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Song Q, Wang X, Zhu J, Shi H. Diagnostic value of dual-source, dual-energy computed tomography combined with the neutrophil-lymphocyte ratio for discriminating gastric signet ring cell from mixed signet ring cell and non-signet ring cell carcinomas. Abdom Radiol (NY) 2024; 49:2996-3002. [PMID: 38526596 PMCID: PMC11335798 DOI: 10.1007/s00261-024-04286-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE To explore the diagnostic value of dual-source computed tomography (DSCT) and neutrophil to lymphocyte ratio (NLR) for differentiating gastric signet ring cell carcinoma (SRC) from mixed SRC (mSRC) and non-SRC (nSRC). METHODS This retrospective study included patients with gastric adenocarcinoma who underwent DSCT between August 2019 and June 2021 at our Hospital. The iodine concentration in the venous phase (ICvp), standardized iodine concentration (NICVP), and the slope of the energy spectrum curve (kVP) were extracted from DSCT data. NLR was determined from laboratory results. DSCT (including ICVP, NICVP, and kVP) and combination (including DSCT model and NLR) models were established based on the multinomial logistic regression analysis. The receiver operator characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic value. RESULTS A total of 155 patients (SRC [n = 45, aged 61.22 ± 11.4 years], mSRC [n = 60, aged 61.09 ± 12.7 years], and nSRC [n = 50, aged 67.66 ± 8.76 years]) were included. There were significant differences in NLR, ICVP, NICVP, and kVP among the SRC, mSRC, and nSRC groups (all P < 0.001). The AUC of the combination model for SRC vs. mSRC + nSRC was 0.964 (95% CI: 0.923-1.000), with a sensitivity of 98.3% and a specificity of 86.7%, higher than with DSCT (AUC: 0.959, 95% CI: 0.919-0.998, sensitivity: 90.0%, specificity: 89.9%) or NLR (AUC: 0.670, 95% CI: 0.577-0.768, sensitivity: 62.2%, specificity: 61.8%). CONCLUSION DSCT combined with NLR showed high diagnostic efficacy in differentiating SRC from mSRC and nSRC.
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Affiliation(s)
- Qinxia Song
- Department of radiology, Anqing Municipal Hospital, Anqing, 246000, Anhui province, China
| | - Xiangfa Wang
- Department of radiology, Anqing Municipal Hospital, Anqing, 246000, Anhui province, China.
| | - Juan Zhu
- Department of radiology, Anqing Municipal Hospital, Anqing, 246000, Anhui province, China
| | - Hengfeng Shi
- Department of radiology, Anqing Municipal Hospital, Anqing, 246000, Anhui province, China
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Zhi H, Xiang Y, Chen C, Zhang W, Lin J, Gao Z, Shen Q, Shao J, Yang X, Yang Y, Chen X, Zheng J, Lu M, Pan B, Dong Q, Shen X, Ma C. Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival. Cancer Imaging 2024; 24:99. [PMID: 39080806 PMCID: PMC11290137 DOI: 10.1186/s40644-024-00741-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 07/13/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC. METHODS We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness. RESULTS On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis. CONCLUSIONS Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.
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Affiliation(s)
- Huaiqing Zhi
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yilan Xiang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Chenbin Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Weiteng Zhang
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jie Lin
- Department of PET/CT, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Zekan Gao
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qingzheng Shen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jiancan Shao
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xinxin Yang
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yunjun Yang
- Department of PET/CT, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xiaodong Chen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jingwei Zheng
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Mingdong Lu
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Bujian Pan
- Department of General Surgery, The Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qiantong Dong
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - Xian Shen
- Department of General Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
| | - Chunxue Ma
- Department of Gastrointestinal Surgery Nursing Unit, Ward 443, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
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Huang T, Chan C, Zhou H, Hu K, Wang L, Ye Z. Construction and validation of the prognostic nomogram model for patients with diffuse-type gastric cancer based on the SEER database. Discov Oncol 2024; 15:305. [PMID: 39048774 PMCID: PMC11269533 DOI: 10.1007/s12672-024-01180-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024] Open
Abstract
OBJECTIVE The prognostic factors of diffuse GC patients were screened the prognostic nomogram was constructed, and the prediction accuracy was verified. METHODS From 2006 to 2018, there were 2877 individuals pathologically diagnosed with diffuse gastric cancer; the clinicopathological features of these patients were obtained from the SEER database & randomly divided into a training cohort (1439) & validation cohort (1438).To create prognostic nomograms & choose independent prognostic indicators to predict the overall survival (OS) of 1, 3, & 5 years, log-rank & multivariate COX analysis were utilized & discrimination ability of nomogram prediction using consistency index and calibration curve. RESULTS Age, T, N, M, TNM, surgical status, chemotherapy status, & all seven markers were independent predictors of OS (P < 0.05), & a nomogram of OS at 1, 3, & 5 years was created using these independent predictors. The nomogram's c-index was 0.750 (95% CI 0.734 ~ 0.766), greater than the TNM staging framework 0.658 (95%CI 0.639 ~ 0.677); the c-index was 0.753 (95% CI 0.737 ~ 0.769) as well as superior to the TNM staging mechanism 0.679 (95% CI 0.503-0.697). According to the calibration curve, the projected survival rate using the nomogram & the actual survival rate are in good agreement. CONCLUSIONS Prognostic nomograms are useful tools for physicians to assess every individual's individualised prognosis & create treatment strategies for those with diffuse gastric cancer. They can reliably predict the prognosis for individuals with diffuse gastrointestinal carcinoma.
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Affiliation(s)
- Ting Huang
- Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - ChuiPing Chan
- The Third School of Clinical Medicine (School of Rehabilitation Medicine), Zhejiang Chinese Medical University, Hangzhou, China
| | - Heran Zhou
- Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - Keke Hu
- Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - Lu Wang
- Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China
| | - Zhifeng Ye
- Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, China.
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Li J, Yin H, Zhang H, Wang Y, Ma F, Li L, Gao J, Qu J. Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers. Acad Radiol 2024:S1076-6332(24)00243-5. [PMID: 38734580 DOI: 10.1016/j.acra.2024.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes-serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer. MATERIAL AND METHODS This prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40 keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs). RESULTS In total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared to the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P<0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P<0.05). CONCLUSION DECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.
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Affiliation(s)
- Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Yi Wang
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Fei Ma
- Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
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Yang Z, Han Y, Li F, Zhang A, Cheng M, Gao J. Deep learning radiomics analysis based on computed tomography for survival prediction in gastric neuroendocrine neoplasm: a multicenter study. Quant Imaging Med Surg 2023; 13:8190-8203. [PMID: 38106311 PMCID: PMC10721996 DOI: 10.21037/qims-23-577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/14/2023] [Indexed: 12/19/2023]
Abstract
Background Survival prediction is crucial for patients with gastric neuroendocrine neoplasms (gNENs) to assess the treatment programs and may guide personalized medicine. This study aimed to develop and evaluate a deep learning (DL) radiomics model to predict the overall survival (OS) in patients with gNENs. Methods The retrospective analysis included 162 consecutive patients with gNENs from two hospitals, who were divided into a training cohort, internal validation cohort (The First Affiliated Hospital of Zhengzhou University; n=108), and an external validation cohort (The Henan Cancer Hospital; n=54). DL radiomics analysis was applied to computed tomography (CT) images of the arterial phase and venous phase, respectively. Based on pretreatment CT images, two DL radiomics signatures were developed to predict OS. The combined model incorporating the radiomics signatures and clinical factors was built through the multivariable Cox proportional hazards (CPH) method. The combined model was visualized into a radiomics nomogram for individualized OS estimation. Prediction performance was assessed with the concordance index (C-index) and the Kaplan-Meier (KM) estimator. Results The DL-based radiomics signatures based on two phases were significantly correlated with OS in the training (C-index: 0.79-0.92; P<0.01), internal validation (C-index: 0.61-0.86; P<0.01), and external validation (C-index: 0.56-0.75; P<0.01) cohorts. The combined model integrating radiomics signatures with clinical factors showed a significant improvement in predictive performance compared to the clinical model in the training (C-index: 0.86 vs. 0.80; P<0.01), internal validation (C-index: 0.77 vs. 0.71; P<0.01), and external validation (C-index: 0.71 vs. 0.66; P<0.01) cohorts. Moreover, the combined model classified patients into high-risk and low-risk groups, and the high-risk group had a shorter OS compared to the low-risk group in the training cohort [hazard ratio (HR) 3.12, 95% confidence interval (CI): 2.34-3.93; P<0.01], which was validated in the internal (HR 2.51, 95% CI: 1.57-3.99; P<0.01) and external validation cohort (HR 1.77, 95% CI: 1.21-2.59; P<0.01). Conclusions DL radiomics analysis could serve as a potential and noninvasive tool for prognostic prediction and risk stratification in patients with gNENs.
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Affiliation(s)
- Zhihao Yang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yijing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fei Li
- School of Cyber Science and Engineering, Wuhan University, Wuhan, China
| | - Anqi Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Cheng
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Ma T, Wang H, Ye Z. Artificial intelligence applications in computed tomography in gastric cancer: a narrative review. Transl Cancer Res 2023; 12:2379-2392. [PMID: 37859746 PMCID: PMC10583011 DOI: 10.21037/tcr-23-201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/01/2023] [Indexed: 10/21/2023]
Abstract
Background and Objective Artificial intelligence (AI) is a revolutionary technique which is deeply impacting and reshaping clinical practice in oncology. This review aims to summarize the current status of the clinical application of AI-based computed tomography (CT) for gastric cancer (GC), focusing on diagnosis, genetic status detection and risk prediction of metastasis, prognosis and treatment efficacy. The challenges and prospects for future research will also be discussed. Methods We searched the PubMed/MEDLINE database to identify clinical studies published between 1990 and November 2022 that investigated AI applications in CT in GC. The major findings of the verified studies were summarized. Key Content and Findings AI applications in CT images have attracted considerable attention in various fields such as diagnosis, prediction of metastasis risk, survival, and treatment response. These emerging techniques have shown a high potential to outperform clinicians in diagnostic accuracy and time-saving. Conclusions AI-powered tools showed great potential to increase diagnostic accuracy and reduce radiologists' workload. However, the goal of AI is not to replace human ability but to help oncologists make decisions in their practice. Therefore, radiologists should play a predominant role in AI applications and decide the best ways to integrate these complementary techniques within clinical practice.
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Affiliation(s)
- Tingting Ma
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hua Wang
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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Ma T, Cui J, Wang L, Li H, Ye Z, Gao X. A CT-based radiomics signature for prediction of HER2 overexpression and treatment efficacy of trastuzumab in advanced gastric cancer. Transl Cancer Res 2022; 11:4326-4337. [PMID: 36644192 PMCID: PMC9834583 DOI: 10.21037/tcr-22-1690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/26/2022] [Indexed: 11/20/2022]
Abstract
Background Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is very important for appropriate management of advanced gastric cancer (AGC) patients. In this study, we aimed to develop and validate a computed tomography (CT)-based radiomics signature for preoperative prediction of HER2 overexpression and treatment efficacy of trastuzumab in AGC. Methods We retrospectively enrolled 536 consecutive AGC patients (median age, 59 years; interquartile range, 52-65 years; 377 male, 159 female) and separated them into a training set (n=357) and a testing set (n=179). Radiomic features were extracted from 3 different phase images of contrast-enhanced CT scans, and a radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. The predictive performance of the radiomics signature was assessed in the training and testing sets. Univariable and multivariable logistical regression analyses were used to identify independent risk factors of HER2 overexpression. Univariable and multivariable Cox regression analyses were used to identify the risk factors of overall survival (OS) and progression-free survival (PFS). The predictive value of the radiomics signature for treatment efficacy of trastuzumab was also evaluated. Results The radiomics signature comprised eight robust features that demonstrated good discrimination ability for HER2 overexpression in the training set [area under the curve (AUC) =0.85] and the testing set (AUC =0.81). Multivariable Cox regression analysis revealed that the radiomics signature was an independent risk factor for OS [hazard ratio (HR) =2.01, P=0.001] and PFS (HR =1.32, P=0.01). The radiomics score of patients who achieved disease control was significantly lower than that of patients with progressive disease (P=0.023). Conclusions The proposed radiomics signature showed favorable accuracy for prediction of HER2 overexpression and prognosis in AGC. It has promising potential as a noninvasive approach for selecting patients for target therapy.
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Affiliation(s)
- Tingting Ma
- Department of Radiology, Tianjin Cancer Hospital Airport Hospital, Tianjin, China;,Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;,National Clinical Research Center for Cancer, Tianjin, China;,Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Jingli Cui
- National Clinical Research Center for Cancer, Tianjin, China;,Tianjin’s Clinical Research Center for Cancer, Tianjin, China;,Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;,Department of General Surgery, Weifang People’s Hospital, Weifang, China
| | - Lingwei Wang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;,National Clinical Research Center for Cancer, Tianjin, China;,Tianjin’s Clinical Research Center for Cancer, Tianjin, China;,The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hui Li
- National Clinical Research Center for Cancer, Tianjin, China;,Department of Gastrointestinal Cancer Biology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;,Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;,National Clinical Research Center for Cancer, Tianjin, China;,Tianjin’s Clinical Research Center for Cancer, Tianjin, China;,The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xujie Gao
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China;,National Clinical Research Center for Cancer, Tianjin, China;,Tianjin’s Clinical Research Center for Cancer, Tianjin, China;,The Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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