A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma

Yahan Tong; Junyi Chen; Jingjing Sun; Taobo Luo; Shaofeng Duan; Kai Li; Kefeng Zhou; Jian Zeng; Fangxiao Lu

doi:10.3389/fonc.2023.1162238

A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma

Front Oncol. 2023 Oct 12:13:1162238. doi: 10.3389/fonc.2023.1162238. eCollection 2023.

Authors

Yahan Tong¹, Junyi Chen², Jingjing Sun¹, Taobo Luo³, Shaofeng Duan⁴, Kai Li¹, Kefeng Zhou⁵, Jian Zeng³, Fangxiao Lu¹

Affiliations

¹ Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China.
² Medical School of Chinese People's Liberation Army (PLA), Beijing, China.
³ Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou, China.
⁴ GE Healthcare, Precision Health Institution, Shanghai, China.
⁵ Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.

Abstract

Purpose: To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent.

Materials and methods: The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results: We selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram.

Conclusion: We successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.

Keywords: X-Ray Computed; esophageal squamous cell carcinoma/esophageal cancer; nomogram; radiomics; recurrence; tomography.

Grants and funding

This work was supported by Medical Health Science and Technology Project of Zhejiang Province (2021KY583, 2022KY654, 2022KY655), Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province (2022E10021), Natural Science Foundation of Zhejiang Province (LSY19H030001).