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

Abstract

ObjectiveTo 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 MethodsOne 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-signature120kVp) and iodine map (Rad-signatureImap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO).ResultsEight 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.ConclusionPredictive 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.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

“…( 71 ) also conducted a related study and showed that AI performed far better than experienced gastrointestinal pathologists. In addition, CT- or MRI-based imaging histology has many applications, including assessing pathological responses after radiotherapy or chemotherapy ( 72 ) and predicting colon cancer infiltration and metastasis ( 73 , 74 ). The duration of keyword bursts was long before 2016 and became shorter after 2016 ( Figure 6C ).…”

Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review

“…Comparatively, radiomics parameters provide more quantitative information than traditional parameters used by radiologists. In 2022, Wang et al [53] explored the relationship between DECT radiomics features of regional largest short-axis LNs and metastasis in patients with rectal cancer. After post-processing the venous phase images from DECT, 120 kVp-like images and iodine images were obtained, from which a substantial set of 833 features were extracted.…”

Immune checkpoint inhibition: a future guided by radiology

“…Radiomics is an ever-advancing field and its use has been applied clinically to other novel imaging techniques including dual energy CT, diffusion- weighted imaging (DWI), intravoxel incoherent motion DWI (IVIM-DWI), functional MRI, BOLD MRI and perfusion studies. 163–169 It is inevitable that future directions will see increasing application of radiomics to such novel imaging techniques for the purposes of advancing diagnostic and prognostication in oncology and guiding optimal immunotherapy treatments. In-depth characterisation of the whole TME by imaging can be achieved unlike biopsies which can only sample a small fraction of the heterogeneous TME.…”