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Hao L, Zhang ZN, Han S, Li SS, Lin SX, Miao YD. New frontiers in hepatocellular carcinoma: Precision imaging for microvascular invasion prediction. World J Gastroenterol 2025; 31:102224. [PMID: 40062334 PMCID: PMC11886512 DOI: 10.3748/wjg.v31.i8.102224] [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: 10/12/2024] [Revised: 01/02/2025] [Accepted: 01/10/2025] [Indexed: 01/23/2025] Open
Abstract
This paper highlights the innovative approach and findings of the recently published study by Xu et al, which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative prediction of microvascular invasion in patients with hepatitis B virus-related hepatocellular carcinoma (HBV-HCC). The study's use of contrast-enhanced computed tomography radiomics to construct predictive models offers a significant advancement in the surgical planning and management of HBV-HCC, potentially transforming patient outcomes through more personalized treatment strategies. This editorial commends the study's contribution to precision medicine and discusses its implications for future research and clinical practice.
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Affiliation(s)
- Liang Hao
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Zhao-Nan Zhang
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Shuang Han
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Shan-Shan Li
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Si-Xiang Lin
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Yan-Dong Miao
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
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Gou J, Li J, Li Y, Lu M, Wang C, Zhuo Y, Dong X. The Diagnostic Accuracy Between Radiomics Model and Non-radiomics Model for Preoperative of Microvascular Invasion of Solitary Hepatocellular Carcinoma: A Systematic Review and Meta-analysis. Acad Radiol 2024; 31:4419-4433. [PMID: 38664142 DOI: 10.1016/j.acra.2024.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/01/2024] [Accepted: 04/05/2024] [Indexed: 11/01/2024]
Abstract
RATIONALE AND OBJECTIVES Microvascular invasion (MVI) is a key prognostic factor for hepatocellular carcinoma (HCC). The predictive models for solitary HCC could potentially integrate more comprehensive tumor information. Owing to the diverse findings across studies, we aimed to compare radiomic and non-radiomic methods for preoperative MVI detection in solitary HCC. MATERIALS AND METHODS Articles were reviewed from databases including PubMed, Embase, Web of Science, and the Cochrane Library until April 7, 2023. The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated using a random-effects model within a 95% confidence interval (CI). Diagnostic accuracy was assessed using summary receiver-operating characteristic curves and the area under the curve (AUC). Meta-regression and Z-tests identified heterogeneity and compared the predictive accuracy. Subgroup analyses were performed to compare the AUC of two methods according to study type, study design, tumor size, modeling methods, and imaging modality. RESULTS The analysis incorporated 26 studies involving 3539 patients with solitary HCC. The radiomics models showed a pooled sensitivity and specificity of 0.79 (95%CI: 0.72-0.85) and 0.78 (95%CI: 0.73-0.82), with an AUC at 0.85 (95%CI: 0.82-0.88). Conversely, the non-radiomics models had sensitivity and specificity of 0.74 (95%CI: 0.65-0.81) and 0.88 (95%CI: 0.82-0.92) and an AUC of 0.88 (95%CI: 0.85-0.91). Subgroups with preoperative MRI, larger tumors, and functional imaging had higher accuracy than those using preoperative CT, smaller tumors, and conventional imaging. CONCLUSION Non-radiomic methods outperformed radiomic methods, but high heterogeneity calls across studies for cautious interpretation.
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Affiliation(s)
- Junjiu Gou
- The Clinical Medical College, Guizhou Medical University, Guiyang 550004, Guizhou Province, China
| | - Jingqi Li
- The Clinical Medical College, Guizhou Medical University, Guiyang 550004, Guizhou Province, China
| | - Yingfeng Li
- The Clinical Medical College, Guizhou Medical University, Guiyang 550004, Guizhou Province, China
| | - Mingjie Lu
- The Clinical Medical College, Guizhou Medical University, Guiyang 550004, Guizhou Province, China
| | - Chen Wang
- The Clinical Medical College, Guizhou Medical University, Guiyang 550004, Guizhou Province, China
| | - Yi Zhuo
- The Clinical Medical College, Guizhou Medical University, Guiyang 550004, Guizhou Province, China
| | - Xue Dong
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
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Deng K, Chen T, Leng Z, Yang F, Lu T, Cao J, Pan W, Zheng Y. Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:1099-1117. [PMID: 39060885 PMCID: PMC11322429 DOI: 10.1007/s11547-024-01840-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024]
Abstract
INTRODUCTION Transarterial chemoembolization (TACE) is one of the predominant locoregional therapeutic modalities for addressing hepatocellular carcinoma (HCC). However, achieving precise prognostic predictions and effective patient selection remains a challenging pursuit. The primary objective of this systematic review and meta-analysis is to evaluate the efficacy of radiomics in forecasting the prognosis associated with TACE treatment. METHODS A comprehensive exploration of pertinent original studies was undertaken, encompassing databases of PubMed, Web of Science and Embase. The studies' quality was meticulously evaluated employing the quality assessment of diagnostic accuracy studies 2 (QUADAS-2), the radiomics quality score (RQS) and the METhodological RadiomICs Score (METRICS). Pooled statistics, along with 95% confidence intervals (95% CI), were computed for sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Additionally, a summary receiver operating characteristic curve (sROC) was generated. To discern potential sources of heterogeneity, meta-regression and subgroup analyses were performed. RESULTS The systematic review incorporated 29 studies, comprising a total of 5483 patients, with 14 studies involving 2691 patients qualifying for inclusion in the meta-analysis. The assessed studies exhibited commendable quality with regard to bias risk, with mean RQS of 12.90 ± 5.13 (35.82% ± 14.25%) and mean METRICS of 62.98% ± 14.58%. The pooled sensitivity was 0.83 (95% CI: 0.78-0.87), specificity was 0.86 (95% CI: 0.79-0.92), PLR was 6.13 (95% CI: 3.79-9.90), and NLR was 0.20 (95% CI: 0.15-0.27). The area under the sROC was 0.90 (95% CI: 0.87-0.93). Significant heterogeneity within all the included studies was observed, while meta-regression and subgroup analyses revealed homogeneous and promising findings in subgroups where principal methodological variables such as modeling algorithms, imaging modalities, and imaging phases were specified. CONCLUSION Radiomics models have exhibited robust predictive capabilities concerning prognosis subsequent to TACE, thereby presenting promising prospects for clinical translation.
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Affiliation(s)
- Kaige Deng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Tong Chen
- Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021, China
| | - Zijian Leng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Fan Yang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Tao Lu
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jingying Cao
- Zunyi Medical University, Zunyi, Guizhou, 563000, China
| | - Weixuan Pan
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, 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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Jiang S, Gao X, Tian Y, Chen J, Wang Y, Jiang Y, He Y. The potential of 18F-FDG PET/CT metabolic parameter-based nomogram in predicting the microvascular invasion of hepatocellular carcinoma before liver transplantation. Abdom Radiol (NY) 2024; 49:1444-1455. [PMID: 38265452 DOI: 10.1007/s00261-023-04166-8] [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: 06/17/2023] [Revised: 06/17/2023] [Accepted: 12/16/2023] [Indexed: 01/25/2024]
Abstract
PURPOSE Microvascular invasion (MVI) is a critical factor in predicting the recurrence and prognosis of hepatocellular carcinoma (HCC) after liver transplantation (LT). However, there is a lack of reliable preoperative predictors for MVI. The purpose of this study is to evaluate the potential of an 18F-FDG PET/CT-based nomogram in predicting MVI before LT for HCC. METHODS 83 HCC patients who obtained 18F-FDG PET/CT before LT were included in this retrospective research. To determine the parameters connected to MVI and to create a nomogram for MVI prediction, respectively, Logistic and Cox regression models were applied. Analyses of the calibration curve and receiver operating characteristic (ROC) curves were used to assess the model's capability to differentiate between clinical factors and metabolic data from PET/CT images. RESULTS Among the 83 patients analyzed, 41% were diagnosed with histologic MVI. Multivariate logistic regression analysis revealed that Child-Pugh stage, alpha-fetoprotein, number of tumors, CT Dmax, and Tumor-to-normal liver uptake ratio (TLR) were significant predictors of MVI. A nomogram was constructed using these predictors, which demonstrated strong calibration with a close agreement between predicted and actual MVI probabilities. The nomogram also showed excellent differentiation with an AUC of 0.965 (95% CI 0.925-1.000). CONCLUSION The nomogram based on 18F-FDG PET/CT metabolic characteristics is a reliable preoperative imaging biomarker for predicting MVI in HCC patients before undergoing LT. It has demonstrated excellent efficacy and high clinical applicability.
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Affiliation(s)
- Shengpan Jiang
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), 216 Guanshan Avenue, Wuhan, 430074, China
| | - Xiaoqing Gao
- Clinical Laboratory Department, Wuhan Third Hospital (Tongren Hospital of Wuhan University), 216 Guanshan Avenue, Wuhan, 430074, China
| | - Yueli Tian
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Jie Chen
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Yichun Wang
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Yaqun Jiang
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China
| | - Yong He
- Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China.
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Ablefoni M, Richter T, Leonhardi J, Ehrengut C, Prasse G, Mehdorn M, Seehofer D, Höhn AK, Denecke T, Meyer HJ. Potential diagnostic value of high b-value computed diffusion-weighted imaging in hepatocellular carcinoma. Clin Exp Hepatol 2024; 10:129-136. [PMID: 39845353 PMCID: PMC11748228 DOI: 10.5114/ceh.2024.139651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 02/02/2024] [Indexed: 01/24/2025] Open
Abstract
Aim of the study Over the past few years, diffusion-weighted imaging (DWI) has become an increasingly important diagnostic tool in the diagnosis of liver lesions. The objective of the present study was to evaluate the diagnostic benefit of high b-value computed diffusion-weighted imaging (c-DWI) compared with standard DWI in patients with hepatocellular carcinoma (HCC) and whether there is an association with microvascular invasion (MVI). Material and methods In total, 37 patients with histopathologically confirmed HCC were retrospectively ana-lyzed. DWI was acquired with b-values of 50, 400, and 800 or 1000 s/mm² on a 1.5 T magnetic resonance imaging (MRI) scanner. The c-DWI was calculated using a monoexponential model with high b-values of 1000, 2000, 3000, 4000, and 5000 s/mm². All high b-value c-DWI images were compared to the standard DWI in terms of volume, detectability of hepatic lesions, and image quality. Results Regarding lesion volume and image quality there were no statistically significant differences between standard and c-DWI. HCC lesions measured on DWI images were statistically significantly larger compared to c-DWI images starting from a b value of 2000 s/mm2 (DWI vs. c-DWI b 2000 s/mm2: 2 cm3 [1-12] cm3 vs. 1 cm3 [0-17] cm3, p < 0.05). Moreover, there was deterioration of image quality starting at b = 2000 s/mm2. There were no significant differences in terms of lesion signal intensity in DWI and c-DWI images. There were no differences for the DWI parameters according to MVI status. Conclusions C-DWI images with high b-values up to b = 1000 s/mm2 demonstrate comparable detectability of HCC compared to standard DWI. The investigated DWI parameters were not associated with MVI status. Further research is needed to evaluate the potential benefit of high b-value c-DWI.
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Affiliation(s)
- Maxime Ablefoni
- Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Theresa Richter
- Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Jakob Leonhardi
- Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Constantin Ehrengut
- Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Gordian Prasse
- Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Matthias Mehdorn
- Department of Visceral and Transplantation Surgery, University of Leipzig, Leipzig, Germany
| | - Daniel Seehofer
- Department of Visceral and Transplantation Surgery, University of Leipzig, Leipzig, Germany
| | | | - Timm Denecke
- Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Hans-Jonas Meyer
- Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
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Zhang K, Zhang L, Li WC, Xie SS, Cui YZ, Lin LY, Shen ZW, Zhang HM, Xia S, Ye ZX, He K, Shen W. Radiomics nomogram for the prediction of microvascular invasion of HCC and patients' benefit from postoperative adjuvant TACE: a multi-center study. Eur Radiol 2023; 33:8936-8947. [PMID: 37368104 DOI: 10.1007/s00330-023-09824-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/15/2023] [Accepted: 03/26/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES To evaluate the performance of a radiomics nomogram developed based on gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) MRI for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization (PA-TACE). METHODS A total of 260 eligible patients were retrospectively enrolled from three hospitals (140, 65, and 55 in training, standardized external, and non-standardized external validation cohort). Radiomics features and image characteristics were extracted from Gd-EOB-DTPA MRI image before hepatectomy for each lesion. In the training cohort, a radiomics nomogram which incorporated the radiomics signature and radiological predictors was developed. The performance of the radiomics nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. A score (m-score) was constructed to stratify the patients and explored whether it could accurately predict patient who benefit from PA-TACE. RESULTS A radiomics nomogram integrated with the radiomics signature, max-D(iameter) > 5.1 cm, peritumoral low intensity (PTLI), incomplete capsule, and irregular morphology had favorable discrimination in the training cohort (AUC = 0.982), the standardized external validation cohort (AUC = 0.969), and the non-standardized external validation cohort (AUC = 0.981). Decision curve analysis confirmed the clinical usefulness of the novel radiomics nomogram. The log-rank test revealed that PA-TACE significantly decreased the early recurrence in the high-risk group (p = 0.006) with no significant effect in the low-risk group (p = 0.270). CONCLUSIONS The novel radiomics nomogram combining the radiomics signature and clinical radiological features achieved preoperative non-invasive MVI risk prediction and patient benefit assessment after PA-TACE, which may help clinicians implement more appropriate interventions. CLINICAL RELEVANCE STATEMENT Our radiomics nomogram could represent a novel biomarker to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization, which may help clinicians to implement more appropriate interventions and perform individualized precision therapies. KEY POINTS • The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction. • An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE. • The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.
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Affiliation(s)
- Kun Zhang
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, 300192, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Wen-Cui Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Shuang-Shuang Xie
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Ying-Zhu Cui
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Li-Ying Lin
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Zhi-Wei Shen
- Philips Healthcare, Beijing, The World Profit Centre, No. 16 Tianze Road, Chaoyang District, Beijing, 100125, China
| | - Hui-Mao Zhang
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Shuang Xia
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, 300192, China
| | - Zhao-Xiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
| | - Kan He
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China.
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China.
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Zheng R, Zhang X, Liu B, Zhang Y, Shen H, Xie X, Li S, Huang G. Comparison of non-radiomics imaging features and radiomics models based on contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma within 5 cm. Eur Radiol 2023; 33:6462-6472. [PMID: 37338553 DOI: 10.1007/s00330-023-09789-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/15/2023] [Accepted: 03/30/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES The purpose of this study is to establish microvascular invasion (MVI) prediction models based on preoperative contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm. METHODS Patients with a single HCC ≤ 5 cm and accepting CEUS and EOB-MRI before surgery were enrolled in this study. Totally, 85 patients were randomly divided into the training and validation cohorts in a ratio of 7:3. Non-radiomics imaging features, the CEUS and EOB-MRI radiomics scores were extracted from the arterial phase, portal phase and delayed phase images of CEUS and the hepatobiliary phase images of EOB-MRI. Different MVI predicting models based on CEUS and EOB-MRI were constructed and their predictive values were evaluated. RESULTS Since univariate analysis revealed that arterial peritumoral enhancement on the CEUS image, CEUS radiomics score, and EOB-MRI radiomics score were significantly associated with MVI, three prediction models, namely the CEUS model, the EOB-MRI model, and the CEUS-EOB model, were developed. In the validation cohort, the areas under the receiver operating characteristic curve of the CEUS model, the EOB-MRI model, and the CEUS-EOB model were 0.73, 0.79, and 0.86, respectively. CONCLUSIONS Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm. CLINICAL RELEVANCE STATEMENT Radiomics models based on CEUS and EOB-MRI are effective for MVI predicting and conducive to pretreatment decision-making in patients with a single HCC within 5 cm. KEY POINTS • Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. • There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.
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Affiliation(s)
- Ruiying Zheng
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoer Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Baoxian Liu
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yi Zhang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hui Shen
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaoyan Xie
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Shurong Li
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
| | - Guangliang Huang
- Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
- Department of Medical Ultrasonics, Guangxi Hospital Division of the First Affiliated Hospital, Sun Yat-Sen University, Guangxi, China.
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Jiang C, Cai YQ, Yang JJ, Ma CY, Chen JX, Huang L, Xiang Z, Wu J. Radiomics in the diagnosis and treatment of hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int 2023; 22:346-351. [PMID: 37019775 DOI: 10.1016/j.hbpd.2023.03.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023]
Abstract
Hepatocellular carcinoma (HCC) is a common malignant tumor. At present, early diagnosis of HCC is difficult and therapeutic methods are limited. Radiomics can achieve accurate quantitative evaluation of the lesions without invasion, and has important value in the diagnosis and treatment of HCC. Radiomics features can predict the development of cancer in patients, serve as the basis for risk stratification of HCC patients, and help clinicians distinguish similar diseases, thus improving the diagnostic accuracy. Furthermore, the prediction of the treatment outcomes helps determine the treatment plan. Radiomics is also helpful in predicting the HCC recurrence, disease-free survival and overall survival. This review summarized the role of radiomics in the diagnosis, treatment and prognosis of HCC.
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Affiliation(s)
- Chun Jiang
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Yi-Qi Cai
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jia-Jia Yang
- Department of Infection Management, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Can-Yu Ma
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jia-Xi Chen
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Lan Huang
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China
| | - Ze Xiang
- Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Jian Wu
- Department of Clinical Laboratory, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou 215008, China.
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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: 8] [Impact Index Per Article: 4.0] [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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11
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Zhang HD, Li XM, Zhang YH, Hu F, Tan L, Wang F, Jing Y, Guo DJ, Xu Y, Hu XL, Liu C, Wang J. Evaluation of Preoperative Microvascular Invasion in Hepatocellular Carcinoma Through Multidimensional Parameter Combination Modeling Based on Gd-EOB-DTPA MRI. J Clin Transl Hepatol 2023; 11:350-359. [PMID: 36643030 PMCID: PMC9817048 DOI: 10.14218/jcth.2021.00546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/30/2022] [Accepted: 04/18/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND AND AIMS The study established and compared the efficacy of the clinicoradiological model, radiomics model and clinicoradiological-radiomics hybrid model in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using gadolinium ethoxybenzyl diethylene triaminepentaacetic acid (Gd-EOB-DTPA) enhanced MRI. METHODS This was a study that enrolled 602 HCC patients from two institutions. Least absolute shrinkage and selection operator (Lasso) method was used to screen for the most important clinicoradiological and radiomics features that predict MVI pre-operatively. Three machine learning algorithms were used to establish the clinicoradiological, radiomics, and clinicoradiological-radiomics hybrid models. Area under the curve (AUC) of receiver operating characteristic (ROC) curves and Delong's test were used to compare and quantify the predictive performance of the models. RESULTS The AUCs of the clinicoradiological model in training and validation cohorts were 0.793 and 0.701, respectively. The radiomics signature of arterial phase (AP) images alone achieved satisfying predictive efficacy for MVI, with AUCs of 0.671 and 0.643 in training and validation cohort, respectively. The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images achieved AUCs of 0.824 and 0.801 in training and validation cohorts, 0.812 and 0.805 in prospective validation and external validation cohorts, respectively. The hybrid model provided the best prediction results. The results of the Delong test revealed that there were statistically significant differences among the clinicoradiological-radiomics hybrid model, clinicoradiological model, and radiomics model (p<0.05). CONCLUSIONS The combination of clinicoradiological factors and fusion radiomics signature of AP and VP images based on Gd-EOB-DTPA-enhanced MRI can effectively predict MVI.
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Affiliation(s)
- Han-Dan Zhang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Xiao-Ming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Yu-Han Zhang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Fang Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
| | - Liang Tan
- Department of Neurosurgery, Third Military Medical University (Army Military Medical University), Chongqing, China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Fang Wang
- Department of Market, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Yang Jing
- Department of Market, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Da-Jing Guo
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Xu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xian-Ling Hu
- Communication Sergeant School, Army Engineering University of PLA, Chongqing, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
- Correspondence to: Chen Liu and Jian Wang, Department of Radiology, Southwest Hospital, Third Military Medical University, Shazheng Street, Shapingba District, Chongqing 400038, China. ORCID: https://orcid.org/0000-0001-5149-2496 (CL) and https://orcid.org/0000-0003-1210-0837 (JW). Tel: +86-131-0896-8808 (CL) and +86-138-8378-5811 (JW), Fax: +86-23-6546-3026, E-mail: (CL) and (JW)
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China
- Correspondence to: Chen Liu and Jian Wang, Department of Radiology, Southwest Hospital, Third Military Medical University, Shazheng Street, Shapingba District, Chongqing 400038, China. ORCID: https://orcid.org/0000-0001-5149-2496 (CL) and https://orcid.org/0000-0003-1210-0837 (JW). Tel: +86-131-0896-8808 (CL) and +86-138-8378-5811 (JW), Fax: +86-23-6546-3026, E-mail: (CL) and (JW)
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12
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Quality of radiomics for predicting microvascular invasion in hepatocellular carcinoma: a systematic review. Eur Radiol 2023; 33:3467-3477. [PMID: 36749371 DOI: 10.1007/s00330-023-09414-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 11/06/2022] [Accepted: 01/01/2023] [Indexed: 02/08/2023]
Abstract
OBJECTIVES To comprehensively evaluate the reporting quality, risk of bias, and radiomics methodology quality of radiomics models for predicting microvascular invasion in hepatocellular carcinoma. METHODS A systematic search of available literature was performed in PubMed, Embase, Web of Science, Scopus, and the Cochrane Library up to January 21, 2022. Studies that developed and/or validated machine learning models based on radiomics data to predict microvascular invasion in hepatocellular carcinoma were included. These studies were reviewed by two investigators and the consensus data were used for analyzing. The reporting quality, risk of bias, and radiomics methodological quality were evaluated by Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD), Prediction model Risk of Bias Assessment Tool, and Radiomics Quality Score (RQS), respectively. RESULTS A total of 30 studies met eligibility criteria with 24 model developing studies and 6 model developing and external validation studies. The median overall TRIPOD adherence was 75.4% (range 56.7-94.3%). All studies were at high risk of bias with at least 2 of 20 sources of bias. Furthermore, 28 studies showed unclear risks of bias in up to 5 signaling questions because of the lack of specified reports. The median RQS score was 37.5% (range 25-61.1%). CONCLUSION Current radiomic models for MVI-status prediction have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. KEY POINTS • Current microvascular invasion prediction radiomics studies have moderate to good reporting quality, moderate radiomics methodology quality, and high risk of bias in model development and validation. • Data representativeness, feature robustness, events-per-variable ratio, evaluation metrics, and appropriate validation are five main aspects futures studies should focus more on to improve the quality of radiomics. • Both Radiomics Quality Score and Prediction model Risk of Bias Assessment Tool are needed to comprehensively evaluate a radiomics study.
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Ma Y, Lin Y, Lu J, He Y, Shi Q, Liu H, Li J, Zhang B, Zhang J, Zhang Y, Yue P, Meng W, Li X. A meta-analysis of based radiomics for predicting lymph node metastasis in patients with biliary tract cancers. Front Surg 2023; 9:1045295. [PMID: 36684162 PMCID: PMC9852536 DOI: 10.3389/fsurg.2022.1045295] [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: 09/15/2022] [Accepted: 10/31/2022] [Indexed: 01/09/2023] Open
Abstract
Background To assess the predictive value of radiomics for preoperative lymph node metastasis (LMN) in patients with biliary tract cancers (BTCs). Methods PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases [VIP, CNKI, Wanfang, and China Biomedical Literature Database (CBM)] were searched to identify relevant studies published up to February 10, 2022. Two authors independently screened all publications for eligibility. We included studies that used histopathology as a gold standard and radiomics to evaluate the diagnostic efficacy of LNM in BTCs patients. The quality of the literature was evaluated using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The diagnostic odds ratio (DOR), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the receiver operating characteristic curve (AUC) were calculated to assess the predictive validity of radiomics for lymph node status in patients with BTCs. Spearman correlation coefficients were calculated, and Meta-regression and subgroup analyses were performed to assess the causes of heterogeneity. Results Seven studies were included, with 977 patients. The pooled sensitivity, specificity and AUC were 83% [95% confidence interval (CI): 77%, 88%], 78% (95% CI: 71, 84) and 0.88 (95% CI: 0.85, 0.90), respectively. The substantive heterogeneity was observed among the included studies (I 2 = 80%, 95%CI: 58,100). There was no threshold effect seen. Meta-regression showed that tumor site contributed to the heterogeneity of specificity analysis (P < 0.05). Imaging methods, number of patients, combined clinical factors, tumor site, model, population, and published year all played a role in the heterogeneity of the sensitivity analysis (P < 0.05). Subgroup analysis revealed that magnetic resonance imaging (MRI) based radiomics had a higher pooled sensitivity than contrast-computed tomography (CT), whereas the result for pooled specificity was the opposite. Conclusion Our meta-analysis showed that radiomics provided a high level of prognostic value for preoperative LMN in BTCs patients.
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Affiliation(s)
- Yuhu Ma
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Yanyan Lin
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
| | - Jiyuan Lu
- School of Stomatology, Lanzhou University, Lanzhou, China
| | - Yulong He
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Qianling Shi
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Haoran Liu
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Jianlong Li
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Baoping Zhang
- The First School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Jinduo Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
| | - Yong Zhang
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
| | - Ping Yue
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China,Correspondence: Wenbo Meng Ping Yue dryueping@sina. Com
| | - Wenbo Meng
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China,Correspondence: Wenbo Meng Ping Yue dryueping@sina. Com
| | - Xun Li
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, China
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14
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Lu XY, Zhang JY, Zhang T, Zhang XQ, Lu J, Miao XF, Chen WB, Jiang JF, Ding D, Du S. Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI. BMC Med Imaging 2022; 22:157. [PMID: 36057576 PMCID: PMC9440540 DOI: 10.1186/s12880-022-00855-w] [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: 02/23/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences. Methods We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model. Results The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration. Conclusions The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00855-w.
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Affiliation(s)
- Xin-Yu Lu
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.,The First People's Hospital of Taicang, Taicang, Suzhou, Jiangsu, China
| | - Ji-Yun Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.
| | - Xue-Qin Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.
| | - Jian Lu
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Xiao-Fen Miao
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | | | - Ji-Feng Jiang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Ding Ding
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Sheng Du
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
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Zhang Y, Wei Q, Huang Y, Yao Z, Yan C, Zou X, Han J, Li Q, Mao R, Liao Y, Cao L, Lin M, Zhou X, Tang X, Hu Y, Li L, Wang Y, Yu J, Zhou J. Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma. Front Oncol 2022; 12:878061. [PMID: 35875110 PMCID: PMC9300962 DOI: 10.3389/fonc.2022.878061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/14/2022] [Indexed: 12/24/2022] Open
Abstract
Background and Aims Microvascular invasion (MVI) is a well-known risk factor for poor prognosis in hepatocellular carcinoma (HCC). This study aimed to develop a deep convolutional neural network (DCNN) model based on contrast-enhanced ultrasound (CEUS) to predict MVI, and thus to predict prognosis in patients with HCC. Methods A total of 436 patients with surgically resected HCC who underwent preoperative CEUS were retrospectively enrolled. Patients were divided into training (n = 301), validation (n = 102), and test (n = 33) sets. A clinical model (Clinical model), a CEUS video-based DCNN model (CEUS-DCNN model), and a fusion model based on CEUS video and clinical variables (CECL-DCNN model) were built to predict MVI. Survival analysis was used to evaluate the clinical performance of the predicted MVI. Results Compared with the Clinical model, the CEUS-DCNN model exhibited similar sensitivity, but higher specificity (71.4% vs. 38.1%, p = 0.03) in the test group. The CECL-DCNN model showed significantly higher specificity (81.0% vs. 38.1%, p = 0.005) and accuracy (78.8% vs. 51.5%, p = 0.009) than the Clinical model, with an AUC of 0.865. The Clinical predicted MVI could not significantly distinguish OS or RFS (both p > 0.05), while the CEUS-DCNN predicted MVI could only predict the earlier recurrence (hazard ratio [HR] with 95% confidence interval [CI 2.92 [1.1–7.75], p = 0.024). However, the CECL-DCNN predicted MVI was a significant prognostic factor for both OS (HR with 95% CI: 6.03 [1.7–21.39], p = 0.009) and RFS (HR with 95% CI: 3.3 [1.23–8.91], p = 0.011) in the test group. Conclusions The proposed CECL-DCNN model based on preoperative CEUS video can serve as a noninvasive tool to predict MVI status in HCC, thereby predicting poor prognosis.
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Affiliation(s)
- Yafang Zhang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Qingyue Wei
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yini Huang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhao Yao
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Cuiju Yan
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xuebin Zou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jing Han
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Qing Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Rushuang Mao
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ying Liao
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lan Cao
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Min Lin
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiaoshuang Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xiaofeng Tang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yixin Hu
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lingling Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yuanyuan Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai, China
- *Correspondence: Jinhua Yu, ; Jianhua Zhou,
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- *Correspondence: Jinhua Yu, ; Jianhua Zhou,
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Li L, Wu C, Huang Y, Chen J, Ye D, Su Z. Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis. Front Oncol 2022; 12:831996. [PMID: 35463303 PMCID: PMC9021380 DOI: 10.3389/fonc.2022.831996] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/11/2022] [Indexed: 12/12/2022] Open
Abstract
Background Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). To perform a meta-analysis to investigate the diagnostic performance of radiomics for the preoperative evaluation of MVI in HCC and the effect of potential factors. Materials and Methods A systematic literature search was performed in PubMed, Embase, and the Cochrane Library for studies focusing on the preoperative evaluation of MVI in HCC with radiomics methods. Data extraction and quality assessment of the retrieved studies were performed. Statistical analysis included data pooling, heterogeneity testing and forest plot construction. Meta-regression and subgroup analyses were performed to reveal the effect of potential explanatory factors [design, combination of clinical factors, imaging modality, number of participants, and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) applicability risk] on the diagnostic performance. Results Twenty-two studies with 4,129 patients focusing on radiomics for the preoperative prediction of MVI in HCC were included. The pooled sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were 84% (95% CI: 81, 87), 83% (95% CI: 78, 87) and 0.90 (95% CI: 0.87, 0.92). Substantial heterogeneity was observed among the studies (I²=94%, 95% CI: 88, 99). Meta-regression showed that all investigative covariates contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Combined clinical factors, MRI, CT and number of participants contributed to the heterogeneity in the specificity analysis (P < 0.05). Subgroup analysis showed that the pooled sensitivity, specificity and AUC estimates were similar among studies with CT or MRI. Conclusion Radiomics is a promising noninvasive method that has high preoperative diagnostic performance for MVI status. Radiomics based on CT and MRI had a comparable predictive performance for MVI in HCC. Prospective, large-scale and multicenter studies with radiomics methods will improve the diagnostic power for MVI in the future. Systematic Review Registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259363, identifier CRD42021259363.
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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
| | - 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
| | - 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
| | - Jiaxin Chen
- Department of Ultrasound, 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
| | - 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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Xu W, Wang Y, Yang Z, Li J, Li R, Liu F. New Insights Into a Classification-Based Microvascular Invasion Prediction Model in Hepatocellular Carcinoma: A Multicenter Study. Front Oncol 2022; 12:796311. [PMID: 35433417 PMCID: PMC9008838 DOI: 10.3389/fonc.2022.796311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/07/2022] [Indexed: 11/28/2022] Open
Abstract
Background and Aims Most microvascular invasion (MVI)-predicting models have not considered MVI classification, and thus do not reflect true MVI effects on prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel MVI-predicting model focused on MVI classification, hoping to provide useful information for clinical treatment strategy decision-making. Methods A retrospective study was conducted with data from two Chinese medical centers for 800 consecutive patients with HCC (derivation cohort) and 250 matched patients (external validation cohort). MVI-associated variables were identified by ordinal logistic regression. Predictive models were constructed based on multivariate analysis results and validated internally and externally. The models' discriminative ability and calibration ability were examined. Results Four factors associated independently with MVI: tumor diameter, tumor number, serum lactate dehydrogenase (LDH) ≥ 176.58 U/L, and γ-glutamyl transpeptidase (γ-GGT). Area under the curve (AUC)s for our M2, M1, and M0 nomograms were 0.864, 0.648, and 0.782. Internal validation of all three models was confirmed with AUC analyses in D-sets (development datasets) and V-sets (validation datasets) and C-indices for each cohort. GiViTI calibration belt plots and Hosmer-Lemeshow (HL) chi-squared calibration values demonstrated good consistency between observed frequencies and predicted probabilities for the M2 and M0 nomograms. Although the M1 nomogram was well calibrated, its discrimination was poor. Conclusion We developed and validated MVI prediction models in patients with HCC that differentiate MVI classification and may provide useful guidance for treatment planning.
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Affiliation(s)
- Wei Xu
- Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, The First Hospital Affiliated with Hunan Normal University, Changsha, China
| | - Yonggang Wang
- Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, The First Hospital Affiliated with Hunan Normal University, Changsha, China
| | - Zhanwei Yang
- Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, The First Hospital Affiliated with Hunan Normal University, Changsha, China
| | - Jingdong Li
- Department of Hepatobiliary Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ruineng Li
- Department of Hepatobiliary Surgery, Xiangtan Central Hospital, Xiangtan, China
| | - Fei Liu
- Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, The First Hospital Affiliated with Hunan Normal University, Changsha, China
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18
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Zhang J, Huang S, Xu Y, Wu J. Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:763842. [PMID: 35280776 PMCID: PMC8907853 DOI: 10.3389/fonc.2022.763842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Background The presence of microvascular invasion (MVI) is considered an independent prognostic factor associated with early recurrence and poor survival in hepatocellular carcinoma (HCC) patients after resection. Artificial intelligence (AI), mainly consisting of non-deep learning algorithms (NDLAs) and deep learning algorithms (DLAs), has been widely used for MVI prediction in medical imaging. Aim To assess the diagnostic accuracy of AI algorithms for non-invasive, preoperative prediction of MVI based on imaging data. Methods Original studies reporting AI algorithms for non-invasive, preoperative prediction of MVI based on quantitative imaging data were identified in the databases PubMed, Embase, and Web of Science. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) scale. The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated using a random-effects model with 95% CIs. A summary receiver operating characteristic curve and the area under the curve (AUC) were generated to assess the diagnostic accuracy of the deep learning and non-deep learning models. In the non-deep learning group, we further performed meta-regression and subgroup analyses to identify the source of heterogeneity. Results Data from 16 included studies with 4,759 cases were available for meta-analysis. Four studies on deep learning models, 12 studies on non-deep learning models, and two studies compared the efficiency of the two types. For predictive performance of deep learning models, the pooled sensitivity, specificity, PLR, NLR, and AUC values were 0.84 [0.75–0.90], 0.84 [0.77–0.89], 5.14 [3.53–7.48], 0.2 [0.12–0.31], and 0.90 [0.87–0.93]; and for non-deep learning models, they were 0.77 [0.71–0.82], 0.77 [0.73–0.80], 3.30 [2.83–3.84], 0.30 [0.24–0.38], and 0.82 [0.79–0.85], respectively. Subgroup analyses showed a significant difference between the single tumor subgroup and the multiple tumor subgroup in the pooled sensitivity, NLR, and AUC. Conclusion This meta-analysis demonstrates the high diagnostic accuracy of non-deep learning and deep learning methods for MVI status prediction and their promising potential for clinical decision-making. Deep learning models perform better than non-deep learning models in terms of the accuracy of MVI prediction, methodology, and cost-effectiveness. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php? RecordID=260891, ID:CRD42021260891.
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Affiliation(s)
- Jian Zhang
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
| | - Shenglan Huang
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
| | - Yongkang Xu
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
| | - Jianbing Wu
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Digestive Oncology, Jiangxi Key Laboratory of Clinical and Translational Cancer Research, Nanchang, China
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Prediction of microvascular invasion in HCC by a scoring model combining Gd-EOB-DTPA MRI and biochemical indicators. Eur Radiol 2022; 32:4186-4197. [DOI: 10.1007/s00330-021-08502-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 11/15/2021] [Accepted: 12/01/2021] [Indexed: 12/17/2022]
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20
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Wang H, Lu Y, Liu R, Wang L, Liu Q, Han S. A Non-Invasive Nomogram for Preoperative Prediction of Microvascular Invasion Risk in Hepatocellular Carcinoma. Front Oncol 2022; 11:745085. [PMID: 35004273 PMCID: PMC8739965 DOI: 10.3389/fonc.2021.745085] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/01/2021] [Indexed: 12/24/2022] Open
Abstract
Background Microvascular invasion (MVI) is a significant predictive factor for early recurrence, metastasis, and poor prognosis of hepatocellular carcinoma. The aim of the present study is to identify preoperative factors for predicting MVI, in addition to develop and validate non-invasive nomogram for predicting MVI. Methods A total of 381 patients with resected HCC were enrolled and divided into a training cohort (n = 267) and a validation cohort (n = 114). Serum VEGF-A level was examined by enzyme-linked immunosorbent assay (ELISA). Risk factors for MVI were assessed based on univariate and multivariate analyses in the training cohort. A nomogram incorporating independent risk predictors was established and validated. Result The serum VEGF-A levels in the MVI positive group (n = 198) and MVI negative group (n = 183) were 215.25 ± 105.68 pg/ml and 86.52 ± 62.45 pg/ml, respectively (P <0.05). Serum VEGF-A concentration ≥138.30 pg/ml was an independent risk factor of MVI (OR: 33.088; 95%CI: 12.871–85.057; P <0.001). Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery were identified as significant predictors for MVI. The nomogram indicated excellent predictive performance with an AUROC of 0.948 (95% CI: 0.923–0.973) and 0.881 (95% CI: 0.820–0.942) in the training and validation cohorts, respectively. The nomogram showed a good model fit and calibration. Conclusions Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery are promising markers for MVI prediction in HCC. A reliable non-invasive nomogram which incorporated blood biomarkers and imaging risk factors was established and validated. The nomogram achieved desirable effectiveness in preoperatively predicting MVI in HCC patients.
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Affiliation(s)
- Huanhuan Wang
- Department of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ye Lu
- Department of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Runkun Liu
- Department of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Liang Wang
- Department of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qingguang Liu
- Department of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shaoshan Han
- Department of Hepatobiliary Surgery, The first Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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21
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Huang H, Wang FF, Luo S, Chen G, Tang G. Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis. DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY (ANKARA, TURKEY) 2021; 27:716-724. [PMID: 34792025 DOI: 10.5152/dir.2021.21153] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE We aimed to assess the diagnostic performance of radiomics using machine learning algorithms to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioma patients. METHODS A comprehensive literature search of PubMed, EMBASE, and Web of Science until 27 July 2021 was performed to identify eligible studies. Stata SE 15.0 and Meta-Disc 1.4 were used for data analysis. RESULTS A total of fifteen studies with 1663 patients were included: five studies with training and validation cohorts and ten with only training cohorts. The pooled sensitivity and specificity of machine learning for predicting MGMT promoter methylation in gliomas were 85% (95% CI 79%-90%) and 84% (95% CI 78%-88%) in the training cohort (n=15) and 84% (95% CI 70%-92%) and 78% (95% CI 63%-88%) in the validation cohort (n=5). The AUC was 0.91 (95% CI 0.88-0.93) in the training cohort and 0.88 (95% CI 0.85-0.91) in the validation cohort. The meta-regression demonstrated that magnetic resonance imaging sequences were related to heterogeneity. The sensitivity analysis showed that heterogeneity was reduced by excluding one study with the lowest diagnostic performance. CONCLUSION This meta-analysis demonstrated that machine learning is a promising, reliable and repeatable candidate method for predicting MGMT promoter methylation status in glioma and showed a higher performance than non-machine learning methods.
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Affiliation(s)
- Huan Huang
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Fei-Fei Wang
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Shigang Luo
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Guangxiang Chen
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
| | - Guangcai Tang
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Sichuan, China
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22
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Zheng X, Wang P, Li L, Yu J, Yu C, Xu L, Li L, Dai F, Feng L, Zou H, Chen X, Zhang M, Xu M. Cancer-Associated Fibroblasts Promote Vascular Invasion of Hepatocellular Carcinoma via Downregulating Decorin-integrin β1 Signaling. Front Cell Dev Biol 2021; 9:678670. [PMID: 34504839 PMCID: PMC8421641 DOI: 10.3389/fcell.2021.678670] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 05/10/2021] [Indexed: 02/05/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common malignancy worldwide, and the high ratio of recurrence and metastasis remains the main cause of its poor prognosis. Vascular invasion of HCC includes microvascular invasion (MVI) and portal vein tumor thrombosis (PVTT) and is regarded as a common roadmap of intrahepatic metastasis in HCC. However, the molecular mechanism underlying vascular invasion of HCC is largely unknown. Here, we analyzed the transcriptomes of primary tumors, PVTT tissues, and tumor tissues with or without MVI. We found that extracellular matrix-related pathways were involved in vascular invasion of HCC and that decorin secreted by cancer-associated fibroblasts was gradually downregulated from normal to tumor tissues and more so in PVTT tissues. We also established that low-level decorin expression is an independent risk factor for MVI and it is associated with a poor prognosis. Decorin downregulated integrin β1 and consequently inhibited HCC cell invasion and migration in vitro. Co-staining DCN and integrin β1 revealed that DCN dynamically regulated integrin β1 protein expression. Integrin β1 knockdown significantly inhibited HCC invasion and migration, and decorin combined with such knockdown synergistically augmented the anti-metastatic effects. Co-IP assay confirmed the direct interaction of decorin with integrin β1. Our findings showed that targeting cancer-associated fibroblast-related decorin is not only a promising strategy for inhibiting HCC vascular invasion and metastasis but also provides insight into the clinical treatment of patients with PVTT.
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Affiliation(s)
- Xiaobo Zheng
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Peng Wang
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Li Li
- Institute of Clinical Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Jing Yu
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Chune Yu
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Liangliang Xu
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Lian Li
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Fuzhen Dai
- Department of General Surgery, The First People’s Hospital of Longquanyi District, Chengdu, China
| | - Lei Feng
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zou
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
- General Surgery Center of PLA, General Hospital of Western Theater Command, Chengdu, China
| | - Xiaobo Chen
- Department of Hepatopancreatobiliary Surgery, Meishan City People’s Hospital, Meishan Hospital of West China Hospital, Sichuan University, Meishan, China
| | - Ming Zhang
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of General Surgery, Mianzhu Hospital of West China hospital, Sichuan University, Mianzhu, China
| | - Mingqing Xu
- Department of Liver Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Hepatopancreatobiliary Surgery, Meishan City People’s Hospital, Meishan Hospital of West China Hospital, Sichuan University, Meishan, China
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23
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Zhang W, Liu Z, Chen J, Dong S, Cen B, Zheng S, Xu X. A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria. Transl Oncol 2021; 14:101200. [PMID: 34399173 PMCID: PMC8367829 DOI: 10.1016/j.tranon.2021.101200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/29/2021] [Accepted: 08/07/2021] [Indexed: 12/12/2022] Open
Abstract
The predictive model used preoperatively accessible clinical parameters and radiographic features developed and validated by us to predict micro vascular invasion (MVI), based on a large sample, two Liver Transplantation (LT) centers observed 5 years among Hepatocellular Carcinoma (HCC) patients who underwent LT. This is the first study to report preoperative clinical variables and radiographic features for preoperative prediction of MVI among HCC patients undergoing LT. Prediction of the presence of MVI can help surgical decision-making and improve surgical management for HCC to further distinguish clinical outcomes. Purpose The prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients. Methods In total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets. Results Univariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p < 0.001), tumor size ((p < 0.001), peritumoral star node (p = 0.003), and tumor margin (p = 0.016). The predictive nomogram using these predictors achieved an area under curve (AUC) of 0.85 and 0.80 in the training and test sets. Furthermore, MVI could discriminate different clinical outcomes within the Milan criteria (MC) and beyond the MC. Conclusions The nomogram based on preoperatively clinical variables demonstrated good performance for predicting MVI. MVI may serve as a supplement to the MC.
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Affiliation(s)
- Wenhui Zhang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China; Zhejiang University Cancer center, Hangzhou, 310058, China; Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Zhikun Liu
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Junli Chen
- National Center for healthcare quality management in liver transplant, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Siyi Dong
- National Center for healthcare quality management in liver transplant, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Beini Cen
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China; Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou,310003, China
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; Department of Hepatobiliary and Pancreatic Surgery, Shulan (Hangzhou) Hospital, Hangzhou, 310000, China.
| | - Xiao Xu
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China; Zhejiang University Cancer center, Hangzhou, 310058, China; Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
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Yoh T, Seo S, Ogiso S, Morino K, Fukumitsu K, Ishii T, Nakamoto Y, Taura K. Quantitative assessment of microvascular invasion in hepatocellular carcinoma using preoperative serological and imaging markers. HPB (Oxford) 2021; 23:1039-1045. [PMID: 33262049 DOI: 10.1016/j.hpb.2020.10.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND The aim of this study was to establish a quantitative equation to predict microvascular invasion (MVI) for patients with resectable hepatocellular carcinoma (HCC). METHODS This retrospective study included 219 patients with resected HCC from 2004 to 2015. All had available three pre-operative serological markers (alfa-feto protein (AFP), fucosylated AFP (AFP-L3), and des-gamma-carboxy prothrombin (DCP)), and one imaging marker (tumor to liver ratio of SUVmax (TLR) by 18F-FDG-PET). A multiple linear regression model for predicting MVI was developed (2004-2009, n = 111) and then validated (2010-2015, n = 108). Further, impact on the obtained model on survival outcomes was assessed. RESULTS Using the derivation cohort, following equation was developed; MVI probability (%) = 14.2 × log10DCP + 9.9 × TLR - 22.0. This model resulted in an area under receiver operating characteristic curve (ROC) of 0.806 and 0.751, in the derivation and validation cohort, respectively. Furthermore, MVI probability ≥40% determined by ROC analysis was associated with worse overall survival and recurrence-free survival in the derivation and the validation cohort (all p < 0.05). CONCLUSION A quantitative model, using DCP and TLR, was able to preoperatively predict with good performance MVI and long-term outcomes in patients with HCC after liver resection.
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Affiliation(s)
- Tomoaki Yoh
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Satoru Seo
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Satoshi Ogiso
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koshiro Morino
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ken Fukumitsu
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takamichi Ishii
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University, Kyoto, Japan
| | - Kojiro Taura
- Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Sabaté-Llobera A, Mestres-Martí J, Reynés-Llompart G, Lladó L, Mils K, Serrano T, Cortés-Romera M, Bertran E, Fabregat I, Ramos E. 2-[ 18F]FDG PET/CT as a Predictor of Microvascular Invasion and High Histological Grade in Patients with Hepatocellular Carcinoma. Cancers (Basel) 2021; 13:2554. [PMID: 34070953 PMCID: PMC8196959 DOI: 10.3390/cancers13112554] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 05/19/2021] [Indexed: 12/14/2022] Open
Abstract
Hepatocellular carcinoma (HCC) generally presents a low avidity for 2-deoxy-2-[18F]fluoro-d-glucose (FDG) in PET/CT although an increased FDG uptake seems to relate to more aggressive biological factors. To define the prognostic value of PET/CT with FDG in patients with an HCC scheduled for a tumor resection, forty-one patients were prospectively studied. The histological factors of a poor prognosis were determined and FDG uptake in the HCC lesions was analyzed semi-quantitatively (lean body mass-corrected standardized uptake value (SUL) and tumor-to-liver ratio (TLR) at different time points). The PET metabolic parameters were related to the histological characteristics of the resected tumors and to the evolution of patients. Microvascular invasion (MVI) and a poor grade of differentiation were significantly related to a worse prognosis. The SULpeak of the lesion 60 min post-FDG injection was the best parameter to predict MVI while the SULpeak of the TLR at 60 min was better for a poor differentiation. Moreover, the latter parameter was also the best preoperative variable available to predict any of these two histological factors. Patients with an increased TLRpeak60 presented a significantly higher incidence of poor prognostic factors than the rest (75% vs. 28.6%, p = 0.005) and a significantly higher incidence of recurrence at 12 months (38% vs. 0%, p = 0.014). Therefore, a semi-quantitative analysis of certain metabolic parameters on PET/CT can help identify, preoperatively, patients with histological factors of a poor prognosis, allowing an adjustment of the therapeutic strategy for those patients with a higher risk of an early recurrence.
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Affiliation(s)
- Aida Sabaté-Llobera
- PET Unit, Department of Nuclear Medicine-IDI, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain; (J.M.-M.); (G.R.-L.); (M.C.-R.)
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
| | - Judit Mestres-Martí
- PET Unit, Department of Nuclear Medicine-IDI, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain; (J.M.-M.); (G.R.-L.); (M.C.-R.)
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
| | - Gabriel Reynés-Llompart
- PET Unit, Department of Nuclear Medicine-IDI, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain; (J.M.-M.); (G.R.-L.); (M.C.-R.)
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
- Department of Medical Physics, Catalan Institute of Oncology, Duran i Reynals Hospital, L’Hospitalet de Llobregat, 08908 Barcelona, Spain
- Faculty of Medicine and Health Sciences, University of Barcelona, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
| | - Laura Lladó
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
- Faculty of Medicine and Health Sciences, University of Barcelona, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
- Hepato-Biliary, Pancreatic and Liver Transplantation Unit, Department of Surgery, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
| | - Kristel Mils
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
- Hepato-Biliary, Pancreatic and Liver Transplantation Unit, Department of Surgery, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
| | - Teresa Serrano
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
- Department of Pathology, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
- Oncology Program, Centro de Investigación Biomédica en Red, Enfermedades Hepáticas y Digestivas (CIBEREHD), National Biomedical Research Institute on Liver and Gastrointestinal Diseases, Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Montserrat Cortés-Romera
- PET Unit, Department of Nuclear Medicine-IDI, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain; (J.M.-M.); (G.R.-L.); (M.C.-R.)
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
- Faculty of Medicine and Health Sciences, University of Barcelona, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
| | - Esther Bertran
- Oncology Program, Centro de Investigación Biomédica en Red, Enfermedades Hepáticas y Digestivas (CIBEREHD), National Biomedical Research Institute on Liver and Gastrointestinal Diseases, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- TGF-ß and Cancer Group, Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
| | - Isabel Fabregat
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
- Oncology Program, Centro de Investigación Biomédica en Red, Enfermedades Hepáticas y Digestivas (CIBEREHD), National Biomedical Research Institute on Liver and Gastrointestinal Diseases, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- TGF-ß and Cancer Group, Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
| | - Emilio Ramos
- Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, 08908 Barcelona, Spain; (L.L.); (K.M.); (T.S.); (I.F.); (E.R.)
- Faculty of Medicine and Health Sciences, University of Barcelona, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
- Hepato-Biliary, Pancreatic and Liver Transplantation Unit, Department of Surgery, University Hospital of Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain
- Oncology Program, Centro de Investigación Biomédica en Red, Enfermedades Hepáticas y Digestivas (CIBEREHD), National Biomedical Research Institute on Liver and Gastrointestinal Diseases, Instituto de Salud Carlos III, 28029 Madrid, Spain;
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Zhang W, Yang R, Liang F, Liu G, Chen A, Wu H, Lai S, Ding W, Wei X, Zhen X, Jiang X. Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography. Front Oncol 2021; 11:660629. [PMID: 33796471 PMCID: PMC8008108 DOI: 10.3389/fonc.2021.660629] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 02/25/2021] [Indexed: 12/24/2022] Open
Abstract
Objective To investigate microvascular invasion (MVI) of HCC through a noninvasive multi-disciplinary team (MDT)-like radiomics fusion model on dynamic contrast enhanced (DCE) computed tomography (CT). Methods This retrospective study included 111 patients with pathologically proven hepatocellular carcinoma, which comprised 57 MVI-positive and 54 MVI-negative patients. Target volume of interest (VOI) was delineated on four DCE CT phases. The volume of tumor core (Vtc) and seven peripheral tumor regions (Vpt, with varying distances of 2, 4, 6, 8, 10, 12, and 14 mm to tumor margin) were obtained. Radiomics features extracted from different combinations of phase(s) and VOI(s) were cross-validated by 150 classification models. The best phase and VOI (or combinations) were determined. The top predictive models were ranked and screened by cross-validation on the training/validation set. The model fusion, a procedure analogous to multidisciplinary consultation, was performed on the top-3 models to generate a final model, which was validated on an independent testing set. Results Image features extracted from Vtc+Vpt(12mm) in the portal venous phase (PVP) showed dominant predictive performances. The top ranked features from Vtc+Vpt(12mm) in PVP included one gray level size zone matrix (GLSZM)-based feature and four first-order based features. Model fusion outperformed a single model in MVI prediction. The weighted fusion method achieved the best predictive performance with an AUC of 0.81, accuracy of 78.3%, sensitivity of 81.8%, and specificity of 75% on the independent testing set. Conclusion Image features extracted from the PVP with Vtc+Vpt(12mm) are the most reliable features indicative of MVI. The MDT-like radiomics fusion model is a promising tool to generate accurate and reproducible results in MVI status prediction in HCC.
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Affiliation(s)
- Wanli Zhang
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Ruimeng Yang
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Fangrong Liang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Guoshun Liu
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Amei Chen
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Hongzhen Wu
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Shengsheng Lai
- School of Medical Equipment, Guangdong Food and Drug Vocational College, Guangzhou, China
| | - Wenshuang Ding
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xin Zhen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xinqing Jiang
- Department of Radiology, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, China.,Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Zhang Y, Shu Z, Ye Q, Chen J, Zhong J, Jiang H, Wu C, Yu T, Pang P, Ma T, Lin C. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics. Front Oncol 2021; 11:633596. [PMID: 33747956 PMCID: PMC7968223 DOI: 10.3389/fonc.2021.633596] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 01/29/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives To systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model. Methods A total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models. Results Among nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model. Conclusions The combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.
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Affiliation(s)
- Yang Zhang
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Qin Ye
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Junfa Chen
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Jianguo Zhong
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Hongyang Jiang
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Cuiyun Wu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Taihen Yu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Tianshi Ma
- Department of Pathology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Chunmiao Lin
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
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