&lt;p&gt;Contrast-Enhanced CT Parameters of Gastric Adenocarcinoma: Can Radiomic Features Be Surrogate Biomarkers for HER2 Over-Expression Status?&lt;/p&gt;

Wang, Na; Wang, Xinxin; Li, Wenya; Ye, Huajun; Bai, Hongzhao; Wu, Jiansheng; Chen, Mengjun

doi:10.2147/cmar.s230138

Cited by 14 publications

(11 citation statements)

References 31 publications

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“…Other investigators have used a D-TA approach to predict the response to neoadjuvant chemotherapy in resectable locally advanced gastric cancer and found that the TA parameter GLCM-contrast was able to predict complete pathologic response with an AUC of 0.763 [129]. Finally, innovative approaches have used radiomics for a non-invasive assessment of the immune microenvironment, correlating the TA features with the Treg cell infiltration or the HER2 expression [130,131].…”

Section: Texture Analysis and Prognosis-focus On Gastric Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

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Section: Texture Analysis and Prognosis-focus On Gastric Cancermentioning

confidence: 99%

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

et al. 2021

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“…Radiomics can decode tumor heterogeneity noninvasively, and there is a potential correlation between tumor genotypes and CT-based radiomics characteristics (30,31). In the study of contrast-enhanced CT parameters of gastric adenocarcinoma, Wang et al (32) found that radiomic features can be surrogate biomarkers for HER2 over-expression Status. Yang et al (33) used deep radiomics features from CT images to evaluate the HER2 status of breast cancer patients.…”

Section: Discussionmentioning

confidence: 99%

“…It is an emerging discipline that combines traditional medical image knowledge with big data analysis and precision medicine ( 29 ). Radiomics can decode tumor heterogeneity noninvasively, and there is a potential correlation between tumor genotypes and CT–based radiomics characteristics ( 30 , 31 ). In the study of contrast-enhanced CT parameters of gastric adenocarcinoma, Wang et al ( 32 ) found that radiomic features can be surrogate biomarkers for HER2 over-expression Status.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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“…In recent years, there have been some studies on the association between radiomic features and HER2 status (22-24, 26, 29-31). Several studies investigated the relationship between HER2 status and radiomic features in gastric cancer and colorectal cancer (29)(30)(31). Li et al (29) built and validated a CT-based radiomics nomogram for HER2 status prediction, which showed good performance.…”

Section: Discussionmentioning

confidence: 99%

Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer

Song

Yin

2021

Front. Oncol.

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ObjectiveTo evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer.Materials and MethodsThis study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis.ResultsAmong the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SIslope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%).ConclusionsTexture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.

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<p>Contrast-Enhanced CT Parameters of Gastric Adenocarcinoma: Can Radiomic Features Be Surrogate Biomarkers for HER2 Over-Expression Status?</p>

Cited by 14 publications

References 31 publications

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

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

Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer

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