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For: Zhao H, Li W, Huang W, Yang Y, Shen W, Liang P, Gao J. Dual-Energy CT-Based Nomogram for Decoding HER2 Status in Patients With Gastric Cancer. AJR Am J Roentgenol 2021;216:1539-48. [PMID: 33852330 DOI: 10.2214/AJR.20.23528] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]

For:	Zhao H, Li W, Huang W, Yang Y, Shen W, Liang P, Gao J. Dual-Energy CT-Based Nomogram for Decoding HER2 Status in Patients With Gastric Cancer. AJR Am J Roentgenol 2021;216:1539-48. [PMID: 33852330 DOI: 10.2214/AJR.20.23528] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]

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Cited by Other Article(s)

Chen W, Lin G, Li X, Feng Y, Mao W, Kong C, Hu Y, Gao Y, Yang W, Chen M, Yan Z, Xia S, Lu C, Xu M, Ji J. Dual-energy computed tomography for predicting histological grading and survival in patients with pancreatic ductal adenocarcinoma. Eur Radiol 2025;35:2818-2832. [PMID: 39414655 DOI: 10.1007/s00330-024-11109-4] [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] [Revised: 08/07/2024] [Accepted: 09/24/2024] [Indexed: 10/18/2024]

Abstract

OBJECTIVES

We evaluated the value of dual-energy computed tomography (DECT) parameters derived from pancreatic ductal adenocarcinoma (PDAC) to discriminate between high- and low-grade tumors and predict overall survival (OS) in patients.

METHODS

Data were retrospectively collected from 169 consecutive patients with pathologically confirmed PDAC who underwent third-generation dual-source DECT enhanced dual-phase scanning before surgery between January 2017 and March 2023. Patients with prior treatments, other malignancies, small tumors, or poor-quality scans were excluded. Two radiologists evaluated three clinical and seven radiological features and measured sixteen DECT-derived parameters. Univariate and multivariate analyses were applied to select independent predictors. A prediction model and a corresponding nomogram were developed, and the area under the curve (AUC), calibration, and clinical applicability were assessed. The correlations between factors and OS were evaluated using Kaplan-Meier survival and Cox regression analyses.

RESULTS

One hundred sixty-nine patients were randomly divided into training (n = 118) and validation (n = 51) cohorts, among which 43 (36.4%) and 19 (37.3%) had high-grade PDAC confirmed by pathology, respectively. The vascular invasion, normalized iodine concentration in the venous phase, and effective atomic number in the venous phase were independent predictors for histological grading. A nomogram was constructed to predict the risk of high-grade tumors in PDAC, with AUCs of 0.887 and 0.844 in the training and validation cohorts, respectively. The nomogram exhibited good calibration and was more beneficial than a single parameter in both cohorts. Pathological- and nomoscore-predicted high-grade PDACs were associated with poor OS (all p < 0.05).

CONCLUSIONS

The nomogram, which combines DECT parameters and radiological features, can predict the histological grade and OS in patients with PDAC before surgery.

KEY POINTS

Question Preoperative determination of histological grade in PDAC is crucial for guiding treatment, yet current methods are invasive and limited. Findings A DECT-based nomogram combining vascular invasion, normalized iodine concentration, and effective atomic number accurately predicts histological grade and OS in PDAC patients. Clinical relevance The DECT-based nomogram is a reliable, non-invasive tool for predicting histological grade and OS in PDAC. It provides essential information to guide personalized treatment strategies, potentially improving patient management and outcomes.

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Affiliation(s)

Weiyue Chen Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Guihan Lin Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Xia Li Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Ye Feng Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Weibo Mao Department of Pathology, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China
Chunli Kong Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Yumin Hu Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Yang Gao Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Weibin Yang Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Minjiang Chen Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Zhihan Yan Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China
Shuiwei Xia Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Chenying Lu Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Min Xu Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China
Jiansong Ji Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China. Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui, China.

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Zhang J, Su C, Zhang Y, Gao R, Lu X, Liang J, Liu H, Tian S, Zhang Y, Ye Z. Spectral CT-based nomogram for preoperative prediction of Lauren classification in locally advanced gastric cancer: a prospective study. Eur Radiol 2025;35:2794-2805. [PMID: 39532722 DOI: 10.1007/s00330-024-11163-y] [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: 04/17/2024] [Revised: 08/24/2024] [Accepted: 09/26/2024] [Indexed: 11/16/2024]

Abstract

OBJECTIVES

To develop a nomogram based on clinical features and spectral quantitative parameters to preoperatively predict the Lauren classification for locally advanced gastric cancer (LAGC).

METHODS

Patients diagnosed with LAGC by postoperative pathology who underwent abdominal triple-phase enhanced spectral computed tomography (CT) were prospectively enrolled in this study between June 2023 and December 2023. All the patients were categorized into intestinal- and diffuse-type groups according to the Lauren classification. Traditional characteristics, including demographic information, serum tumor markers, gastroscopic pathology, and image semantic features, were collected. Spectral quantitative parameters, including iodine concentration (IC), effective atomic number (Zeff), and slope of the energy spectrum curve from 40 keV to 70 keV (λ), were measured three times for each patient by two blinded radiologists in arterial/venous/delayed phases (AP/VP/DP). Differences in traditional features and spectral quantitative parameters between the two groups were compared using univariable analysis. Independent predictors of the Lauren classification of LAGC were screened using multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminating capability. Ultimately, the nomogram, including clinical features and spectral CT quantitative parameters, was developed.

RESULTS

Gender, nIC in AP (APnIC), and λ in DP (λd) were independent predictors for Lauren classification. The nomogram based on these indicators produced the best performance with an area under the curve of 0.841 (95% confidence interval: 0.749-0.932), specificity of 85.3%, accuracy of 76.4%, and sensitivity of 68.4%.

CONCLUSION

The nomogram based on clinical features and spectral CT quantitative parameters exhibits great potential in the preoperative and non-invasive assessment of Lauren classification for LAGC.

KEY POINTS

Question Can the proposed nomogram, integrating clinical features and spectral quantitative parameters, preoperatively predict the Lauren classification in locally advanced gastric cancer (LAGC)? Findings The nomogram, based on gender, arterial phase normalized iodine concentration, and slope of the energy spectrum curve in the delayed phase showed satisfactory predictive ability. Clinical relevance The established nomogram could contribute to guiding individualized treatment strategies and risk stratification in patients by predicting the Lauren classification for LAGC before surgery.

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Liu Y, Chen Y, Shu J, Zhang Z, You Y, Yue S, Ji Q, Chen K, Liu Y, Duan B, Yu B, Kou S, Pang X, Wang W, Yang L, Zhao Z, Gao J. Dual-energy CT for predicting progression-free survival of locally advanced gastric cancer after gastrectomy: Insights into tumor angiogenesis. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2025;51:110017. [PMID: 40222263 DOI: 10.1016/j.ejso.2025.110017] [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: 12/11/2024] [Revised: 03/12/2025] [Accepted: 04/03/2025] [Indexed: 04/15/2025]

Abstract

OBJECTIVES

To investigate preoperative dual-energy CT (DECT)-derived independent risk factors affecting progression-free survival (PFS) in patients with locally advanced gastric cancer (LAGC) undergoing gastrectomy, and to reveal the underlying histopathologic changes.

METHODS

This prospective study included patients who underwent preoperative DECT scan and gastrectomy. Clinical data, DECT-derived morphological characteristics and iodine-related parameters were comprehensively collected. Univariate and multivariate analyses were carried out to identify independent risk factors associated with PFS. The prognostic performance of various parameters was evaluated using the bootstrap-based consistency index (C-index) and time-dependent receiver operating characteristic (ROC) analysis. Kaplan-Meier curves were used to assess the differences in survival analysis. The histopathologic underpinnings of the DECT-based combined parameter for evaluating PFS were explored.

RESULTS

120 LAGC patients (63.3 ± 10.9 years; 94 men) were analyzed. Age, arterial enhancement fraction (AEF), serosal invasion, and tumor thickness were identified as preoperative independent risk factors affecting PFS (all p < 0.05). The combined parameters based on these risk factors achieved a C-index of 0.75, significantly or slightly superior to that of any single risk factor (all p < 0.05) or postoperative pathological staging (C-index, 0.67; p > 0.05). For predicting the 0.5-, 1- and 2-year PFS, the combined parameter had an area-under-the-curve (AUC) of 0.72, 0.77, and 0.74, respectively. PFS significantly differed between patients of high- and low-risks assessed with the combined parameter (p < 0.001). Histopathologically, the combined parameter was associated with tumor microvessel density (r = 0.31, p < 0.001).

CONCLUSION

The combination of DECT-derived morphological characteristics, iodine-related parameters, and clinical data helped accurately stratify PFS in LAGC before surgery and is associated with tumor angiogenesis.

CLINICAL RELEVANCE STATEMENT

Dual-energy CT was promising in the preoperative evaluation of the progression-free survival in LAGC patients after gastrectomy.

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Affiliation(s)

Yiyang Liu Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China; Henan Key Laboratory of CT Imaging, Zhengzhou, China; The First Clinical School of Medicine, Zhengzhou University, Zhengzhou, 450052, China
Yusong Chen Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China; Henan Key Laboratory of CT Imaging, Zhengzhou, China; The First Clinical School of Medicine, Zhengzhou University, Zhengzhou, 450052, China
Jiao Shu The First Clinical School of Medicine, Zhengzhou University, Zhengzhou, 450052, China; Department of Pathology, The First Affiliated Hospital of Zhengzhou University, China
Zhe Zhang The First Clinical School of Medicine, Zhengzhou University, Zhengzhou, 450052, China; Department of Pathology, The First Affiliated Hospital of Zhengzhou University, China
Yaru You Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China; Henan Key Laboratory of CT Imaging, Zhengzhou, China; The First Clinical School of Medicine, Zhengzhou University, Zhengzhou, 450052, China
Songwei Yue Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China; Henan Key Laboratory of CT Imaging, Zhengzhou, China
Qingyu Ji Department of Radiology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, 014030, China
Kuisheng Chen Department of Pathology, The First Affiliated Hospital of Zhengzhou University, China
Yao Liu Department of Pathology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, China
Bo Duan Department of Radiology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, 014030, China
Baiqing Yu Department of Medical Oncology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, China
Songzi Kou The First Clinical School of Medicine, Zhengzhou University, Zhengzhou, 450052, China; Department of Pathology, The First Affiliated Hospital of Zhengzhou University, China
Xia Pang Department of Pathology, The First Affiliated Hospital of Zhengzhou University, China
Weitao Wang Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
Li Yang Department of Pathology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, China.
Zihao Zhao Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China; Henan Key Laboratory of CT Imaging, Zhengzhou, China.
Jianbo Gao Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China; Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China; Henan Key Laboratory of CT Imaging, Zhengzhou, China; The First Clinical School of Medicine, Zhengzhou University, Zhengzhou, 450052, China.

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Yang W, Shi H, Li M, Qiao X, Li L, Liu S. Dual-energy CT for predicting serosal invasion in gastric cancer and subtype analysis. Abdom Radiol (NY) 2024:10.1007/s00261-024-04735-5. [PMID: 39690282 DOI: 10.1007/s00261-024-04735-5] [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: 10/05/2024] [Revised: 11/26/2024] [Accepted: 11/30/2024] [Indexed: 12/19/2024]

Liu Y, Yuan M, Zhao Z, Zhao S, Chen X, Fu Y, Shi M, Chen D, Hou Z, Zhang Y, Du J, Zheng Y, Liu L, Li Y, Gao B, Ji Q, Li J, Gao J. A quantitative model using multi-parameters in dual-energy CT to preoperatively predict serosal invasion in locally advanced gastric cancer. Insights Imaging 2024;15:264. [PMID: 39480564 PMCID: PMC11528085 DOI: 10.1186/s13244-024-01844-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Accepted: 10/09/2024] [Indexed: 11/02/2024] Open

Abstract

OBJECTIVES

To develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT).

MATERIALS AND METHODS

A total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis.

RESULTS

A quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p < 0.001). Besides, disease-free survival (DFS) (TC, p = 0.015; and VC1, p = 0.043) could be stratified using this quantitative model.

CONCLUSION

The proposed quantitative model using multi-parameters in DECT accurately predicts serosal invasion for LAGC and showed a significant correlation with the DFS of patients.

CRITICAL RELEVANCE STATEMENT

This quantitative model from dual-energy CT is a useful tool for predicting the serosal invasion of locally advanced gastric cancer.

KEY POINTS

Serosal invasion is a poor prognostic factor in locally advanced gastric cancer that may be predicted by DECT. DECT quantitative model for predicting serosal invasion was significantly and positively correlated with pathologic T stages. This quantitative model was associated with patient postoperative disease-free survival.

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Affiliation(s)

Yiyang Liu Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China Henan Key Laboratory of CT Imaging, Zhengzhou, China
Mengchen Yuan Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China Henan Key Laboratory of CT Imaging, Zhengzhou, China
Zihao Zhao Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China
Shuai Zhao Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China Henan Key Laboratory of CT Imaging, Zhengzhou, China
Xuejun Chen Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, 450008, China
Yang Fu Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou, University, Zhengzhou, 450052, China
Mengwei Shi Department of Radiology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, 014030, China
Diansen Chen Department of Radiology, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, 471003, China
Zongbin Hou Department of Radiology, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, 471003, China
Yongqiang Zhang CT Diagnostic Center, Sanmenxia Central Hospital, Sanmenxia, 472000, China
Juan Du CT Diagnostic Center, Sanmenxia Central Hospital, Sanmenxia, 472000, China
Yinshi Zheng Medical Imaging Center, The First People's Hospital of Shangqiu City, Shangqiu, 476100, China
Luhao Liu College of Acupuncture and Massage, Henan University of Chinese Medicine, Zhengzhou, 450046, China
Yiming Li Medical Imaging Center, The First People's Hospital of Shangqiu City, Shangqiu, 476100, China
Beijun Gao Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
Qingyu Ji Department of Radiology, The Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, 014030, China.
Jing Li Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, 450008, China.
Jianbo Gao Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China. Henan International Joint Laboratory of Medical Imaging, Zhengzhou, China. Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, Zhengzhou, China. Henan Key Laboratory of CT Imaging, Zhengzhou, China.

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Zhang WL, Sun J, Huang RF, Zeng Y, Chen S, Wang XP, Chen JH, Chen YB, Zhu CS, Ye ZS, Xiao YP. Whole-volume histogram analysis of spectral-computed tomography iodine maps characterizes HER2 expression in gastric cancer. World J Gastroenterol 2024;30:4211-4220. [PMID: 39493333 PMCID: PMC11525878 DOI: 10.3748/wjg.v30.i38.4211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 09/04/2024] [Accepted: 09/18/2024] [Indexed: 09/29/2024] Open

Abstract

BACKGROUND

Although surgery remains the primary treatment for gastric cancer (GC), the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance. HER2 overexpression occurs in approximately 15%-20% of advanced GC cases, directly affecting treatment-related decisions. Spectral-computed tomography (sCT) enables the quantification of material compositions, and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth, angiogenesis, and response to systemic chemotherapy. No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps (IMs).

AIM

To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.

METHODS

This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations. Nineteen parameters were extracted via sCT IM histogram analysis: The minimum, maximum, mean, standard deviation, variance, coefficient of variation, skewness, kurtosis, entropy, percentiles (1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 99th), and lesion volume. Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed. Receiver operating characteristic curves were used to evaluate the parameters' diagnostic performance.

RESULTS

Values for the histogram parameters of the maximum, mean, standard deviation, variance, entropy, and percentiles were significantly lower in the HER2+ group than in the HER2- group (all P < 0.05). The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue (P = 0.001 and 0.023, respectively). The 99th percentile had the largest area under the curve for GC HER2 status identification (0.740), with 76.2%, sensitivity, 65.0% specificity, and 67.3% accuracy. All sCT IM histogram parameters correlated positively with the GC HER2 status (r = 0.237-0.337, P = 0.001-0.017).

CONCLUSION

Whole-lesion histogram parameters derived from sCT IM analysis, and especially the 99th percentile, can serve as imaging biomarkers of HER2 overexpression in GC.

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Affiliation(s)

Wei-Ling Zhang Department of Radiology, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Jing Sun Department of Radiology, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Rong-Fang Huang Department of Pathology, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Yi Zeng Department of Gastric Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Shu Chen Department of Gastric Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Xiao-Peng Wang Department of Gastric Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Jin-Hu Chen Department of Gastric Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Yun-Bin Chen Department of Radiology, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Chun-Su Zhu Department of Epidemiology, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
Zai-Sheng Ye Department of Gastric Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China
You-Ping Xiao Department of Radiology, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital (Fujian Branch of Fudan University Affiliated Cancer Hospital), Fuzhou 350014, Fujian Province, China

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Qin L, Chen W, Ye Y, Yi H, Pang W, Long B, Wang Y, Ye T, Li L. Prediction of HER2 Expression in Gastric Adenocarcinoma Based On Preoperative Noninvasive Multimodal ¹⁸F-FDG PET/CT Imaging. Acad Radiol 2024;31:3200-3211. [PMID: 38302386 DOI: 10.1016/j.acra.2024.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/02/2024] [Accepted: 01/11/2024] [Indexed: 02/03/2024]

Affiliation(s)

Lilin Qin Department of Nuclear Medicine, Zhejiang Cancer Hospital, Banshan Street 1, Hangzhou, Zhejiang 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
Wujie Chen Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
Yuanxin Ye Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China; Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
Heqing Yi Department of Nuclear Medicine, Zhejiang Cancer Hospital, Banshan Street 1, Hangzhou, Zhejiang 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
Weiqiang Pang Department of Nuclear Medicine, Zhejiang Cancer Hospital, Banshan Street 1, Hangzhou, Zhejiang 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
Bin Long Department of Nuclear Medicine, Zhejiang Cancer Hospital, Banshan Street 1, Hangzhou, Zhejiang 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
Yun Wang Department of Nuclear Medicine, Zhejiang Cancer Hospital, Banshan Street 1, Hangzhou, Zhejiang 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
Ting Ye Department of Nuclear Medicine, Zhejiang Cancer Hospital, Banshan Street 1, Hangzhou, Zhejiang 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
Linfa Li Department of Nuclear Medicine, Zhejiang Cancer Hospital, Banshan Street 1, Hangzhou, Zhejiang 310022, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.

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Xia H, Chen Y, Cao A, Wang Y, Huang X, Zhang S, Gu Y. Differentiating between benign and malignant breast lesions using dual-energy CT-based model: development and validation. Insights Imaging 2024;15:173. [PMID: 38981953 PMCID: PMC11233492 DOI: 10.1186/s13244-024-01752-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/16/2024] [Indexed: 07/11/2024] Open

Abstract

OBJECTIVES

To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT.

MATERIALS AND METHODS

This study prospectively enrolled patients with suspected breast cancer who underwent dual-phase contrast-enhanced DECT from July 2022 to July 2023. Breast lesions were randomly divided into the training and test cohorts at a ratio of 7:3. Clinical characteristics, DECT-based morphological features, and DECT quantitative parameters were collected. Univariate analyses and multivariate logistic regression were performed to determine independent predictors of benign and malignant breast lesions. An individualized model was constructed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the model, whose calibration and clinical usefulness were assessed by calibration curve and decision curve analysis.

RESULTS

This study included 200 patients (mean age, 49.9 ± 11.9 years; age range, 22-83 years) with 222 breast lesions. Age, lesion shape, and the effective atomic number (Zeff) in the venous phase were significant independent predictors of breast lesions (all p < 0.05). The discriminative power of the model incorporating these three factors was high, with AUCs of 0.844 (95%CI 0.764-0.925) and 0.791 (95% CI 0.647-0.935) in the training and test cohorts, respectively. The constructed model showed a preferable fitting (all p > 0.05 by the Hosmer-Lemeshow test) and provided enhanced net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts.

CONCLUSION

The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT.

CRITICAL RELEVANCE STATEMENT

The combination of clinical and morphological characteristics and DECT-derived parameter have the potential to identify benign and malignant breast lesions and it may be useful for incidental breast lesions on DECT to decide if further work-up is needed.

KEY POINTS

It is important to characterize incidental breast lesions on DECT for patient management. DECT-based model can differentiate benign and malignant breast lesions with good performance. DECT-based model is a potential tool for distinguishing breast lesions detected on DECT.

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Li Y, Dai WG, Lin Q, Wang Z, Xu H, Chen Y, Wang J. Predicting human epidermal growth factor receptor 2 status of patients with gastric cancer by computed tomography and clinical features. Gastroenterol Rep (Oxf) 2024;12:goae042. [PMID: 38726026 PMCID: PMC11078894 DOI: 10.1093/gastro/goae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 04/05/2024] [Accepted: 04/15/2024] [Indexed: 05/12/2024] Open

Abstract

Background

There have been no studies on predicting human epidermal growth factor receptor 2 (HER2) status in patients with resectable gastric cancer (GC) in the neoadjuvant and perioperative settings. We aimed to investigate the use of preoperative contrast-enhanced computed tomography (CECT) imaging features combined with clinical characteristics for predicting HER2 expression in GC.

Methods

We retrospectively enrolled 301 patients with GC who underwent curative resection and preoperative CECT. HER2 status was confirmed by postoperative immunohistochemical analysis with or without fluorescence in situ hybridization. A prediction model was developed using CECT imaging features and clinical characteristics that were independently associated with HER2 status using multivariate logistic regression analysis. Receiver operating characteristic curves were constructed and the performance of the prediction model was evaluated. The bootstrap method was used for internal validation.

Results

Three CECT imaging features and one serum tumor marker were independently associated with HER2 status in GC: enhancement ratio in the arterial phase (odds ratio [OR] = 4.535; 95% confidence interval [CI], 2.220-9.264), intratumoral necrosis (OR = 2.64; 95% CI, 1.180-5.258), tumor margin (OR = 3.773; 95% CI, 1.968-7.235), and cancer antigen 125 (CA125) level (OR = 5.551; 95% CI, 1.361-22.651). A prediction model derived from these variables showed an area under the receiver operating characteristic curve of 0.802 (95% CI, 0.740-0.864) for predicting HER2 status in GC. The established model was stable, and the parameters were accurately estimated.

Conclusions

Enhancement ratio in the arterial phase, intratumoral necrosis, tumor margin, and CA125 levels were independently associated with HER2 status in GC. The prediction model derived from these factors may be used preoperatively to estimate HER2 status in GC and guide clinical treatment.

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Hong Y, Zhong L, Lv X, Liu Q, Fu L, Zhou D, Yu N. Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects. Front Mol Biosci 2023;10:1284549. [PMID: 37954980 PMCID: PMC10634296 DOI: 10.3389/fmolb.2023.1284549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 09/26/2023] [Indexed: 11/14/2023] Open

Li L, Huang W, Hou P, Li W, Feng M, Liu Y, Gao J. A computed tomography-based preoperative risk scoring system to distinguish lymphoepithelioma-like gastric carcinoma from non-lymphoepithelioma-like gastric carcinoma. Front Oncol 2022;12:872814. [PMID: 36185305 PMCID: PMC9522524 DOI: 10.3389/fonc.2022.872814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open

Du KP, Huang WP, Liu SY, Chen YJ, Li LM, Liu XN, Han YJ, Zhou Y, Liu CC, Gao JB. Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction. World J Gastroenterol 2022;28:4363-4375. [PMID: 36159013 PMCID: PMC9453771 DOI: 10.3748/wjg.v28.i31.4363] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/11/2022] [Accepted: 07/25/2022] [Indexed: 02/06/2023] Open

Abstract

BACKGROUND

The biological behavior of carcinoma of the esophagogastric junction (CEGJ) is different from that of gastric or esophageal cancer. Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with CEGJ is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens.

AIM

To establish and evaluate computed tomography (CT)-based multiscale and multiphase radiomics models to distinguish SCCEG and AEG preoperatively.

METHODS

We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (n = 130) and AEG (n = 130). The data were divided into either a training (n = 182) or a test group (n = 78) at a ratio of 7:3. A total of 1409 radiomics features were separately extracted from two dimensional (2D) or three dimensional (3D) regions of interest in arterial and venous phases. Intra-/inter-observer consistency analysis, correlation analysis, univariate analysis, least absolute shrinkage and selection operator regression, and backward stepwise logical regression were applied for feature selection. Totally, six logistic regression models were established based on 2D and 3D multi-phase features. The receiver operating characteristic curve analysis, the continuous net reclassification improvement (NRI), and the integrated discrimination improvement (IDI) were used for assessing model discrimination performance. Calibration and decision curves were used to assess the calibration and clinical usefulness of the model, respectively.

RESULTS

The 2D-venous model (5 features, AUC: 0.849) performed better than 2D-arterial (5 features, AUC: 0.808). The 2D-arterial-venous combined model could further enhance the performance (AUC: 0.869). The 3D-venous model (7 features, AUC: 0.877) performed better than 3D-arterial (10 features, AUC: 0.876). And the 3D-arterial-venous combined model (AUC: 0.904) outperformed other single-phase-based models. The venous model showed a positive improvement compared with the arterial model (NRI > 0, IDI > 0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (P < 0.05). Decision curve analysis showed that combined 3D-arterial-venous model and 3D-venous model had a higher net clinical benefit within the same threshold probability range in the test group.

CONCLUSION

The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.

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Singh T, Gupta P. Role of Dual-Energy Computed Tomography in Gallbladder Disease: A Review. JOURNAL OF GASTROINTESTINAL AND ABDOMINAL RADIOLOGY 2022. [DOI: 10.1055/s-0042-1743173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open

Liu YY, Zhang H, Wang L, Lin SS, Lu H, Liang HJ, Liang P, Li J, Lv PJ, Gao JB. Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study. Front Oncol 2021;11:740732. [PMID: 34604085 PMCID: PMC8480311 DOI: 10.3389/fonc.2021.740732] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/24/2021] [Indexed: 12/24/2022] Open

Abstract

Objective

To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).

Methods

A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.

Result

Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model.

Conclusion

The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

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