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Li H, Zhang D, Pei J, Hu J, Li X, Liu B, Wang L. Dual-energy computed tomography iodine quantification combined with laboratory data for predicting microvascular invasion in hepatocellular carcinoma: a two-centre study. Br J Radiol 2024; 97:1467-1475. [PMID: 38870535 PMCID: PMC11256957 DOI: 10.1093/bjr/tqae116] [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: 12/06/2023] [Revised: 05/16/2024] [Accepted: 06/05/2024] [Indexed: 06/15/2024] Open
Abstract
OBJECTIVES Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction. METHODS This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score. RESULTS Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively. CONCLUSIONS The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively. ADVANCES IN KNOWLEDGE Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.
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Affiliation(s)
- Huan Li
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Dai Zhang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Jinxia Pei
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Jingmei Hu
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
| | - Bin Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
| | - Longsheng Wang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230601, China
- Medical Imaging Research Center, Anhui Medical University, Hefei, Anhui 230601, China
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Wu L, Li S, Li S, Lin Y, Wei D. Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1416378. [PMID: 39026971 PMCID: PMC11254676 DOI: 10.3389/fonc.2024.1416378] [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: 04/12/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024] Open
Abstract
Background The purpose of this systematic review and meta-analysis is to evaluate the potential significance of radiomics, derived from preoperative magnetic resonance imaging (MRI), in detecting deep stromal invasion (DOI), lymphatic vascular space invasion (LVSI) and lymph node metastasis (LNM) in cervical cancer (CC). Methods A rigorous and systematic evaluation was conducted on radiomics studies pertaining to CC, published in the PubMed database prior to March 2024. The area under the curve (AUC), sensitivity, and specificity of each study were separately extracted to evaluate the performance of preoperative MRI radiomics in predicting DOI, LVSI, and LNM of CC. Results A total of 4, 7, and 12 studies were included in the meta-analysis of DOI, LVSI, and LNM, respectively. The overall AUC, sensitivity, and specificity of preoperative MRI models in predicting DOI, LVSI, and LNM were 0.90, 0.83 (95% confidence interval [CI], 0.75-0.89) and 0.83 (95% CI, 0.74-0.90); 0.85, 0.80 (95% CI, 0.73-0.86) and 0.75 (95% CI, 0.66-0.82); 0.86, 0.79 (95% CI, 0.74-0.83) and 0.80 (95% CI, 0.77-0.83), respectively. Conclusion MRI radiomics has demonstrated considerable potential in predicting DOI, LVSI, and LNM in CC, positioning it as a valuable tool for preoperative precision evaluation in CC patients.
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Affiliation(s)
| | | | | | | | - Dayou Wei
- Department of Medical Ultrasound, Maoming People’s Hospital, Maoming, Guangdong, China
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Wang F, Liao HZ, Chen XL, Lei H, Luo GH, Chen GD, Zhao H. Preoperative prediction of microvascular invasion: new insights into personalized therapy for early-stage hepatocellular carcinoma. Quant Imaging Med Surg 2024; 14:5205-5223. [PMID: 39022260 PMCID: PMC11250313 DOI: 10.21037/qims-24-44] [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: 01/09/2024] [Accepted: 05/29/2024] [Indexed: 07/20/2024]
Abstract
Owing to advances in diagnosis and treatment methods over past decades, a growing number of early-stage hepatocellular carcinoma (HCC) diagnoses has enabled a greater of proportion of patients to receive curative treatment. However, a high risk of early recurrence and poor prognosis remain major challenges in HCC therapy. Microvascular invasion (MVI) has been demonstrated to be an essential independent predictor of early recurrence after curative therapy. Currently, biopsy is not generally recommended before treatment to evaluate MVI in HCC according clinical guidelines due to sampling error and the high risk of tumor cell seeding following biopsy. Therefore, the postoperative histopathological examination is recognized as the gold standard of MVI diagnosis, but this lagging indicator greatly impedes clinicians in selecting the optimal effective treatment for prognosis. As imaging can now noninvasively and completely assess the whole tumor and host situation, it is playing an increasingly important role in the preoperative assessment of MVI. Therefore, imaging criteria for MVI diagnosis would be highly desirable for optimizing individualized therapeutic decision-making and achieving a better prognosis. In this review, we summarize the emerging image characteristics of different imaging modalities for predicting MVI. We also discuss whether advances in imaging technique have generated evidence that could be practice-changing and whether advanced imaging techniques will revolutionize therapeutic decision-making of early-stage HCC.
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Affiliation(s)
- Fang Wang
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
- Departments of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Hua-Zhi Liao
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Xiao-Long Chen
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Hao Lei
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Guang-Hua Luo
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Guo-Dong Chen
- Department of Hepatobiliary Pancreatic Surgery, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
| | - Heng Zhao
- Department of Radiology, The First Affiliated Hospital of University of South China, Hengyang Medical School, University of South China, Hengyang, China
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Liu WM, Zhao XY, Gu MT, Song KR, Zheng W, Yu H, Chen HL, Xu XW, Zhou X, Liu AE, Jia NY, Wang PJ. Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma. World J Oncol 2024; 15:58-71. [PMID: 38274720 PMCID: PMC10807913 DOI: 10.14740/wjon1731] [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: 09/19/2023] [Accepted: 11/15/2023] [Indexed: 01/27/2024] Open
Abstract
Background The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.
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Affiliation(s)
- Wan Min Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Xing Yu Zhao
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Meng Ting Gu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kai Rong Song
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Wei Zheng
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Yu
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Lin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiao Wen Xu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiang Zhou
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ai E Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Ning Yang Jia
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Pei Jun Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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Xue Y, Zhang H, Zheng Z, Liu X, Yin J, Zhang J. Predictive performance of radiomics for peritoneal metastasis in patients with gastric cancer: a meta-analysis and radiomics quality assessment. J Cancer Res Clin Oncol 2023; 149:12103-12113. [PMID: 37422882 DOI: 10.1007/s00432-023-05096-0] [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: 05/23/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE The purpose of this meta-analysis is to systematically review the diagnostic performance of radiomic techniques in predicting peritoneal metastasis in patients with gastric cancer, and to evaluate the quality of current research. METHODS We searched PubMed, Web of Science, EBSCO, Embase, and Cochrane databases for relevant studies up to April 3, 2023. Data extraction and quality evaluation were performed by two independent reviewers. Then we performed statistical analysis, including plotting the forest plot and summary receiver operating characteristic (SROC) curve, and source of heterogeneity analysis, through the MIDAS module in Stata 15. We performed meta-regression and subgroup analyses to analyze the sources of heterogeneity. Using the QUADAS-2 scale and the RQS scale to assess the quality of retrieved studies. RESULTS Ten studies with 6199 patients were finally included in our meta-analysis. Pooled sensitivity and specificity were 0.77 (95% confidence interval [CI]: 0.66, 0.86), and 0.88 (95% CI 0.80, 0.93), respectively. The overall AUC was 0.89 (95% CI 0.86, 0.92). The heterogeneity of this meta-analysis was high, with I2 = 88% (95% CI 75,100). The result of meta-regression showed that QUADAS-2 results, RQS results and machine learning method led to heterogeneity in sensitivity and specificity (P < 0.05). Furthermore, the image segmentation area and the presence or absence of combined clinical factors were associated with sensitivity heterogeneity and specificity heterogeneity, respectively. CONCLUSION Undoubtedly, radiomics has potential value in diagnosing peritoneal metastasis of gastric cancer, but the quality of current research is inconsistent, and more standardized and high-quality research is still needed in the future to achieve the transformation of radiomics results into clinical applications.
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Affiliation(s)
- Yasheng Xue
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Haiqiao Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Zhi Zheng
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Xiaoye Liu
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jie Yin
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China
| | - Jun Zhang
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing, 100050, China.
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Nan Y, Xu X, Dong S, Yang M, Li L, Zhao S, Duan Z, Jia J, Wei L, Zhuang H. Consensus on the tertiary prevention of primary liver cancer. Hepatol Int 2023; 17:1057-1071. [PMID: 37369911 PMCID: PMC10522749 DOI: 10.1007/s12072-023-10549-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 05/04/2023] [Indexed: 06/29/2023]
Abstract
To effectively prevent recurrence, improve the prognosis and increase the survival rate of primary liver cancer (PLC) patients with radical cure, the Chinese Society of Hepatology, Chinese Medical Association, invited clinical experts and methodologists to develop the Consensus on the Tertiary Prevention of Primary Liver Cancer, which was based on the clinical and scientific advances on the risk factors, histopathology, imaging finding, clinical manifestation, and prevention of recurrence of PLC. The purpose is to provide a current basis for the prevention, surveillance, early detection and diagnosis, and the effective measures of PLC recurrence.
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Affiliation(s)
- Yuemin Nan
- Department of Traditional and Western Medical Hepatology, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051 China
| | - Xiaoyuan Xu
- Department of Infectious Diseases, Peking University First Hospital, Beijing, 100034 China
| | - Shiming Dong
- Department of Traditional and Western Medical Hepatology, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051 China
| | - Ming Yang
- Peking University People’s Hospital, Peking University Hepatology Institute, Beijing, China
| | - Ling Li
- Department of Intervention, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025 China
| | - Suxian Zhao
- Department of Traditional and Western Medical Hepatology, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051 China
| | - Zhongping Duan
- Artificial Liver Centre, Beijing You-An Hospital, Capital Medical University, Beijing, 100069 China
| | - Jidong Jia
- Liver Research Centre, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050 China
| | - Lai Wei
- Hepatopancreatobiliary Centre, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, 102218 China
| | - Hui Zhuang
- Department of Microbiology and Centre for Infectious Diseases, Peking University Health Science Centre, Beijing, 100191 China
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Li L, Chen J, Huang Y, Wu C, Ye D, Wu W, Zhou X, Qin P, Jia T, Lin Y, Su Z. Precise localization of microvascular invasion in hepatocellular carcinoma based on three-dimensional histology-MR image fusion: an ex vivo experimental study. Quant Imaging Med Surg 2023; 13:5887-5901. [PMID: 37711836 PMCID: PMC10498258 DOI: 10.21037/qims-23-220] [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: 02/23/2023] [Accepted: 06/19/2023] [Indexed: 09/16/2023]
Abstract
Background Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). However, MVI cannot be detected by conventional imaging. To localize MVI precisely on magnetic resonance (MR) images, we evaluated the feasibility and accuracy of 3-dimensional (3D) histology-MR image fusion of the liver. Methods Animal models of VX2 liver tumors were established in 10 New Zealand white rabbits under ultrasonographic guidance. The whole liver lobe containing the VX2 tumor was extracted and divided into 4 specimens, for a total of 40 specimens. MR images were obtained with a T2-weighted sequence for each specimen, and then histological images were obtained by intermittent, serial pathological sections. 3D histology-MR image fusion was performed via landmark registration in 3D Slicer software. We calculated the success rate and registration errors of image fusion, and then we located the MVI on MR images. Regarding influencing factors, we evaluated the uniformity of tissue thickness after sampling and the uniformity of tissue shrinkage after dehydration. Results The VX2 liver tumor model was successfully established in the 10 rabbits. The incidence of MVI was 80% (8/10). 3D histology-MR image fusion was successfully performed in the 39 specimens, and the success rate was 97.5% (39/40). The average registration error was 0.44±0.15 mm. MVI was detected in 20 of the 39 successfully registered specimens, resulting in a total of 166 MVI lesions. The specific location of all MVI lesions was accurately identified on MR images using 3D histology-MR image fusion. All MVI lesions showed as slightly hyperintense on the high-resolution MR T2-weighted images. The results of the influencing factor assessment showed that the tissue thickness was uniform after sampling (P=0.38), but the rates of the tissue shrinkage was inconsistent after dehydration (P<0.001). Conclusions 3D histology-MR image fusion of the isolated liver tumor model is feasible and accurate and allows for the successful identification of the specific location of MVI on MR images.
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Affiliation(s)
- Liujun Li
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Department of Ultrasound, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Jiaxin Chen
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Yongquan Huang
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Chaoqun Wu
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Dalin Ye
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Wenhao Wu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Xuan Zhou
- Department of Pathology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Peixin Qin
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Taoyu Jia
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Yuhong Lin
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Zhongzhen Su
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
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Mirza-Aghazadeh-Attari M, Madani SP, Shahbazian H, Ansari G, Mohseni A, Borhani A, Afyouni S, Kamel IR. Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis. Abdom Radiol (NY) 2023; 48:2570-2584. [PMID: 37202642 DOI: 10.1007/s00261-023-03940-y] [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: 12/20/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
Lymph node metastases are associated with poor clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). In preoperative imaging, conventional diagnostic modalities do not provide the desired accuracy in diagnosing lymph node metastasis. The current review aims to determine the pooled diagnostic profile of studies examining the role of radiomics features in detecting lymph node metastasis in PDAC. PubMed, Google Scholar, and Embase databases were searched for relevant articles. The quality of the studies was examined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools. Pooled results for sensitivity, specificity, likelihood, and odds ratios with the corresponding 95% confidence intervals (CIs) were calculated using a random-effect model (DerSimonian-Liard method). No significant publication bias was detected among the studies included in this meta-analysis. The pooled sensitivity of the validation datasets included in the study was 77.4% (72.7%, 81.5%) and pooled specificity was 72.4% (63.8, 79.6%). The diagnostic odds ratio of the validation datasets was 9.6 (6.0, 15.2). No statistically significant heterogeneity was detected for sensitivity and odds ratio (P values of 0.3 and 0.08, respectively). However, there was significant heterogeneity concerning specificity (P = 0.003). The pretest probability of having lymph node metastasis in the pooled databases was 52% and a positive post-test probability was 76% after the radiomics features were used, showing a net benefit of 24%. Classifiers trained on radiomics features extracted from preoperative images can improve the sensitivity and specificity of conventional cross-sectional imaging in detecting lymph node metastasis in PDAC.
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Affiliation(s)
- Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Seyedeh Panid Madani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Haneyeh Shahbazian
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Golnoosh Ansari
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Ali Borhani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Shadi Afyouni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 North Wolfe Street, MRI 143, Baltimore, MD, 21287, USA.
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Zhong J, Lu J, Zhang G, Mao S, Chen H, Yin Q, Hu Y, Xing Y, Ding D, Ge X, Zhang H, Yao W. An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation. Insights Imaging 2023; 14:111. [PMID: 37336830 DOI: 10.1186/s13244-023-01437-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. METHODS A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. RESULTS We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. CONCLUSIONS The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. CLINICAL RELEVANCE STATEMENT The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Guangcheng Zhang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shiqi Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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10
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Endo Y, Alaimo L, Lima HA, Moazzam Z, Ratti F, Marques HP, Soubrane O, Lam V, Kitago M, Poultsides GA, Popescu I, Alexandrescu S, Martel G, Workneh A, Guglielmi A, Hugh T, Aldrighetti L, Endo I, Pawlik TM. A Novel Online Calculator to Predict Risk of Microvascular Invasion in the Preoperative Setting for Hepatocellular Carcinoma Patients Undergoing Curative-Intent Surgery. Ann Surg Oncol 2023; 30:725-733. [PMID: 36103014 DOI: 10.1245/s10434-022-12494-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/25/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND The presence of microvascular invasion (MVI) has been highlighted as an important determinant of hepatocellular carcinoma (HCC) prognosis. We sought to build and validate a novel model to predict MVI in the preoperative setting. METHODS Patients who underwent curative-intent surgery for HCC between 2000 and 2020 were identified using a multi-institutional database. Preoperative predictive models for MVI were built, validated, and used to develop a web-based calculator. RESULTS Among 689 patients, MVI was observed in 323 patients (46.9%). On multivariate analysis in the test cohort, preoperative parameters associated with MVI included α-fetoprotein (AFP; odds ratio [OR] 1.50, 95% confidence interval [CI] 1.23-1.83), imaging tumor burden score (TBS; hazard ratio [HR] 1.11, 95% CI 1.04-1.18), and neutrophil-to-lymphocyte ratio (NLR; OR 1.18, 95% CI 1.03-1.35). An online calculator to predict MVI was developed based on the weighted β-coefficients of these three variables ( https://yutaka-endo.shinyapps.io/MVIrisk/ ). The c-index of the test and validation cohorts was 0.71 and 0.72, respectively. Patients with a high risk of MVI had worse disease-free survival (DFS) and overall survival (OS) compared with low-risk MVI patients (3-year DFS: 33.0% vs. 51.9%, p < 0.001; 5-year OS: 44.2% vs. 64.8%, p < 0.001). DFS was worse among patients who underwent an R1 versus R0 resection among those patients at high risk of MVI (R0 vs. R1 resection: 3-year DFS, 36.3% vs. 16.1%, p = 0.002). In contrast, DFS was comparable among patients at low risk of MVI regardless of margin status (R0 vs. R1 resection: 3-year DFS, 52.9% vs. 47.3%, p = 0.16). CONCLUSION Preoperative assessment of MVI using the online tool demonstrated very good accuracy to predict MVI.
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Affiliation(s)
- Yutaka Endo
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Laura Alaimo
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Surgery, University of Verona, Verona, Italy
| | - Henrique A Lima
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Zorays Moazzam
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | | | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | - Olivier Soubrane
- Department of Hepatibiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France
| | - Vincent Lam
- Department of Surgery, Westmead Hospital, Sydney, NSW, Australia
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | | | - Irinel Popescu
- Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania
| | | | | | - Aklile Workneh
- Department of Surgery, University of Ottawa, Ottawa, ON, Canada
| | | | - Tom Hugh
- Department of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia
| | | | - Itaru Endo
- Yokohama City University School of Medicine, Yokohama, Japan
| | - Timothy M Pawlik
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
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11
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Liang G, Yu W, Liu S, Zhang M, Xie M, Liu M, Liu W. The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis. Front Oncol 2023; 12:960944. [PMID: 36798691 PMCID: PMC9928182 DOI: 10.3389/fonc.2022.960944] [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: 06/03/2022] [Accepted: 12/23/2022] [Indexed: 02/01/2023] Open
Abstract
Objective The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Method The databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searched for original studies from their inception to 20 August 2022. The quality of each study included was assessed according to the Quality Assessment of Diagnostic Accuracy Studies 2 and the radiomics quality score. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. Sensitivity analysis and subgroup analysis were performed to explore the source of the heterogeneity. Deeks' test was used to assess publication bias. Results A total of 15 studies involving 981 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.79 (95%CI: 0.72-0.85), 0.81 (95%CI: 0.73-0.87), 4.1 (95%CI:2.9-5.9), 0.26 (95%CI: 0.19-0.35), 16 (95%CI: 9-28), and 0.87 (95%CI: 0.84-0.89), respectively. The results showed great heterogeneity among the included studies. Sensitivity analysis indicated that the results of this study were statistically reliable. The results of subgroup analysis showed that hepatocyte-specific contrast media (HSCM) had equivalent sensitivity and equivalent specificity compared to the other set. The least absolute shrinkage and selection operator method had high sensitivity and specificity than other methods, respectively. The investigated area of the region of interest had high specificity compared to the volume of interest. The imaging-to-surgery interval of 15 days had higher sensitivity and slightly low specificity than the others. Deeks' test indicates that there was no publication bias (P=0.71). Conclusion Radiomics-based MRI has high accuracy in predicting MVI in HCC, and it can be considered as a non-invasive method for assessing MVI in HCC.
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Affiliation(s)
- Gao Liang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Wei Yu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Shuqin Liu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Mingxing Zhang
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Mingguo Xie
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China,*Correspondence: Mingguo Xie,
| | - Min Liu
- Toxicology Department, West China-Frontier PharmaTech Co., Ltd. (WCFP), Chengdu, Sichuan, China
| | - Wenbin Liu
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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12
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Bodard S, Liu Y, Guinebert S, Kherabi Y, Asselah T. Performance of Radiomics in Microvascular Invasion Risk Stratification and Prognostic Assessment in Hepatocellular Carcinoma: A Meta-Analysis. Cancers (Basel) 2023; 15:cancers15030743. [PMID: 36765701 PMCID: PMC9913680 DOI: 10.3390/cancers15030743] [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: 12/13/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC. METHODS We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form. RESULTS Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76-0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72-0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76-0.84). CONCLUSIONS Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.
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Affiliation(s)
- Sylvain Bodard
- Service de Radiologie Adulte, Hôpital Universitaire Necker-Enfants Malades, AP-HP Centre, 75015 Paris, France
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
- CNRS, INSERM, UMR 7371, Laboratoire d’Imagerie Biomédicale, Sorbonne Université, 75006 Paris, France
- Correspondence: ; Tel.: +33-6-18-81-62-10
| | - Yan Liu
- Faculty of Life Science and Medicine, King’s College London, London WC2R 2LS, UK
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France
| | - Sylvain Guinebert
- Service de Radiologie Adulte, Hôpital Universitaire Necker-Enfants Malades, AP-HP Centre, 75015 Paris, France
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
| | - Yousra Kherabi
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
| | - Tarik Asselah
- Faculté de Médecine, Université Paris Cité, 75007 Paris, France
- Service d’Hépatologie, INSERM, UMR1149, Hôpital Beaujon, AP-HP.Nord, 92110 Clichy, France
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13
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Feng LH, Zhu YY, Zhou JM, Wang M, Wang L, Xu WQ, Zhang T, Mao AR, Cong WM, Dong H, Wang L. A Practical Risk Classification of Early Recurrence in Hepatocellular Carcinoma Patients with Microvascular invasion after Hepatectomy: A Decision Tree Analysis. Ann Surg Oncol 2023; 30:363-372. [PMID: 36151430 DOI: 10.1245/s10434-022-12598-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/04/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE This study was designed to establish risk classifications for early recurrence in hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI) after hepatectomy. METHODS The data of 563 HCC patients with MVI after hepatectomy from two hospitals were retrospectively reviewed. Kaplan-Meier curves and Cox proportional hazards regression models were used to analyse early recurrence. The risk classification for early recurrence was established by using classification and regression tree (CART) analysis and validated by using two independent validation cohorts from two hospitals. RESULTS Multivariate analysis revealed that four indices, namely, infection of chronic viral hepatitis, MVI classification, tumour size, and serum alpha-fetoprotein (AFP), were independent prognostic factors for early recurrence in HCC patients with MVI. By CART analysis, MVI classification and serum AFP became the nodes of a decision tree and 3-stratification classifications that satisfactorily determined the risk of early recurrence were established. The area under the time-dependent receiver operating characteristic curve (AUC) values of the classification for early recurrence at 0.5, 1.0, and 2.0 years were 0.75, 0.73, and 0.71, respectively, which were all significantly higher than three common classic HCC stages (BCLC stage, Chinese stage, and TNM stage). The calibration curves showed good agreement between predictions by classification for early recurrence and actual survival outcomes. These prediction results also were confirmed in the independent internal and external validation cohorts. CONCLUSIONS The 3 stratification classifications enabled satisfactory risk evaluation of early recurrence in HCC patients with MVI after hepatectomy.
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Affiliation(s)
- Long-Hai Feng
- Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yao Zhu
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University, Shanghai, China
| | - Jia-Min Zhou
- Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Miao Wang
- Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lei Wang
- Department of Pathology, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wei-Qi Xu
- Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ti Zhang
- Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - An-Rong Mao
- Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Wen-Ming Cong
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University, Shanghai, China.
| | - Hui Dong
- Department of Pathology, Eastern Hepatobiliary Surgery Hospital, The Second Military Medical University, Shanghai, China.
| | - Lu Wang
- Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, People's Republic of China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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14
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Gong X, Guo Y, Zhu T, Peng X, Xing D, Zhang M. Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1046005. [PMID: 36518318 PMCID: PMC9742555 DOI: 10.3389/fonc.2022.1046005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 11/11/2022] [Indexed: 10/03/2023] Open
Abstract
BACKGROUND This study aimed to perform a meta-analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer. MATERIALS AND METHODS Multiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman's correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity. RESULTS A total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis (P < 0.05). CONCLUSION Our results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.
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Affiliation(s)
| | | | | | | | | | - Minguang Zhang
- Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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