve analysis in the training and validation cohorts.

Results

All patients were divided into responders (n = 40, 28%) and non-responders (n = 104, 72%) based on the TRG results and a statistically significant split by overall survival analysis (0.899 [0.754–0.961] vs. 0.630 [0.510–0.729], respectively). There were no significant differences between responders and non-responders in terms of age, sex, tumor size, tumor location, or histopathology. The mean AUC of fivefold in the validation cohort was 0.720 (confidence interval [CI]: 0.594–0.982), and the best AUC of the radiomic model using logistic regression to predict the non-responders was 0.815 (CI: 0.626–1.000, sensitivity 0.620, specificity 0.860).

Conclusion

A radiomic model derived from contrast-enhanced CT may help stratify chemotherapy effect prediction and improve clinical decision-making.

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Acknowledgements

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Funding

This study was supported by a grant from the Japanese Ministry of Education, Culture, Sports, Science, and Technology (Grant-in-Aid for Young Scientists KAKEN; no. 18K15573) and Canon Medical Systems.

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Authors and Affiliations

  1. Department of Diagnostic Radiology, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan

    Shioto Oda, Hirofumi Kuno, Takashi Hiyama, Tomoaki Sasaki & Tatsushi Kobayashi

  2. Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa Japan, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan

    Shingo Sakashita

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  1. Shioto Oda
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by SO, SS, and HK. The first draft of the manuscript was written by SO and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shioto Oda.

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Tatsushi Kobayashi has funding from Canon Medical Systems. The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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This retrospective study was approved by the institutional review board in our center, and the requirement for written informed consent was waived.

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Oda, S., Kuno, H., Hiyama, T. et al. Computed tomography-based radiomic analysis for predicting pathological response and prognosis after neoadjuvant chemotherapy in patients with locally advanced esophageal cancer. Abdom Radiol 48, 2503–2513 (2023). https://doi.org/10.1007/s00261-023-03938-6

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