CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study

Abstract

Background: Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT. A nomogram for the preoperative diagnosis of lymph node metastasis in patients with resectable ESCC has been previously developed. However, to the best of our knowledge, no reports focus on developing CT radiomics features to discriminate ESCC patients with regional lymph node metastasis (RLNM) and non-regional lymph node metastasis (NRLNM). We, therefore, aimed to develop CT radiomics models to predict lymph node metastasis (LNM) in advanced ESCC and to discriminate ESCC between RLNM and NRLNM. Methods: This study enrolled 334 patients with pathologically confirmed advanced ESCC, including 152 patients without LNM and 182 patients with LNM, and 103 patients with RLNM and 79 patients NRLNM. Radiomics features were extracted from CT data for each patient. The least absolute shrinkage and selection operator (LASSO) model and independent samples t-tests or Mann-Whitney U tests were exploited for dimension reduction and selection of radiomics features. Optimal radiomics features were chosen using multivariable logistic regression analysis. The discriminating performance was assessed by area under the receiver operating characteristic curve (AUC) and accuracy. Results: The radiomics features were developed based on multivariable logistic regression and were significantly associated with LNM status in both the training and validation cohorts (P<0.001). The radiomics models could differentiate between patients with and without LNM (AUC =0.79 and 0.75, and accuracy =0.75 and 0.71 in the training and validation cohorts, respectively). In patients with LNM, the radiomics features could effectively differentiate between RLNM and NRLNM (AUC =0.98 and 0.95, and accuracy =0.94 and 0.83 in the training and validation cohorts, respectively).Conclusions: CT radiomics features could help predict the LNM status of advanced ESCC patients and effectively discriminate ESCC between RLNM and NRLNM.

The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer

“…As for radiomics methods, a clinical-radiomic machine learning model to predict MSI based on computer tomography (CT) in EC and yielded an AUC value of 0.78 in the training set (n=102) and 0.78 in the test set (n=42) (16). The radiomic method has been widely used in other tumors (33)(34)(35)(36)(37), but it faces great challenges in the clinic due to limited interpretability. It is also challenging to delineate the boundaries of EC tumors on CE-CT images.…”

Development and validation of novel radiomics-based nomograms for the prediction of EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer

“…Radiomics analysis of large imaging datasets has been successfully employed in the field of oncology for noninvasively profiling tumor heterogeneity (15,16), and there is a growing interest in devising maps that display the associations between tumor heterogeneity and imaging features (17). This involves the extraction of quantitative features from digital medical images, which enables mineable high-dimensional data to be applied within clinical decision support to offer improved diagnostic, prognostic, and predictive accuracy (18)(19)(20)(21)(22). Radiomics is gaining importance in personalized cancer therapy.…”

Prognostic and incremental value of computed tomography-based radiomics from tumor and nodal regions in esophageal squamous cell carcinoma

“…ESCC prognosis was highly correlated with progression of primary tumor and lymph node metastasis, and thus except primary tumor, lymph node status should also be considered in radiomics model construction. However, many radiomic studies on the progression of ESCC considered only the primary tumor ( 9 - 13 , 28 ). In this study, we evaluated the added value of lymph nodal radiomics in OS prediction and demonstrated that the performance of combined tumor and nodal radiomics was superior to those of either tumor radiomics or nodal radiomics alone.…”