A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer

Dongqing Wang; Zijian Zhuang; Shuting Wu; Jixiang Chen; Xin Fan; Mengsi Liu; Haitao Zhu; Ming Wang; Jinmei Zou; Qun Zhou; Peng Zhou; Jing Xue; Xiangpan Meng; Shenghong Ju; Lirong Zhang

doi:10.3389/fonc.2022.846840

A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer

Front Oncol. 2022 Jun 7:12:846840. doi: 10.3389/fonc.2022.846840. eCollection 2022.

Authors

Dongqing Wang^{1

2}, Zijian Zhuang^{1

2}, Shuting Wu², Jixiang Chen³, Xin Fan³, Mengsi Liu², Haitao Zhu^{1

2}, Ming Wang¹, Jinmei Zou¹, Qun Zhou¹, Peng Zhou², Jing Xue², Xiangpan Meng^{4

5}, Shenghong Ju^{4

5}, Lirong Zhang^{1

4}

Affiliations

¹ Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, China.
² School of Medicine, Jiangsu University, Zhenjiang, China.
³ Department of General Surgery, The Affiliated Hospital of Jiangsu University, Zhenjiang, China.
⁴ School of Medicine, Southeast University, Nanjing, China.
⁵ Department of Radiology, Zhongda Hospital, Southeast University, Nanjing, China.

Abstract

Objective: To explore the value of dual-energy computed tomography (DECT) radiomics of the regional largest short-axis lymph nodes for evaluating lymph node metastasis in patients with rectal cancer.

Materials and methods: One hundred forty-one patients with rectal cancer (58 in LNM+ group, 83 in LNM- group) who underwent preoperative total abdominal DECT were divided into a training group and testing group (7:3 ratio). After post-processing DECT venous phase images, 120kVp-like images and iodine (water) images were obtained. The highest-risk lymph nodes were identified, and their long-axis and short-axis diameter and DECT quantitative parameters were measured manually by two experienced radiologists who were blind to the postoperative pathological results. Four DECT parameters were analyzed: arterial phase (AP) normalized iodine concentration, AP normalized effective atomic number, the venous phase (VP) normalized iodine concentration, and the venous phase normalized effective atomic number. The carcinoembryonic antigen (CEA) levels were recorded one week before surgery. Radiomics features of the largest lymph nodes were extracted, standardized, and reduced before modeling. Radomics signatures of 120kVp-like images (Rad-signature_120kVp) and iodine map (Rad-signature_Imap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO).

Results: Eight hundred thirty-three features were extracted from 120kVp-like and iodine images, respectively. In testing group, the radiomics features based on 120kVp-like images showed the best diagnostic performance (AUC=0.922) compared to other predictors [CT morphological indicators (short-axis diameter (AUC=0.779, IDI=0.262) and long-axis diameter alone (AUC=0.714, IDI=0.329)), CEA alone (AUC=0.540, IDI=0.414), and normalized DECT parameters alone (AUC=0.504-0.718, IDI=0.290-0.476)](P<0.05 in Delong test). Contrary, DECT iodine map-based radiomic signatures showed similar performance in predicting lymph node metastasis (AUC=0.866). The decision curve showed that the 120kVp-like-based radiomics signature has the highest net income.

Conclusion: Predictive model based on DECT and the largest short-axis diameter lymph nodes has the highest diagnostic value in predicting lymph node metastasis in patients with rectal cancer.

Keywords: clinical prediction rule; dual-energy scanned projection; lymph node metastasis; machine learning; radiomics; rectal cancer (RC).