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Lu M, Yan Z, Qu Q, Zhu G, Xu L, Liu M, Jiang J, Gu C, Chen Y, Zhang T, Zhang X. Diagnostic Model for Proliferative HCC Using LI-RADS: Assessing Therapeutic Outcomes in Hepatectomy and TKI-ICI Combination. J Magn Reson Imaging 2024. [PMID: 38647041 DOI: 10.1002/jmri.29400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/06/2024] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Proliferative hepatocellular carcinoma (HCC), aggressive with poor prognosis, and lacks reliable MRI diagnosis. PURPOSE To develop a diagnostic model for proliferative HCC using liver imaging reporting and data system (LI-RADS) and assess its prognostic value. STUDY TYPE Retrospective. POPULATION 241 HCC patients underwent hepatectomy (90 proliferative HCCs: 151 nonproliferative HCCs), divided into the training (N = 167) and validation (N = 74) sets. 57 HCC patients received combination therapy with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). FIELD STRENGTH/SEQUENCE 3.0 T, T1- and T2-weighted, diffusion-weighted, in- and out-phase, T1 high resolution isotropic volume excitation and dynamic gadoxetic acid-enhanced imaging. ASSESSMENT LI-RADS v2018 and other MRI features (intratumoral artery, substantial hypoenhancing component, hepatobiliary phase peritumoral hypointensity, and irregular tumor margin) were assessed. A diagnostic model for proliferative HCC was established, stratifying patients into high- and low-risk groups. Follow-up occurred every 3-6 months, and recurrence-free survival (RFS), progression-free survival (PFS) and overall survival (OS) in different groups were compared. STATISTICAL TESTS Fisher's test or chi-square test, t-test or Mann-Whitney test, logistic regression, Harrell's concordance index (C-index), Kaplan-Meier curves, and Cox proportional hazards. Significance level: P < 0.05. RESULTS The diagnostic model, incorporating corona enhancement, rim arterial phase hyperenhancement, infiltrative appearance, intratumoral artery, and substantial hypoenhancing component, achieved a C-index of 0.823 (training set) and 0.804 (validation set). Median follow-up was 32.5 months (interquartile range [IQR], 25.1 months) for postsurgery patients, and 16.8 months (IQR: 13.2 months) for combination-treated patients. 99 patients experienced recurrence, and 30 demonstrated tumor nonresponse. Differences were significant in RFS and OS rates between high-risk and low-risk groups post-surgery (40.3% vs. 65.8%, 62.3% vs. 90.1%, at 5 years). In combination-treated patients, PFS rates differed significantly (80.6% vs. 7.7% at 2 years). DATA CONCLUSION The MR-based model could pre-treatment identify proliferative HCC and assist in prognosis evaluation. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Mengtian Lu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Zuyi Yan
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Qi Qu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Guodong Zhu
- Department of Hepatobiliary Surgery, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Lei Xu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Maotong Liu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Jifeng Jiang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Chunyan Gu
- Department of Pathology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Ying Chen
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Xueqin Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
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Wang F, Qin Y, Wang ZM, Yan CY, He Y, Liu D, Wen L, Zhang D. A Dynamic Online Nomogram Based on Gd-EOB-DTPA-Enhanced MRI and Inflammatory Biomarkers for Preoperative Prediction of Pathological Grade and Stratification in Solitary Hepatocellular Carcinoma: A Multicenter Study. Acad Radiol 2024:S1076-6332(24)00126-0. [PMID: 38494348 DOI: 10.1016/j.acra.2024.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/24/2023] [Accepted: 02/22/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is an inflammatory cancer. We aimed to explore whether preoperative inflammation biomarkers compared to the gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI can add complementary value for predicting HCC pathological grade, and to develop a dynamic nomogram to predict solitary HCC pathological grade. METHODS 331 patients from the Institution A were divided chronologically into the training cohort (n = 231) and internal validation cohort (n = 100), and recurrence-free survival (RFS) was determined to follow up after surgery. 79 patients from the Institution B served as the external validation cohort. Overall, 410 patients were analyzed as the complete dataset cohort. Least absolute shrinkage and selection operator (LASSO) and multivariate Logistic regression were used to gradually filter features for model construction. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate model's performance. RESULTS Five models of the inflammation, imaging, inflammation+AFP, inflammation+imaging and nomogram were developed. Adding inflammation to imaging model can improve the AUC in training cohort (from 0.802 to 0.869), internal validation cohort (0.827 to 0.870), external validation cohort (0.740 to 0.802) and complete dataset cohort (0.739 to 0.788), and obtain more net benefit. The nomogram had excellent performance for predicting high-grade HCC in four cohorts (AUCs: 0.882 vs. 0.869 vs. 0.829 vs. 0.806) with a good calibration, and accessed at https://predict-solitaryhccgrade.shinyapps.io/DynNomapp/. Additionally, the nomogram obtained an AUC of 0.863 (95% CI 0.797-0.913) for predicting high-grade HCC in the HCC≤ 3 cm. Kaplan-Meier survival curves demonstrated that the nomogram owned excellent stratification for HCC grade (P < 0.0001). CONCLUSION This easy-to-use dynamic online nomogram hold promise for use as a noninvasive tool in prediction HCC grade with high accuracy and robustness.
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Affiliation(s)
- Fei Wang
- Department of Radiology, XinQiao Hospital of Army Medical University, No.83, Xinqiao Central Street, Shapingba District, Chongqing 400037, China
| | - Yuan Qin
- Department of Radiology, Chongqing University Three Gorges Hospital, No.165, Xincheng Road, Wanzhou District, Chongqing 404031, China
| | - Zheng Ming Wang
- Department of Radiology, XinQiao Hospital of Army Medical University, No.83, Xinqiao Central Street, Shapingba District, Chongqing 400037, China
| | - Chun Yue Yan
- Department of gynaecology and obstetrics, Luzhou People's Hospital, No.316, Jiugu Avenue, Jiangyang District, Luzhou 646000, China
| | - Ying He
- Department of Radiology, XinQiao Hospital of Army Medical University, No.83, Xinqiao Central Street, Shapingba District, Chongqing 400037, China
| | - Dan Liu
- Department of Radiology, XinQiao Hospital of Army Medical University, No.83, Xinqiao Central Street, Shapingba District, Chongqing 400037, China
| | - Li Wen
- Department of Radiology, XinQiao Hospital of Army Medical University, No.83, Xinqiao Central Street, Shapingba District, Chongqing 400037, China
| | - Dong Zhang
- Department of Radiology, XinQiao Hospital of Army Medical University, No.83, Xinqiao Central Street, Shapingba District, Chongqing 400037, China.
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Yan Y, Lin XS, Ming WZ, Chuan ZQ, Hui G, Juan SY, Shuang W, Yang Fan LV, Dong Z. Radiomic Analysis Based on Gd-EOB-DTPA Enhanced MRI for the Preoperative Prediction of Ki-67 Expression in Hepatocellular Carcinoma. Acad Radiol 2024; 31:859-869. [PMID: 37689559 DOI: 10.1016/j.acra.2023.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 09/11/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a random forest model based on radiomic features in Gd-EOB-DTPA enhanced MRI for predicting the Ki-67 expression in solitary HCC. MATERIALS AND METHODS This retrospective study analyzed 258 patients with solitary HCC. Significant clinicoradiological factors were identified through univariate and multivariate analyses for distinguishing HCC with high (>20%) and low (≤20%) Ki-67 expression. Radiomic features were extracted at Gd-EOB-DTPA enhanced MRI. The recursive feature elimination (RFE) strategy was employed to screen robust radiomic features, and the Random Forest (RF) algorithm was utilized to rank radiomic features and construct prediction models. The AUC, accuracy, precision, recall, and f1-score were used to evaluate the performance of RF models. RESULTS Multivariate analysis identified serum AFP level, tumor size, growth type, and peritumoral enhancement as independent predictors for HCC with high Ki-67 expression. The clinicoradiological-radiomic model that incorporated the clinicoradiological predictors and the top ten radiomic features outperformed the clinicoradiological model in the training set (AUCs 0.876 vs. 0.780; p < 0.001), though the test set did not have a statistical significance (AUCs 0.809 vs. 0.723; p = 0.123). The addition of clinicoradiological predictors did not yield a significant improvement in the performance of radiomic features in both sets (training, p = 0.692; test, p = 0.229). Decision curve analysis further confirmed the clinical utility of the RF models. CONCLUSION The RF models based on radiomic features of Gd-EOB-DTPA enhanced MRI achieved satisfactory performance in preoperatively predicting Ki-67 expression in HCC.
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Affiliation(s)
- Yang Yan
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.)
| | - Xiao Shi Lin
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.)
| | - Wang Zheng Ming
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.)
| | - Zhang Qi Chuan
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.)
| | - Gan Hui
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.)
| | - Sun Ya Juan
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.)
| | - Wang Shuang
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.)
| | - L V Yang Fan
- Department of Pathology, XinQiao Hospital, Army Medical University, Chongqing, People's Republic of China (L.Y.F.)
| | - Zhang Dong
- Department of Radiology, XinQiao Hospital, Army Medical University, Chongqing 400037, People's Republic of China (Y.Y., X.S.L., W.Z.M., Z.Q.C., G.H., S.Y.J., W.S., Z.D.).
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Zhou L, Qu Y, Quan G, Zuo H, Liu M. Nomogram for Predicting Microvascular Invasion in Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced MRI and Intravoxel Incoherent Motion Imaging. Acad Radiol 2024; 31:457-466. [PMID: 37491178 DOI: 10.1016/j.acra.2023.06.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023]
Abstract
RATIONALE AND OBJECTIVES Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but it can only be determined through histopathological results. The aim of this study was to develop and validate a nomogram for preoperative prediction MVI in HCC using gadoxetic acid-enhanced magnetic resonance imaging (MRI) and intravoxel incoherent motion imaging (IVIM). MATERIALS AND METHODS From July 2017 to September 2022, 148 patients with surgically resected HCC who underwent preoperative gadoxetic acid-enhanced MRI and IVIM were included in this retrospective study. Clinical indicators, imaging features, and diffusion parameters were compared between the MVI-positive and MVI-negative groups using the chi-square test, Mann-Whitney U test, and independent sample t test. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance in predicting MVI. Univariate and multivariate analyses were conducted to identify the significant clinical-radiological variables associated with MVI. Subsequently, a predictive nomogram that integrates clinical-radiological risk factors and diffusion parameters was developed and validated. RESULTS Serum alpha-fetoprotein level, tumor size, nonsmooth tumor margin, peritumoral hypo-intensity on hepatobiliary phase (HBP), apparent diffusion coefficient value and D value were statistically significant different between MVI-positive group and MVI-negative group. The results of multivariate analysis identified tumor size (odds ratio [OR], 0.786; 95% confidence interval [CI], 0.675-0.915; P < .01), nonsmooth tumor margin (OR, 2.299; 95% CI, 1.005-5.257; P < .05), peritumoral hypo-intensity on HBP (OR, 2.786; 95% CI, 1.141-6.802; P < .05) and D (OR, 0.293; 95% CI,0.089-0.964; P < .05) was the independent risk factor for the status of MVI. In ROC analysis, the combination of peritumoral hypo-intensity on HBP and D demonstrated the highest area under the curve value (0.902) in prediction MVI status, with sensitivity 92.8% and specificity 87.7%. The nomogram exhibited excellent predictive performance with C-index of 0.936 (95% CI 0.895-0.976) in the patient cohort, and had well-fitted calibration curve. CONCLUSION The nomogram incorporating clinical-radiological risk factors and diffusion parameters achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.
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Affiliation(s)
- Lisui Zhou
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.)
| | - Yuan Qu
- Department of Radiology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China (Y.Q.)
| | - Guangnan Quan
- MR Research China, GE Healthcare China, Beijing, China (G.Q.)
| | - Houdong Zuo
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.)
| | - Mi Liu
- Department of Radiology, Chengdu Xinhua Hospital Affiliated to North Sichuan Medical College, Chengdu, China (L.Z., H.Z., M.L.).
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Zhou L, Chen Y, Li Y, Wu C, Xue C, Wang X. Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis. Front Oncol 2024; 13:1323534. [PMID: 38234405 PMCID: PMC10792117 DOI: 10.3389/fonc.2023.1323534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024] Open
Abstract
Background Radiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC. Methods Original studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression. Results We identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86-0.92) in the training set, and 0.87 (95% CI: 0.82-0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81-0.90) in the training set, and 0.79 (95% CI: 0.73-0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity (I 2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased. Conclusion Radiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.
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Affiliation(s)
- Lu Zhou
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Yiheng Chen
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Yan Li
- Traditional Chinese Medicine (Zhong Jing) School, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Chaoyong Wu
- Shenzhen Hospital of Beijing University of Chinese Medicine, Shenzhen, China
| | - Chongxiang Xue
- Graduate School, Beijing University of Chinese Medicine, Beijing, China
| | - Xihong Wang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China
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Liu HF, Wang M, Lu YJ, Wang Q, Lu Y, Xing F, Xing W. CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning. Acad Radiol 2023:S1076-6332(23)00659-1. [PMID: 38057182 DOI: 10.1016/j.acra.2023.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023]
Abstract
RATIONALE AND OBJECTIVES To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting microvascular invasion (MVI) and pathological differentiation in hepatocellular carcinoma (HCC). METHODS CEMRI images were retrospectively obtained from 277 HCCs in 265 patients. Habitat analysis and DL features were extracted from the CEMRI images and selected with the least absolute shrinkage and selection operator approach to develop ITH and DL models, respectively, and these robust features were then integrated to design a fusion model for predicting MVI and poorly differentiated HCC (pHCC). The predictive value of the three models was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The training and validation sets comprised 221 HCCs and 56 HCCs, respectively. The ITH and DL models presented AUC values of (0.90 vs. 0.87) for predicting MVI in the training set, with AUC values of 0.86 and 0.83 in the validation set. The AUC values of the ITH model to predict pHCC were 0.90 and 0.86 in the two sets, respectively; they were 0.84 and 0.80 for the DL model. The fusion model yielded the best performance for predicting MVI and pHCC in the training set (AUC=0.95, 0.90) and in the validation set (AUC=0.89, 0.87), respectively. CONCLUSION A fusion model integrating ITH and DL features derived from CEMRI images can serve as an excellent imaging biomarker for predicting aggressive characteristics in HCC.
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Affiliation(s)
- Hai-Feng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Min Wang
- Department of Anesthesiology, The Second People's Hospital of Changzhou, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China (M.W.)
| | - Yu-Jie Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Yang Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Fei Xing
- Department of Radiology, Nantong Third People's Hospital, Nantong, Jiangsu, China (F.X.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.).
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Qin Q, Deng LP, Chen J, Ye Z, Wu YY, Yuan Y, Song B. The value of MRI in predicting hepatocellular carcinoma with cytokeratin 19 expression: a systematic review and meta-analysis. Clin Radiol 2023; 78:e975-e984. [PMID: 37783612 DOI: 10.1016/j.crad.2023.08.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 10/04/2023]
Abstract
AIM To evaluate the overall diagnostic performance of magnetic resonance imaging (MRI), different image features, and different image analysis methods in predicting hepatocellular carcinoma (HCC) with cytokeratin 19 (CK19) expression. MATERIALS AND METHODS A systematic literature search was performed to identify studies using MRI to predict HCC with CK19 expression between 2012 and 2023. Data were extracted to calculate the pooled sensitivity and specificity. Overall diagnostic performance was assessed using areas under the summary receiver operating characteristic curve (AUC). Subgroup analyses were conducted for specific image features and according to image analysis methods (traditional image feature, radiomics, and combined methods). Z-test statistics was used to analyse the differences in diagnostic performance between combined and individual methods. RESULTS Eleven studies with 14 datasets (1,278 lesions from 1,264 patients) were included. The overall pooled sensitivity, specificity, and AUC with corresponding 95% confidence intervals were estimated to be 0.72 (0.55, 0.85), 0.88 (0.80, 0.93), and 0.89 (0.86, 0.91) for MRI in predicting HCC with CK19 expression. Combined methods had higher sensitivity than image feature methods (0.86 versus 0.54, p=0.001), with no difference in specificity (0.85 versus 0.87, p=0.641). There were no significant differences between radiomics and combined methods regarding sensitivity (p=0.796) and specificity (p=0.535), respectively. CONCLUSION MRI shows moderate sensitivity and high specificity in identifying HCC with CK19 expression. The application of radiomics can improve the sensitivity of MRI in identifying HCC with CK19 expression.
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Affiliation(s)
- Q Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - L P Deng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - J Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Z Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Wu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Yuan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - B Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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