CT Radiomics for Distinction of Human Epidermal Growth Factor Receptor 2 Negative Gastric Cancer

Wang, Yue; Yu, Yang; Han, Wei; Zhang, Yingjing; Jiang, Lin; Xue, Huadan; Lei, Jing; Jin, Zhengyu; Yu, Jiang

doi:10.1016/j.acra.2020.02.018

Cited by 23 publications

(16 citation statements)

References 28 publications

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“…The analysis of the results confirmed that the classification model constructed using the features selected by the four selecting methods outperformed the model constructed by Wang et al. ( 45 ) using a single feature selecting method (AUC, 0.8955 vs 0.830).…”

Section: Discussionsupporting

confidence: 68%

Evaluation of Epidermal Growth Factor Receptor 2 Status in Gastric Cancer by CT-Based Deep Learning Radiomics Nomogram

2022

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PurposeTo explore the role of computed tomography (CT)-based deep learning and radiomics in preoperative evaluation of epidermal growth factor receptor 2 (HER2) status in gastric cancer.Materials and methodsThe clinical data on gastric cancer patients were evaluated retrospectively, and 357 patients were chosen for this study (training cohort: 249; test cohort: 108). The preprocessed enhanced CT arterial phase images were selected for lesion segmentation, radiomics and deep learning feature extraction. We integrated deep learning features and radiomic features (Inte). Four methods were used for feature selection. We constructed models with support vector machine (SVM) or random forest (RF), respectively. The area under the receiver operating characteristics curve (AUC) was used to assess the performance of these models. We also constructed a nomogram including Inte-feature scores and clinical factors.ResultsThe radiomics-SVM model showed good classification performance (AUC, training cohort: 0.8069; test cohort: 0.7869). The AUC of the ResNet50-SVM model and the Inte-SVM model in the test cohort were 0.8955 and 0.9055. The nomogram also showed excellent discrimination achieving greater AUC (training cohort, 0.9207; test cohort, 0.9224).ConclusionCT-based deep learning radiomics nomogram can accurately and effectively assess the HER2 status in patients with gastric cancer before surgery and it is expected to assist physicians in clinical decision-making and facilitates individualized treatment planning.

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Section: Discussionsupporting

confidence: 68%

Evaluation of Epidermal Growth Factor Receptor 2 Status in Gastric Cancer by CT-Based Deep Learning Radiomics Nomogram

2022

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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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“…Gastric cancer has been characterized by high genetic and immunological heterogeneity [ 26 ], and tumor feature identification in a noninvasive and convenient manner pretreatment would help clinicians develop an efficient treatment plan. Wang et al conducted a retrospective analysis and found that preoperative CT-based radiomics analysis is an effective tool for screening progressive gastric cancer and deducing the status of human epidermal growth factor receptor 2 (HER-2) [ 12 , 13 ]. Huang et al and Sun et al successfully applied radiomics analysis before surgery to predict the peritoneal metastasis of gastric cancer and the response to neoadjuvant therapy [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…As a result, radiomics analysis is also known as an "imaging biomarker" [10,11]. Several CT-based radiomics studies have shown that radiomics analysis can be used to diagnose different tumors, predict prognoses, assess a specific genetic status, and predict treatment outcomes [12][13][14]. Hitherto, none of the studies have been conducted to predict the treatment outcomes of immunotherapy for advanced gastric cancer.…”

Section: Introductionmentioning

confidence: 99%

A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment

Liang

Huang

Wang

et al. 2022

Aging

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Programmed cell death 1 (PD1) inhibitors have shown promising treatment effects in advanced gastric cancer, the beneficiary population not definite. This study aimed to construct an individualized radiomics model to predict the treatment benefits of PD-1 inhibitors in gastric cancer. Patients with advanced gastric cancer treated with PD-1 inhibitors were randomly divided into a training set (n = 58) and a validation set (n = 29). CT imaging data were extracted from medical records, and an individual radiomics nomogram was generated based on the imaging features and clinicopathological risk factors. Discrimination performance was evaluated by Harrell’s c-index and receiver operator characteristic (ROC) curve analyses. The areas under the ROC curves (AUCs) were analyzed to predict anti-PD-1 efficacy and survival. We found that the radiomics nomogram could predict the response of gastric cancer to anti-PD-1 treatment. The AUC was 0.865 with a 95% CI of 0.812-0.828 in the training set, while the AUC was 0.778 with a 95% CI of 0.732–0.776 in the validation set. The diagnostic performance of the radiomics was significantly higher than that of the clinical factors ( p < 0.01). Patients with a low risk of disease progression discriminated by the radiomics nomogram had longer progression-free survival than those with a high risk (6.5 vs. 3.2 months, HR 1.99, 95% CI: 1.19-3.31, p = 0.009). The radiomics nomogram based on CT imaging features and clinical risk factors could predict the treatment benefits of PD-1 inhibitors in advanced gastric cancer, enabling it to guide decision-making regarding clinical treatment.

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CT Radiomics for Distinction of Human Epidermal Growth Factor Receptor 2 Negative Gastric Cancer

Cited by 23 publications

References 28 publications

Evaluation of Epidermal Growth Factor Receptor 2 Status in Gastric Cancer by CT-Based Deep Learning Radiomics Nomogram

Evaluation of Epidermal Growth Factor Receptor 2 Status in Gastric Cancer by CT-Based Deep Learning Radiomics Nomogram

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

A radiomics model predicts the response of patients with advanced gastric cancer to PD-1 inhibitor treatment

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