The Value of Predicting Human Epidermal Growth Factor Receptor 2 Status in Adenocarcinoma of the Esophagogastric Junction on CT-Based Radiomics Nomogram

Abstract

PurposeWe developed and validated a CT-based radiomics nomogram to predict HER2 status in patients with adenocarcinoma of esophagogastric junction (AEG).MethodA total of 101 patients with HER2-positive (n=46) and HER2-negative (n=55) esophagogastric junction adenocarcinoma (AEG) were retrospectively analyzed. They were then randomly divided into a training cohort (n=70) and a verification cohort (n=31). The radiomics features were obtained from the portal phase of the CT enhanced scan. We used the least absolute shrinkage and selection operator (LASSO) logistic regression method to select the best radiomics features in the training cohort, combined them linearly, and used the radiomics signature formula to calculate the radiomics score (Rad-score) of each AEG patient. A multivariable logistic regression method was applied to develop a prediction model that incorporated the radiomics signature and independent risk predictors. The prediction performance of the nomogram was evaluated using the training and validation cohorts.ResultIn the training (P<0.001) and verification groups (P<0.001), the radiomics signature combined with seven radiomics features was significantly correlated with HER2 status. The nomogram composed of CT-reported T stage and radiomics signature showed very good predictive performance for HER2 status. The area under the curve (AUC) of the training cohort was 0.946 (95% CI: 0.919–0.973), and that of the validation group was 0.903 (95% CI: 0.847–0.959). The calibration curve of the radiomics nomogram showed a good degree of calibration. Decision-curve analysis revealed that the radiomics nomogram was useful.ConclusionThe nomogram CT-based radiomics signature combined with CT-reported T stage can better predict the HER2 status of AEG before surgery. It can be used as a non-invasive prediction tool for HER2 status and is expected to guide clinical treatment decisions in clinical practice, and it can assist in the formulation of individualized treatment plans.

Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound

“…The transform-filtered texture features could provide potential insight for quantifying tumor biological and multidimensional heterogeneity ( 6 , 11 , 30 , 36 ). Many studies had found that transform-filtered texture features were useful in predicting the tumor benignity and malignancy, lymph node metastasis, gene expression, and the efficacy of neoadjuvant chemotherapy ( 11 , 18 – 20 , 30 , 36 , 37 ), although some explanations of relationship between these complex features and tumor biology behavior remained to be elucidated ( 6 ). Some transform-filtered texture features from DBT and MRI could be incorporated as prediction model for the LVI status in breast cancer ( 6 , 11 ), since the transform-filtered features may be associated with the tumor complex microstructure, such as tumor cell proliferation, local necrosis, hemorrhage, inflammation, and microcalcifications, etc in LVI-positive tumor ( 6 , 7 ).…”

Association of the collagen signature with pathological complete response in rectal cancer patients

“…Univariable and multivariable logistic regression analyses were used to analyze the value of clinicopathological candidate predictors and collagen signatures in the primary cohort. Then, an individualized prediction model for pCR was developed based on the results of the multivariable analysis and presented as a visual nomogram 22,23 …”

“…Then, an individualized prediction model for pCR was developed based on the results of the multivariable analysis and presented as a visual nomogram. 22,23 The discrimination and calibration of the nomogram was measured by the ROC curve and calibration curve with the Hosmer-Lemeshow test. In addition, the variance inflation factor was calculated to evaluate the multicollinearity of the multivariate prediction model.…”

A Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperative Prediction of DNA Mismatch Repair Deficiency in Gastric Adenocarcinoma

“…Wang et al reported that the radiomics nomogram integrated with clinic-radiological features holds promise for clinical use as a non-invasive tool in the individual prediction of lymph node metastasis in GC (30). Wang et al found that the nomogram-integrated CT-radiomics signature and CT-reported T stage can enhance prediction of the human epidermal growth factor receptor 2 status of esophagogastric junction adenocarcinoma before surgery (31).…”

“…Wang et al. found that the nomogram-integrated CT-radiomics signature and CT-reported T stage can enhance prediction of the human epidermal growth factor receptor 2 status of esophagogastric junction adenocarcinoma before surgery ( 31 ).…”