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Bayram E, Kidi MM, Camadan YA, Biter S, Yaslikaya S, Toyran T, Mete B, Kara IO, Sahin B. Can the Pathological Response in Patients with Locally Advanced Gastric Cancer Receiving Neoadjuvant Treatment Be Predicted by the CEA/Albumin and CRP/Albumin Ratios? J Clin Med 2024; 13:2984. [PMID: 38792528 PMCID: PMC11122553 DOI: 10.3390/jcm13102984] [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: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Background: The purposes of neoadjuvant chemotherapy are to tumor size to improve the tumor removal rate, extend survival, and prevent metastasis. In this study, the importance of CRP/albumin ratio and CEA/albumin ratio in the prediction of neoadjuvant treatment response in gastric cancer patients was evaluated. Methods: This study retrospectively included 135 gastric cancer patients who received neoadjuvant chemotherapy at Çukurova University Balcalı Hospital between January 2018 and December 2023. Preoperative CRP/albumin and CEA/albumin ratios were compared according to treatment response and multivariate logistic regression analysis was performed to determine the potential importance of these ratios in predicting pathological response. Results: The mean age of the 135 patients was 58.79 ± 10.83 (min = 26-max = 78). The CRP/albumin and CEA/albumin ratios were found to be significantly lower in patients who did not respond to neoadjuvant therapy. Each 1-unit increase in the CRP/albumin ratio was associated with a 1.16-fold decrease in the odds of pathological complete response to neoadjuvant therapy. Both CRP/albumin and CEA/albumin ratios were found to be significant in distinguishing neoadjuvant therapy response. The optimal cut-off value was 2.74 for the CRP/albumin ratio (sensitivity = 60%, specificity = 78.4%) and 1.40 for the CEA/albumin ratio (sensitivity = 74.2%, specificity = 67.6%). Values below these cut-off points favored neoadjuvant therapy response. Pathological complete response to neoadjuvant therapy was 4.75 times higher in patients with a CRP/albumin ratio below 2.74 and 5.14 times higher in patients with a CEA/albumin ratio below 1.40. Conclusions: Findings demonstrate that in patients with locally advanced gastric cancer receiving neoadjuvant treatment, CRP/Albumin and CEA/Albumin ratios are significant markers of pathological response.
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Affiliation(s)
- Ertugrul Bayram
- Department of Medical Oncology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey; (M.M.K.); (Y.A.C.); (S.B.); (S.Y.); (I.O.K.); (B.S.)
| | - Mehmet Mutlu Kidi
- Department of Medical Oncology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey; (M.M.K.); (Y.A.C.); (S.B.); (S.Y.); (I.O.K.); (B.S.)
| | - Yasemin Aydınalp Camadan
- Department of Medical Oncology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey; (M.M.K.); (Y.A.C.); (S.B.); (S.Y.); (I.O.K.); (B.S.)
| | - Sedat Biter
- Department of Medical Oncology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey; (M.M.K.); (Y.A.C.); (S.B.); (S.Y.); (I.O.K.); (B.S.)
| | - Sendag Yaslikaya
- Department of Medical Oncology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey; (M.M.K.); (Y.A.C.); (S.B.); (S.Y.); (I.O.K.); (B.S.)
| | - Tugba Toyran
- Department of Medical Pathology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey;
| | - Burak Mete
- Department of Public Health, Faculty of Medicine, Cukurova University, Adana 01250, Turkey;
| | - Ismail Oguz Kara
- Department of Medical Oncology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey; (M.M.K.); (Y.A.C.); (S.B.); (S.Y.); (I.O.K.); (B.S.)
| | - Berksoy Sahin
- Department of Medical Oncology, Faculty of Medicine, Cukurova University, Adana 01250, Turkey; (M.M.K.); (Y.A.C.); (S.B.); (S.Y.); (I.O.K.); (B.S.)
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Xie X, Yan H, Liu K, Guan W, Luo K, Ma Y, Xu Y, Zhu Y, Wang M, Shen W. Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer. Quant Imaging Med Surg 2024; 14:749-764. [PMID: 38223109 PMCID: PMC10784007 DOI: 10.21037/qims-23-447] [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: 04/05/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024]
Abstract
Background The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). Methods In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients' basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher's exact test. The Mann-Whitney U-test and independent sample t-test were used to analyze the differences in multiple SDCT quantitative parameters between the non-LNM and LNM groups. The diagnostic efficacy of the corresponding parameters in predicting LNM in NSCLC was evaluated by plotting the receiver operating characteristic (ROC) curves. A multivariate logistic regression analysis was conducted to determine the independent predictive factors of LNM in NSCLC. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR40keV, SAR70keV, Δ40keV, Δ70keV, CER40keV, CER70keV, NEF40keV, NEF70keV, λ, normalized iodine concentration (NIC) and NZeff] were significantly higher in the non-LNM group than the LNM group (P<0.05). The ROC analysis showed that CER40keV, NIC, and CER70keV had higher diagnostic efficacy than other quantitative parameters in predicting LNM [areas under the curve (AUCs) =0.794, 0.791, and 0.783, respectively]. The multivariate logistic regression analysis showed that size, λ, and NIC were independent predictive factors of LNM. The combination of size, λ, and NIC had the highest diagnostic efficacy (AUC =0.892). The interobserver repeatability of the SDCT quantitative and derived quantitative parameters in the study was good (ICC: 0.801-0.935). Conclusions The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.
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Affiliation(s)
- Xiaodong Xie
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Hongwei Yan
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Kaifang Liu
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Weizheng Guan
- School of Medical Imaging, Bengbu Medical College, Bengbu, China
| | - Kai Luo
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Yikun Ma
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Youtao Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Yinsu Zhu
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Meiqin Wang
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
| | - Wenrong Shen
- Department of Radiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China
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Ke X, Liu W, Shen L, Zhang Y, Liu W, Wang C, Wang X. Early Screening of Colorectal Precancerous Lesions Based on Combined Measurement of Multiple Serum Tumor Markers Using Artificial Neural Network Analysis. BIOSENSORS 2023; 13:685. [PMID: 37504084 PMCID: PMC10377288 DOI: 10.3390/bios13070685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/21/2023] [Accepted: 06/24/2023] [Indexed: 07/29/2023]
Abstract
Many patients with colorectal cancer (CRC) are diagnosed in the advanced stage, resulting in delayed treatment and reduced survival time. It is urgent to develop accurate early screening methods for CRC. The purpose of this study is to develop an artificial intelligence (AI)-based artificial neural network (ANN) model using multiple protein tumor markers to assist in the early diagnosis of CRC and precancerous lesions. In this retrospective analysis, 148 cases with CRC and precancerous diseases were included. The concentrations of multiple protein tumor markers (CEA, CA19-9, CA 125, CYFRA 21-1, CA 72-4, CA 242) were measured by electrochemical luminescence immunoassays. By combining these markers with an ANN algorithm, a diagnosis model (CA6) was developed to distinguish between normal healthy and abnormal subjects, with an AUC of 0.97. The prediction score derived from the CA6 model also performed well in assisting in the diagnosis of precancerous lesions and early CRC (with AUCs of 0.97 and 0.93 and cut-off values of 0.39 and 0.34, respectively), which was better than that of individual protein tumor indicators. The CA6 model established by ANN provides a new and effective method for laboratory auxiliary diagnosis, which might be utilized for early colorectal lesion screening by incorporating more tumor markers with larger sample size.
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Affiliation(s)
- Xing Ke
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai 200092, China
| | - Wenxue Liu
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lisong Shen
- Department of Clinical Laboratory, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Institute of Artificial Intelligence Medicine, Shanghai Academy of Experimental Medicine, Shanghai 200092, China
| | - Yue Zhang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Wei Liu
- Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing 100080, China
| | - Chaofu Wang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xu Wang
- Department of Pathology, Ruijin Hospital and College of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Nanning Jiuzhouyuan Biotechnology Co., Ltd., Nanning 530007, China
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Wu A, Wu C, Zeng Q, Cao Y, Shu X, Luo L, Feng Z, Tu Y, Jie Z, Zhu Y, Zhou F, Huang Y, Li Z. Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer. Sci Rep 2023; 13:8442. [PMID: 37231100 DOI: 10.1038/s41598-023-35155-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/13/2023] [Indexed: 05/27/2023] Open
Abstract
""We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798-0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710-0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730-0.879), had the better predictive ability. The Hosmer-Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726-0.945) and 0.779 (95% CI 0.634-0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.
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Affiliation(s)
- Ahao Wu
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Changlei Wu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Qingwen Zeng
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yi Cao
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Xufeng Shu
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Lianghua Luo
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Zongfeng Feng
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Zhigang Jie
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Ya Huang
- Department of Radiology, The Second Affiliated Hospital, Nanchang University, Nanchang, China
| | - Zhengrong Li
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China.
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Su Y, Lu C, Zheng S, Zou H, Shen L, Yu J, Weng Q, Wang Z, Chen M, Zhang R, Ji J, Wang M. Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram. Front Oncol 2023; 13:1006172. [PMID: 37007144 PMCID: PMC10061075 DOI: 10.3389/fonc.2023.1006172] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
ObjectivesTo develop and validate a CT-based radiomics nomogram that can provide individualized pretreatment prediction of the response to platinum treatment in small cell lung cancer (SCLC).MaterialsA total of 134 SCLC patients who were treated with platinum as a first-line therapy were eligible for this study, including 51 patients with platinum resistance (PR) and 83 patients with platinum sensitivity (PS). The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were applied for feature selection and model construction. The selected texture features were calculated to obtain the radiomics score (Rad-score), and the predictive nomogram model was composed of the Rad-score and the clinical features selected by multivariate analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to assess the performance of the nomogram.ResultsThe Rad-score was calculated using 10 radiomic features, and the resulting radiomics signature demonstrated good discrimination in both the training set (area under the curve [AUC], 0.727; 95% confidence interval [CI], 0.627–0.809) and the validation set (AUC, 0.723; 95% CI, 0.562–0.799). To improve diagnostic effectiveness, the Rad-score created a novel prediction nomogram by combining CA125 and CA72-4. The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.900; 95% CI, 0.844-0.947) and the validation set (AUC, 0.838; 95% CI, 0.534-0.735). The radiomics nomogram proved to be clinically beneficial based on decision curve analysis.ConclusionWe developed and validated a radiomics nomogram model for predicting the response to platinum in SCLC patients. The outcomes of this model can provide useful suggestions for the development of tailored and customized second-line chemotherapy regimens.
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Affiliation(s)
- Yanping Su
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, Institute of Aging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Alzheimer’s Disease of Zhejiang, Wenzhou, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Shenfei Zheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Hao Zou
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Lin Shen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Junchao Yu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Qiaoyou Weng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Zufei Wang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
| | - Ran Zhang
- AI Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, Zhejiang, China
- Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, Zhejiang, China
- *Correspondence: Meihao Wang, ; Jiansong Ji,
| | - Meihao Wang
- Department of Radiology, Key Laboratory of Intelligent Medical Imaging of Wenzhou, Institute of Aging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Key Laboratory of Alzheimer’s Disease of Zhejiang, Wenzhou, Zhejiang, China
- *Correspondence: Meihao Wang, ; Jiansong Ji,
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Xu M, Liu S, Li L, Qiao X, Ji C, Tan L, Zhou Z. Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer. Sci Rep 2022; 12:14177. [PMID: 35986169 PMCID: PMC9391326 DOI: 10.1038/s41598-022-18433-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/11/2022] [Indexed: 12/24/2022] Open
Abstract
The combination of trastuzumab and chemotherapy is recommended as first-line therapy for patients with human epidermal growth factor receptor 2 (HER2) positive advanced gastric cancers (GCs). Successful trastuzumab-induced targeted therapy should be based on the assessment of HER2 overexpression. This study aimed to evaluate the feasibility of multivariate models based on hematological parameters, endoscopic biopsy, and computed tomography (CT) findings for assessing HER2 overexpression in GC. This retrospective study included 183 patients with GC, and they were divided into primary (n = 137) and validation (n = 46) cohorts at a ratio of 3:1. Hematological parameters, endoscopic biopsy, CT morphological characteristics, and CT value-related and texture parameters of all patients were collected and analyzed. The mean corpuscular hemoglobin concentration value, morphological type, 3 CT value-related parameters, and 22 texture parameters in three contrast-enhanced phases differed significantly between the two groups (all p < 0.05). Multivariate models based on the regression analysis and support vector machine algorithm achieved areas under the curve of 0.818 and 0.879 in the primary cohort, respectively. The combination of hematological parameters, CT morphological characteristics, CT value-related and texture parameters could predict HER2 overexpression in GCs with satisfactory diagnostic efficiency. The decision curve analysis confirmed the clinical utility.
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Affiliation(s)
- Mengying Xu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Lin Li
- Department of Pathology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China
| | - Xiangmei Qiao
- Department of Ultrasound, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, China
| | - Changfeng Ji
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Lingyu Tan
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Zhengyang Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No.321, Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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